The Johns Hopkins ACG® System, Technical Reference Guide

U

The Johns Hopkins

ACG

®

System

Technical Reference Guide

Version 10.0

December 2011

Important Warranty Limitation and Copyright Notices

Copyright 2011, The Johns Hopkins University. All rights reserved.

This document is produced by the Health Services Research & Development Center at

The Johns Hopkins University, Bloomberg School of Public Health.

The terms The Johns Hopkins ACG ® System, ACG ® System, ACG ® , ADG ® , Adjusted

Clinical Groups ® , Ambulatory Care Groups TM , Aggregated Diagnostic Groups TM ,

Ambulatory Diagnostic Groups TM , Johns Hopkins Expanded Diagnosis Clusters TM ,

EDCs TM , ACG Predictive Model



, Rx-Defined Morbidity Groups



, Rx-MGs



, ACG

Rx Gaps TM, , ACG Coordination Markers™, ACG-PM



, Dx-PM



, Rx-PM



and DxRx-

PM



are trademarks of The Johns Hopkins University. All materials in this document are copyrighted by The Johns Hopkins University. It is an infringement of copyright law to develop any derivative product based on the grouping algorithm or other information presented in this document.

This document is provided as an information resource on measuring population morbidity for those with expertise in risk-adjustment models. The documentation should be used for informational purposes only. Information contained herein does not constitute recommendation for or advice about medical treatment or business practices.

No permission is granted to redistribute this documentation. No permission is granted to modify or otherwise create derivative works of this documentation.

Copies may be made only by the individual who requested the documentation initially from Johns Hopkins or their agents and only for that person's use and those of his/her coworkers at the same place of employment. All such copies must include the copyright notice above, this grant of permission and the disclaimer below must appear in all copies made; and so long as the name of The Johns Hopkins University is not used in any advertising or publicity pertaining to the use or distribution of this software without specific, written prior authorization.

Disclaimer: This documentation is provided AS IS, without representation as to its fitness for any purpose, and without warranty of any kind, either express or implied, including without limitation the implied warranties of merchantability and fitness for a particular purpose. The Johns Hopkins University and the Johns Hopkins Health System shall not be liable for any damages, including special, indirect, incidental, or consequential damages, with respect to any claim arising out of or in connection with the use of the documentation, even if it has been or is hereafter advised of the possibility of such damages.

Documentation Production Staff

Senior Editor: Jonathan P. Weiner, Dr. P.H.

Managing Editor: Chad Abrams, M.A.

Production assistance provided by: David Bodycombe Sc.D., Klaus Lemke, Ph.D., and

Patricio Muniz, M.D., MPH, MBA

Additional production assistance and original content provided by Rosina DeGiulio,

Lisa Kabasakalian, Meg McGinn, and Amy Salls of DST Health Solutions, LLC. The

ACG Team gratefully acknowledges the support provided by our corporate partner in helping to move this publication forward.

Dedication

This Guide is dedicated to the memory of Barbara Starfield, M.D., MPH, whose ideas permeate the ACG System and who continues to inspire the attainment of the best possible primary health care throughout the world.

If users have questions regarding the software and its application, they are advised to contact the organization from which they obtained the ACG software. Questions about grants of rights or comments, criticisms, or corrections related to this document should be directed to the Johns Hopkins ACG team (see below). Such communication is encouraged.

ACG Project Coordinator

624 N. Broadway - Room 607

Baltimore, MD 21205-1901 USA

Telephone (410) 955-5660

Fax: (410) 955-0470

E-mail: askacg@jhsph.edu

Website: http://acg.jhsph.edu

Third Party Library Acknowledgements

This product includes software developed by the following companies:

Health Plus Technologies (http://www.healthplustech.com)

Karsten Lentzsch (http://www.jgoodies.com)

SafeNet (http://www.safenet-inc.com.com

)

This product includes software developed by The Apache Software Foundation

(http://www.apache.org)

This product includes the Java Runtime Environment developed by Sun Microsystems

(http://java.sun.com)

This product includes the following open source:

JDOM library (http://www.jdom.org) iText library (http://www.lowagie.com/iText)

JasperReports library (http://www.jasperforge.org)

Table of Contents

1 Introduction ...................................................................................................... 1-i

Introduction to The Johns Hopkins ACG ® System ......................................... 1-1

Technical Reference Guide Objective ............................................................ 1-1

Technical Reference Guide Navigation .......................................................... 1-1

Technical Reference Guide Content ............................................................... 1-2

Installation and Usage Guide Content............................................................. 1-3

Applications Guide Content ............................................................................ 1-4

Customer Commitment and Contact Information ........................................... 1-5

2 Diagnosis and Code Sets .................................................................................. 2-i

Introduction ..................................................................................................... 2-1

Code Set Support ............................................................................................ 2-1

Coding Issues Using the International Classification of Diseases (ICD) ....... 2-2

Using National Drug Codes (NDC) ................................................................ 2-4

Using Anatomical Therapeutic Chemical (ATC) Codes ................................ 2-6

3 Adjusted Clinical Groups (ACGs®) ............................................................... 3-i

Overview ......................................................................................................... 3-1

Overview of the ACG Assignment Process .................................................... 3-3

Major ADGs .................................................................................................... 3-7

Clinical Aspects of ACGs ............................................................................. 3-29

Clinically Oriented Examples of ACGs ........................................................ 3-33 i

Technical Reference Guide The Johns Hopkins ACG System, Version 10.0

ii

4 Expanded Diagnosis Clusters (EDCs

TM

)........................................................ 4-i

Overview ......................................................................................................... 4-1

Development of EDCs .................................................................................... 4-1

Understanding How EDCs Work .................................................................... 4-3

5 Medication Defined Morbidity ....................................................................... 5-i

Objectives ........................................................................................................ 5-1

National Drug Code System............................................................................ 5-1

Anatomical Therapeutic Chemical (ATC) System ......................................... 5-2

Healthcare Common Procedure Coding System (HCPCS) ............................ 5-3

Rx-Defined Morbidity Groups ........................................................................ 5-3

NDC-to-Rx-MG Assignment Methodology ................................................... 5-9

ATC-to-Rx-MG Assignment Methodology .................................................... 5-9

Active Ingredient Count Variable ................................................................. 5-23

Conclusion .................................................................................................... 5-23

6 Special Population Markers ............................................................................ 6-i

Introduction ..................................................................................................... 6-1

Hospital Dominant Morbidity Types .............................................................. 6-1

Frailty Conditions ........................................................................................... 6-3

Pregnancy without Delivery ............................................................................ 6-5

Chronic Condition Count ................................................................................ 6-5

Condition Markers ........................................................................................ 6-10

7 Predicting Future Resource Use ..................................................................... 7-i

Objectives ........................................................................................................ 7-1

Conceptual Basis of Predictive Modeling ....................................................... 7-1

Development of ACG Predictive Models ..................................................... 7-13

The Johns Hopkins ACG System, Version 10.0 Technical Reference Guide

Resource Bands ............................................................................................. 7-16

High, Moderate, and Low Impact Conditions ............................................... 7-17

High Pharmacy Utilization Model ................................................................ 7-24

8 Predictive Modeling Statistical Performance ................................................ 8-i

Overview of ACG® Predictive Models Statistical Performance .................... 8-1

Validation Data ............................................................................................... 8-1

Adjusted R-Square Values .............................................................................. 8-1

Sensitivity and Positive Predictive Value ....................................................... 8-8

ROC Curve .................................................................................................... 8-10

Clinical Information Improves on Prior Cost................................................ 8-11

9 Predicting Hospitalization ............................................................................... 9-i

Introduction ..................................................................................................... 9-1

Framework for Predicting Hospitalization Using ACG .................................. 9-1

Empiric Validation of the Likelihood of Hospitalization Model .................... 9-6

10 Coordination ................................................................................................. 10-i

Introduction ................................................................................................... 10-1

Assessing Care Coordination ........................................................................ 10-1

Majority Source of Care (MSOC) Marker .................................................... 10-6

Management Visit Count .............................................................................. 10-8

Unique Provider Count (UPC) Marker ......................................................... 10-8

Specialty Count (SC) Marker ...................................................................... 10-10

Generalist Seen (GS) Marker ...................................................................... 10-12

Risk of Coordination ................................................................................... 10-13

Conclusion .................................................................................................. 10-15 iii


iv

11 Pharmacy Adherence ................................................................................... 11-i

Objectives ...................................................................................................... 11-1

Significance of Pharmacy Adherence for Effective Care ............................. 11-1

Development of Possession/Adherence Markers .......................................... 11-2

Pharmacy Adherence Defined....................................................................... 11-4

Validation and Testing ................................................................................ 11-14

Appendix A: ACG® Publication List.............................................................. A-1

Appendix B: Variables Necessary to Locally Calibrate the ACG®

Predictive Models ............................................................................................... B-i

Index ................................................................................................................... IN-i


Introduction 1-i

1 Introduction

Introduction to The Johns Hopkins ACG

®

System .................................... 1-1

Technical Reference Guide Objective ......................................................... 1-1

Technical Reference Guide Navigation ....................................................... 1-1

Technical Reference Guide Content ............................................................ 1-2

Installation and Usage Guide Content ........................................................ 1-3

Applications Guide Content ......................................................................... 1-4

Customer Commitment and Contact Information .................................... 1-5


1-ii

This page was left blank intentionally.

Introduction


Introduction

Introduction to The Johns Hopkins ACG

®

System

1-1

The ACG (Adjusted Clinical Groups) System was developed by faculty at the Johns

Hopkins Bloomberg School of Public Health to help make health care delivery more efficient and more equitable. Because the ACG System can be used for numerous management, finance, and analytical applications related to health and health care, it has become the most widely used, population-based, case-mix / risk adjustment methodology. Precisely because of the diversity of ACG applications, one size does not fit all in terms of methodology. Like health management and analysis itself, using casemix or risk adjustment methods involves art as well as science, and these applications are particularly context and objective driven. We hope this documentation will provide you with much of the guidance you will need in order to apply the ACG System to most effectively meet the risk adjustment and case-mix needs of your organization.

Technical Reference Guide Objective

The

Technical Reference Guide

was designed to augment the

Installation and Usage

Guide

that accompanies the ACG Software. The objective is to provide the organization with the clinical basis and empirical performance of the ACG System.

Technical Reference Guide Navigation

Locating information in the


is facilitated by the following search methods:

•

•

•

Master Table of Contents

. The master table of contents contains the chapter names and principal headings for each chapter.

Chapter Table of Contents

. Each chapter has a table of contents, which lists the principal headings and subheadings and figures and tables.

Index

. Each chapter is indexed and organized alphabetically.


1-2 Introduction

Technical Reference Guide Content

For your convenience, a list of the

Technical Reference Guide chapters is provided.

• Chapter 1: Introduction

. Provides a general overview of the physical organization of the manual as well as content.

•

•

Chapter 2

:

Diagnosis and Code Sets

. This chapter discusses the applicability of diagnosis data to the field of risk assessment and the challenges of managing multiple standards for diagnosis coding.

Chapter 3: Adjusted Clinical Groups

®

(ACGs)

. This chapter provides a brief overview of the history of the clinical origin of the ACG System and describes the details of the ACG assignment algorithm.

• Chapter 4: Expanded Diagnosis Clusters

TM

(EDCs)

. This chapter explains the development and evolution of the EDC methodology.

• Chapter 5: Medication Defined Morbidity

.

This chapter discusses the applicability of pharmacy claims in risk assessment and defines how drug codes are assigned to morbidity groups.

• Chapter 6: Special Population Markers

. This chapter discusses the definitions and clinical criteria for the HOSDOM, Frailty, Chronic Condition Count, and Chronic

Condition Markers.

•

•

•

•

•

Chapter 7: Predicting Resource Use

.

This chapter discusses the methods and variations of ACG Predictive Models developed to predict resource use.

Chapter 8

:

Predictive Modeling Statistical Performance

.

This chapter demonstrates the ACG Predictive Models’ statistical performance while describing the various ways in which they can be applied in health care applications.

Chapter 9: Predicting Hospitalization

. This chapter discusses the methods and variations of ACG Predictive Models developed to predict hospitalization risk.

Chapter 10: Coordination

. This chapter discusses the methods for evaluating coordination of care.

Chapter 11: Pharmacy Adherence

. This chapter discusses the methods and metrics associated with medication possession and gaps in pharmacy adherence.

• Appendix A: ACG Publication List

• Appendix B: Variables Necessary to Locally Calibrate the ACG Predictive

Models


Introduction

• Index

Installation and Usage Guide Content

1-3


Installation and Usage Guide

chapters is provided.

•

•

Chapter 1: Getting Started.

Provides a general overview of the physical organization of the manual as well as content.

Chapter 2: Overview of the ACG Toolkit.

Intended for all clients, this chapter provides a brief overview of the ACG toolkit to provide a baseline introduction to the nomenclature of the ACG System components.

•

•

Chapter 3: Installing the ACG Software.

Intended for the programmer / analyst, this chapter discusses the technical aspects of installing the software.

Chapter 4: Basic Data Requirements.

Intended for the programmer / analyst, this chapter discusses at a high level the minimum data input requirements and other necessary data requirements for performing ACG-based risk adjusted analyses.

Included are discussions of augmenting or supplementing diagnosis information with optional user-supplied flags as well as consideration of the use of pharmacy information.

• Chapter 5: Using the ACG Sample Data.

Intended for the programmer / analyst, this chapter walks through the example of processing the sample data provided with the installation. This sample data is provided to allow clients to understand the outputs of the ACG System and demonstrate system functionality prior to the availability of the client’s own input data.

•

•

Chapter 6: Using Client Data.

Intended for the programmer / analyst, the purpose of this chapter is to describe the process of importing user-supplied data files into the system.

Chapter 7: Operating the ACG Software.

Intended for the programmer / analyst, this chapter discusses the technical aspects of using the software, and importing and exporting data and reports.

•

•

Chapter 8: Validating Results.

Intended for the programmer / analyst, the purpose of this chapter is to provide examples of the ACG System functionality that was designed to assist in the validation of user-supplied data.

Chapter 9: Troubleshooting.

Intended for the programmer / analyst, the purpose of this chapter is to leverage prior client experience with the software to describe the symptom and solution to common client issues.

• Appendix A: Output Data Dictionary


1-4

• Appendix B: Report Detail

• Appendix C: Batch Mode Processing

• Appendix D: Java API

• Index

Applications Guide Content

Introduction


Applications Guide

chapters is provided.

• Chapter 1: Introduction

. Provides a general overview of the physical organization of the manual as well as content.

•

•

Chapter 2

:

Health Status Monitoring

. This chapter demonstrates the application of the ACG System markers and analyses to measures disease prevalence and support health status monitoring.

Chapter 3: Performance Assessment

. This chapter outlines the basic steps to taking a population-based approach to profiling.

• Chapter 4

:

Clinical Screening by Care and Disease Managers

. This chapter demonstrates the application of the ACG System markers to high risk case selection, risk stratification, and amenability.

• Chapter 5

:

Managing Financial Risk for Pharmacy Benefits

. This chapter describes use of the ACG System markers to the application of managing pharmacy risk.

• Chapter 6

:

Capitation and Rate Setting

. This chapter describes various methods of applying the ACG System to capitation and rate setting.

•

•

Chapter 7

:

Final Considerations

Index


Introduction 1-5

Customer Commitment and Contact Information

As part of our ongoing commitment to furthering the international state-of-the-art of riskadjustment methodology and supporting clients of the ACG System worldwide, we will continue to perform evaluation, research, and development. We will look forward to sharing the results of this work with our client base via white papers, our web site, peerreviewed articles, and in-person presentations. After you have carefully reviewed the documentation supplied with this software release, we would welcome your inquiries on any topic of relevance to your use of the ACG System within your organization.

Technical support is available during standard business hours by contacting your designated account representative directly. If you do not know how to contact your account representative, please call 866-287-9243 or e-mail acg@dsthealthsolutions.com

.

We thank you for using the ACG System and for helping us to work toward meeting the

Johns Hopkins University’s ultimate goal of improving the quality, efficiency, and equity of health care across the United States and around the globe.


1-6


Introduction


Diagnosis and Code Sets 2-i

2 Diagnosis and Code Sets

Introduction ................................................................................................... 2-1

Code Set Support .......................................................................................... 2-1

Table 1: Supported Coding Systems ......................................................... 2-1

Coding Issues Using the International Classification of Diseases (ICD) .. 2-2

Diagnosis Codes with Three and Four Digits ............................................ 2-2

Rule-Out, Suspected, and Provisional Diagnoses ...................................... 2-3

Special Note for ICD-10 (Non-U.S.) Users ............................................... 2-4

Using ICD-9 and ICD-10 Simultaneously ................................................. 2-4

Using National Drug Codes (NDC) .............................................................. 2-4

Table 2: Classification of Metformin – National Drug Codes .................. 2-5

Using Anatomical Therapeutic Chemical (ATC) Codes............................ 2-6

Table 3: Classification of Metformin – Anatomical Therapeutic

Chemical (ATC) Codes .............................................................................. 2-6


2-ii

This page was left blank intentionally

.

Diagnosis and Code Sets

Technical Reference Guide he Johns Hopkins ACG System, Version 10.0

Diagnosis and Code Sets 2-1

Introduction

The Johns Hopkins ACG ® System characterizes population health status based upon readily available medical and pharmacy claims, patient registries, or other administrative sources. These data sources often feed financial or payment systems and were not originally intended as a source of clinical information. When proper care is taken in preparing and using the data in the Johns Hopkins ACG System, these administrative sources provide a very robust input for clinical classification purposes. This chapter discusses the challenges related to extending the use of administrative coding systems and subsequent considerations in preparing data for use by the ACG System.

Code Set Support

The ACG System is the most widely used Risk Adjustment and Predictive Modeling tool for population health management in the world. To make this possible, the ACG System has been adapted to many different coding systems.

Table 1: Supported Coding

Systems

depicts some of our major supported coding systems. Within these general families, the ACG System also supports numerous national variants.

Table 1: Supported Coding Systems

Diagnostic Pharmacy Procedure Encounter Specialty Classification

ICD-9-CM

ICD-10

NDC

ATC

CPT

HCPCS

ICPC Health Care Provider Taxonomy

CMS Specialty

ICD-10-CM READ

The following discussion emphasizes the appropriate use of some of the major coding systems with respect to the ACG System. More information on preparing data for entry in these various formats is provided in the


.


2-2 Diagnosis and Code Sets

Coding Issues Using the International Classification of

Diseases (ICD)

Diagnosis codes are the primary data requirement of the Johns Hopkins ACG System.

The user must ensure, to the extent possible, the diagnosis codes recorded on the claims encounter records, and the resulting machine-readable data records are comprehensive and consistent with the source medical records. For the purpose of assessing the quality of diagnosis code data, a rudimentary understanding of the structure and limitations of the

International Classification of Diseases (ICD-9, ICD-9-CM, ICD-10, and / or ICD-10-

CM) is needed. The two current editions of the ICD (ICD-9 and ICD-10) are developed and maintained by the World Health Organization (WHO). In the United States, a clinical modification of ICD-9, known as ICD-9-CM, has been in use since the early

1980s and is expected to be replaced by ICD10-CM in 2013. ICD-10 was adopted by the

WHO in 1993 and it and its various adaptations are in use by many countries.

The next sections describe some ICD coding issues for which ACG Software clients should be aware.

Diagnosis Codes with Three and Four Digits

An ICD-10-CM code of as little as three characters is valid as long as there are no more detailed subcategories or codes beneath it. Where four or more additional characters are required, an “x” can be used as a placeholder character for intervening characters that are not used. There are very few valid ICD-10-CM codes of three character length; only 293 out of roughly 78,000 codes. The vast majority of codes use all seven allotted characters

(53,442 or 68 percent of all codes). Three-digit codes will often not be accepted by payers on insurance claims.

Three and four digit diagnosis codes were used with some frequency in the ICD-9-CM coding system. The ACG System accepts only a limited number of these diagnosis codes and coding at that level affects the ability to produce important clinical markers such as those for medical frailty and hospital dominant conditions. The advent of ICD-10-CM and it richer level of detail is expected to improve the ability of the ACG System to accurately represent the true morbidity burden of the target population and to provide an array of important contextual clinical markers.


Diagnosis and Code Sets 2-3

Rule-Out, Suspected, and Provisional Diagnoses

One of the most frequent criticisms of the ICD system is the lack of codes that allow a provider to stipulate that a particular diagnosis be designated as rule-out (

R / O), suspected, or provisional. Providers may record diagnoses as R / O on medical records even though they do not strongly suspect them because certain tests, procedures, or trials of therapy are used to make a more definitive diagnosis. However, because ICD has no rule-out code or modifier, diagnoses such as coronary artery disease, subarachnoid hemorrhage, and hiatal hernia, just to name a few, may remain in the patient’s claim database because they were recorded on one or more of the claim forms in the course of the patient’s work-up.

With the exception of excluding diagnoses from lab and x-ray claims (which frequently are rule-out or provisional in nature), the Johns Hopkins ACG Development Team does not believe that R / O or suspected diagnoses have a dramatic effect on ACG assignment.

One reason is that in a retrospective application, R / O diagnoses still affect the consumption of healthcare resources. For example, a patient who has R / O coronary artery disease or R / O hiatal hernia still consumes the resources associated with the differential diagnosis of these disorders. Although the extent of their impact is not well understood in applications designed to predict resource consumption in the next time period, the presence of rule-out or suspected diagnosis codes may have an effect if they appear in large numbers or if certain providers or groups use these more than other providers or groups. This impact is especially relevant if the ruled-out diagnoses resolve to ADGs ® that the patient would not be otherwise assigned to, based upon the array of his/her other confirmed diagnoses. For patients with multiple co-morbidities, the probability of this is lower than for patients who are relatively healthy. While it is certainly possible for rule-out diagnoses to make healthy individuals appear sicker than they really are, this distortion should occur for only a small subgroup of patients. To some extent, the user can assess this by linking a count of ADGs assigned to a broad measure of resource consumption, such as total charges, and a narrow one, such as office visits, and then comparing the correlation between ADG counts and the two resource consumption measures. Persons with many ADGs, low total charges, and many visits, may suggest that rule-out diagnoses play a role in the assignment of the ADGs. When a particular healthcare organization or physician consistently appears to have a high morbidity mix but relatively low resource use, it may be useful to ascertain, using medical records, if the use of R / O diagnoses is higher in these instances. For example, this situation could occur if certain experienced diagnosticians are referred a disproportionate share of difficult patients with unclear symptoms.



Special Note for ICD-10 (Non-U.S.) Users

The WHO version of the ICD-10 was first incorporated into the ACG Grouper in August of 2003. Users of ICD-10 are encouraged to pay special attention to the discussion on augmenting pregnancy, delivery, and low birth weight information as the usefulness of

ICD-10 data for these purposes is not well established in the United States.

The ACG System uses the standard WHO version of four digit ICD-10 codes. This excludes country-specific adaptations, specialty-specific adaptations, and supplementary divisions that utilize a fifth digit.



Tip :

The ACG System supports the WHO version of ICD-10. If there is a need for a country-specific adaptation, please contact the ACG Software Distributor to discuss the potential for local customization.

Using ICD-9 and ICD-10 Simultaneously

It is possible to simultaneously use both ICD-9 and ICD-10 data collected on the same population. These diagnosis codes can be processed as one data stream; however, ICD-9 data must be stored in separate fields (or columns on the input data) from the ICD-10 data

(see the “Basic Data Requirements” chapter in the


for more detail). The ACG System will normalize the diagnosis codes from each coding system into ACG System Markers.

Using National Drug Codes (NDC)

The National Drug Code (NDC) is a drug product classification system. First compiled and organized as part of a Medicare (United States federal program that pays for certain health care expenses for people aged 65 or older) outpatient drug reimbursement plan, it has grown and spread to numerous sectors within the health care industry among which include managed care organizations, pharmaceutical manufacturers, wholesalers, hospitals, and Medicaid (United States health program for certain people and families with low incomes and resources). Its usages span from clinical patient profile screening, to inventory control and drug claims processes. Recorded within a database headed by the U.S. Food and Drug Administration (FDA), it is used specifically by the government for product tracking, evaluations, research, and drug approval within the United States.


Diagnosis and Code Sets

Table 2: Classification of Metformin

provides examples of NDC codes related to the ingredient Metformin. There are 54 manufacturers of Metformin with nearly 300 individual NDC codes for single-ingredient doses of Metformin and an additional 350

NDC codes for combination drugs containing Metformin.

Table 2: Classification of Metformin – National Drug Codes

NDC Code

00087-6060-05

00087-6060-10

00093-1048-05

00172-4330-80

00185-0213-05

49884-*741-05

55289-*919-30

Manufacturer

Bristol-Myers Squibb ™

Bristol-Myers Squibb ™

TEVA Pharmaceuticals ®

IVAX Pharmaceuticals ®

EON LABS ®

PAR Pharmaceuticals ®

PD-Rx Pharmaceuticals ®

Drug Strength Package Size

500 mg

500 mg

500 mg

850 mg

500 mg

1000 mg

1000 mg

100

500

500

1000

500

500

270

2-5



Using Anatomical Therapeutic Chemical (ATC) Codes

The WHO’s Anatomical Therapeutic Chemical TM (ATC) codes may also be processed with the ACG System. In the ATC classification system, the drugs are divided into different groups according to the organ or system on which they act and their chemical, pharmacological and therapeutic properties. Drugs are classified in groups at five different levels. The drugs are divided into fourteen main groups (first level), with one pharmacological / therapeutic subgroup (second level). The third and fourth levels are chemical/pharmacological / therapeutic subgroups and the fifth level is the chemical substance. The second, third, and fourth levels are often used to identify pharmacological subgroups when that is considered more appropriate than therapeutic or chemical subgroups.

Table 3: Classification of Metformin

provides the complete classification of Metformin and code structure.

Table 3: Classification of Metformin – Anatomical Therapeutic

Chemical (ATC) Codes

The complete classification of Metformin illustrates the structure of the code.

Code Description

A

A10

Alimentary tract and metabolism (first level, anatomical main group)

Drugs used in diabetes (second level, therapeutic subgroup)

A10B

A10BA

Blood glucose lowering drugs, excludes insulins (third level, pharmacological subgroup)

Biguanides (fourth level, chemical subgroup)

A10BA02 Metformin (fifth level, chemical substance)

The ATC system was created to serve as a tool for drug utilization research. Because the

ATC system has been specifically designed to capture the therapeutic use of the main active ingredient, there is much more relevant information imbedded in an ATC code for making Rx-Defined Morbidity Group  (Rx-MG  ) assignments (reference the chapter entitled, “Medication Defined Morbidity” in the


for a more detailed description of the Rx-MG assignment methodology.)



Tip

: The ACG System supports other drug coding systems, including Read coding used in the United Kingdom. Information on how to prepare ACG inputs for these different coding systems is provided in the


.


Adjusted Clinical Groups (ACGs ® )

3 Adjusted Clinical Groups (ACGs

®

)

Overview ........................................................................................................ 3-1

Development of ACGs ............................................................................... 3-1

How ACGs Work ....................................................................................... 3-2

Overview of the ACG Assignment Process ................................................. 3-3

Step 1: Mapping ICD Codes to a Parsimonious Set of Aggregated

ADGs .......................................................................................................... 3-4

Table 1: ADGs and Common ICD10 Codes Assigned to Them ............... 3-4

Major ADGs .................................................................................................. 3-7

Table 2: Major ADGs for Adult and Pediatric Populations ...................... 3-7

Step 2: Creating a Manageable Number of ADG Subgroups (CADGs) ... 3-8

Table 3: Collapsed ADG Clusters and the ADGs that they Comprise ...... 3-8

Step 3: Frequently Occurring Combinations of CADGs (MACs) .......... 3-10

Table 4: MACs and the Collapsed ADGs Assigned to Them ................. 3-10

Step 4: Forming the Terminal Groups (ACGs) ....................................... 3-11

Concurrent ACG-Weights ........................................................................ 3-11

Resource Utilization Bands (RUBs) ........................................................ 3-13

Table 5: Final ACG Categories, Reference Concurrent Weights, and RUBs ................................................................................................. 3-14

Figure 1: ACG Decision Tree ................................................................. 3-22

Figure 2: Decision Tree for MAC-12—Pregnant Women ...................... 3-24

Figure 3: Decision Tree for MAC-26—Infants ....................................... 3-26

Figure 4: Decision Tree for MAC-24—Multiple ADG Categories ........ 3-28

Clinical Aspects of ACGs ........................................................................... 3-29

Duration .................................................................................................... 3-29

Severity ..................................................................................................... 3-30

Diagnostic Certainty ................................................................................. 3-30

Etiology .................................................................................................... 3-30

Expected Need for Specialty Care ........................................................... 3-30

Table 6: Duration, Severity, Etiology, and Certainty of the ADGs ........ 3-31

Clinically Oriented Examples of ACGs..................................................... 3-33

Chronic Illnesses ...................................................................................... 3-33

Example 1: Hypertension ........................................................................ 3-34

Example 2: Diabetes Mellitus ................................................................. 3-36

3-i


3-ii Adjusted Clinical Groups (ACGs ® )

Pregnancy ................................................................................................. 3-38

Example 3: Pregnancy / Delivery with Complications ........................... 3-39

Table 7: Clinical Classification of Pregnancy / Delivery ACGs ............. 3-40

Infants ....................................................................................................... 3-40

Example 4: Infants .................................................................................. 3-41

Table 8: Clinical Classification of Infant ACGs ..................................... 3-42

The Johns Hopkins ACG ® System, Version 10.0 Technical Reference Guide


Overview

3-1

The Johns Hopkins ACG ® Case-Mix System (the ACG System) is a statistically valid, diagnosis-based, case-mix methodology that allows healthcare providers, healthcare organizations, and public-sector agencies to describe or predict a population’s past or future healthcare utilization and costs. The ACG System is also widely used by researchers and analysts to compare various patient populations’ prior health resource use, while taking into account morbidity or illness burden.

Adjusted Clinical Group actuarial cells, or ACGs, are the building blocks of the Johns

Hopkins ACG Case-Mix System (the ACG System) methodology. ACGs are a series of mutually exclusive, health status categories defined by morbidity, age, and sex. They are based on the premise that the level of resources necessary for delivering appropriate healthcare to a population is correlated with the illness burden of that population.

ACGs are used to determine the morbidity profile of patient populations to more fairly assess provider performance, to reimburse providers based on the health needs of their patients, and to allow for more equitable comparisons of utilization or outcomes across two or more patient or enrollee aggregations.

Development of ACGs

The concept of ACG actuarial cells grew out of research by Dr. Barbara Starfield and her colleagues in the late 1970s when they examined the relationship between morbidity or illness burden and healthcare services utilization among children in managed care settings.

The research team theorized that the children using the most healthcare resources were not those with a single chronic illness, but rather were those with multiple, seemingly unrelated conditions. To test their original hypothesis, illnesses found within pediatric health maintenance organization (HMO) populations were grouped into five discrete categories:

1.

Minor illnesses that are self-limited if treated appropriately, e.g., the flu or chicken pox.

2.

Illnesses that are more severe but also time-limited if treated appropriately, e.g., a broken leg or pneumonia.

3.

Medical illnesses that are generally chronic and which remain incurable even with medical therapy, e.g., diabetes or cystic fibrosis.

4.

Illnesses resulting from structural problems that are generally not curable even with adequate and appropriate intervention, e.g., cerebral palsy or scoliosis.

5.

Psychosocial conditions, e.g., behavior problems or depression.


3-2 Adjusted Clinical Groups (ACGs ® )

The Johns Hopkins team’s findings supported the hypothesis that clustering of morbidity is a better predictor of health services resource use than the presence of specific diseases. This finding forms the basis of the current ACG System and remains the fundamental concept that differentiates ACGs from other case-mix adjustment methodologies.

Since this early research more than two decades ago, the Johns Hopkins ACG Case-Mix

System has been refined and expanded based on years of extensive research and development in collaboration with over 25 private and public healthcare organizations. The peer reviewed research literature on the ACG System by the Johns Hopkins team and others is quite extensive and we encourage you to study it. (See Appendix A for a complete listing of

ACG -related publications.)

How ACGs Work

ACGs are a person-focused method of categorizing patients’ illnesses. Over time, each person develops numerous conditions. Based on the pattern of these morbidities, the ACG approach assigns each individual to a single ACG category. Thus, an ACG captures the specific clustering of morbidities experienced by a person over a given period of time, such as a year.

The ACG System assigns all International Classification of Diseases (ICD) (-9-CM,-10,-10-

CM) codes to one of 32 diagnosis clusters known as Aggregated Diagnosis Groups™, or

ADGs ® . Individual diseases or conditions are placed into a single ADG cluster based on five clinical dimensions:

• Duration of the condition

(acute, recurrent, or chronic): How long will healthcare resources be required for the management of this condition?

•

•

Severity of the condition

(e.g., minor and stable versus major and unstable): How intensely must healthcare resources be applied to manage the condition?

Diagnostic certainty

(symptoms versus documented disease): Will a diagnostic evaluation be needed or will services for treatment be the primary focus?

• Etiology of the condition

(infectious, injury, or other): What types of healthcare services will likely be used?

• Specialty care involvement

(e.g., medical, surgical, obstetric, hematology): To what degree will specialty care services be required?


Adjusted Clinical Groups (ACGs ® ) 3-3

All diseases--even those yet to be discovered--can be classified along these dimensions and categorized into one of these 32 ADG clusters 1 .

Because most management applications for population-based case-mix adjustment systems require that patients be grouped into single, mutually exclusive categories, the ACG methodology uses a branching algorithm to place people into one of 93 2 discrete categories based on their assigned ADGs, their age and their sex. The result is that individuals within a given ACG have experienced a similar pattern of morbidity and resource consumption over the course of a given year.

ACGs can be assigned to individuals using readily available diagnostic information derived from outpatient or ambulatory physician visit claims records, encounter records, inpatient hospital claims, and computerized discharge abstracts. A patient / enrollee is assigned to a single ACG based on the diagnoses assigned by all clinicians seeing them during all contacts, regardless of setting. ACGs are truly person-oriented and are not based on visits or episodes.

Typically, ACGs perform up to ten times better than age and sex adjustment, the traditional risk-adjustment mechanism used within the health insurance industry.

Overview of the ACG Assignment Process

The ACG System relies on automated claims or encounter data derived from healthcare settings to characterize the degree of overall morbidity in patients and populations. ACGs are designed to be conceptually simple, accessible, and practical to both clinicians and managers. A significant advantage of the ACG System is its open architecture. Unlike other case-mix, severity, and profiling systems’ decision rules, those used for ACGs are not housed in a “black box.” The ACG grouping logic is easily accessible.

This chapter provides a detailed description of the ACG grouping logic. There is a section in this chapter entitled, “Clinical Aspects of ACGs” that expands this discussion by providing a clinical discussion of the ACG System.

1 Please note that originally when the ACG System was termed the Ambulatory Care Group System, ADG stood for

Ambulatory Diagnostic Groups. Today, we retain the ADG acronym and call them Aggregated Diagnosis Groups.

2 There are 82 default categories and 93 if all optional branchings are turned on.



Step 1: Mapping ICD Codes to a Parsimonious Set of Aggregated

ADGs

There are thousands of International Classification of Disease (ICD) diagnosis codes that clinicians can use to describe patients’ health conditions. The first step of the ACG grouping logic is to assign each of these codes to one of 32 diagnosis groups referred to as Aggregated

Diagnosis Groups, or ADGs. The ICD-to-ADG mapping embedded in the ACG Software includes an ADG assignment for all

Services (CMS) list of ICD-9 codes

3

4

ICD codes in the Center for Medicare and Medicaid

. ICD-10 codes are sourced from the

Official ICD-10

Updates

published by the World Health Organization (WHO). Beginning with Version 10.0 of the ACG System, all ICD-10-CM codes in the CMS list will be included as well.

Each ADG is a grouping of diagnosis codes that are similar in terms of severity and likelihood of persistence of the health condition treated over a relevant period of time (such as a year of HMO enrollment). Diagnosis codes within the same ADG are similar in terms of both clinical criteria and expected need for healthcare resources. Just as individuals may have multiple diagnosis codes, they may have multiple ADGs (up to 32).

Table 1

:

ADGs and Common ICD Codes Assigned to Them

lists the 32 ADGs and exemplary diagnosis codes.

ADGs are distinguished by several clinical characteristics (time limited or not, medical / specialty / pregnancy, physical health / psycho-social), and degree of refinement of the problem (diagnosis or symptom / sign). They are not categorized by organ system or disease.

Instead, they are based on clinical dimensions that help explain or predict the need for healthcare resources over time. The need for healthcare resources is primarily determined by the likelihood of persistence of problems and their level of severity rather than organ system involvement. See the section entitled, “Clinical Aspects of ACGs,” in this manual for further discussion of these clinical criteria.

Table 1: ADGs and Common ICD10 Codes Assigned to Them

ADG

1.

Time Limited: Minor

ICD9-CM

558.9

691.0

2.

Time Limited: Minor-

Primary Infections

3.

Time Limited: Major

079.9

464.4

451.2

560.3

K52.9

L22

B09

J05.0

I80.3

K56.7

ICD-10 Diagnosis

Noninfectious

Gastroententris

Diaper or Napkin Rash

Unspecified Viral Infection

Croup

Phlebitis of Lower

Extremities

3 Because they indicate the cause of injury rather than an underlying morbidity, ICD-10 codes beginning V through

Y have generally been excluded from the ICD-9 to ADG mapping.

4 Available for download at http://www.cms.gov.



ADG ICD9-CM

4.

Time Limited: Major-

Primary Infections

5.

Allergies

573.3

711.0

477.9

708.9

6.

Asthma

7.

Likely to Recur: Discrete

8.

Likely to Recur: Discrete-

Infections

9.

Likely to Recur: Progressive

493.0

493.1

274.9

724.5

474.0

599.0

250.10

434.0

10.

21.

Chronic Medical: Stable

11.

Chronic Medical: Unstable

12.

Chronic Specialty: Stable-

Orthopedic

13.


Ear, Nose, Throat

14.


Eye

16.

Chronic Specialty: Unstable-

Orthopedic

17.


Ear, Nose, Throat

18.


Eye

20.

Dermatologic

Injuries/Adverse Effects:

Minor

22.

Injuries/Adverse Effects:

Major

250.00

401.9

282.6

277.0

721.0

718.8

389.14

385.3

367.1

372.9

724.02

732.7

386.0

383.1

365.9

379.0

078.1

448.1

847.0

959.1

854.0

972.1

23.

Psychosocial: Time Limited,

Minor

24.

Psychosocial: Recurrent or

Persistent, Stable

25.


Persistent, Unstable

305.2

309.0

300.01

307.51

295.2

291.0

ICD-10

H90.5

H71

H52.1

H11.9

M48.0

M92.8

H81.0

H70.1

H40.9

H15.0

A63.0

I78.1

S13.4

T09.0

S06

T46.0

K75.9

M00.9

J30.0

L50.9

J45.0

J45.1

M10.9

M54.9

J35.1

N39.0

E11.1

I66.9

E10.9

I10

D57.1

E84.0

M48.9

M24.9

F12.1

F32.0

F41.0

F50.3

F20.2

F10.3

Technical Reference Guide

3-5

Diagnosis

Impaction of Intestine

Hepatitis, Unspecified

Pyogenic Arthritis

Allergic Rhinitis, Cause

Unspecified

Unspecified Urticaria

Extrinsic Asthma

Intrinsic Asthma

Gout, Unspecified

Backache, Unspecified

Chronic Tonsillitus

Urinary Tract Infection

Adult Onset Type II

Diabetes w / Ketoacidosis

Cerebral Thrombosis

Adult-Onset Type 1

Diabetes

Essential Hypertension

Sickle-Cell Anemia

Cystic Fibrosis

Cervical Spondylosis

Without Myelopathy

Other Joint Derangement

Central Hearing Loss

Cholesteatoma

Myopia

Unspecified Disorder of

Conjunctiva

Spinal Stenosis of Lumbar

Region

Osteochondritis Dissecans

Meniere's Disease

Chronic Mastoiditis

Unspecified Glaucoma

Scleritis / Episcleritis

Viral Warts

Nevus, Non-Neoplastic

Neck Sprain

Injury to Trunk

Intracranial Injury

Poisoning by Cardiotonic

Glycosides and Similar

Drugs

Cannabis Abuse,

Unspecified

Brief Depressive Reaction

Panic Disorder

Bulimia

Catatonic Schizophrenia

Alcohol Withdrawal

The Johns Hopkins ACG System, Version 10.0


ADG

26.

Signs/Symptoms: Minor

27.

Signs/Symptoms: Uncertain

28.

Signs/Symptoms: Major

29.

Discretionary

30.

See and Reassure

31.

Prevention/Administrative

32.

Malignancy

33.

Pregnancy

ICD9-CM

784.0

729.5

719.06

780.7

429.3

780.2

550.9

706.2

611.1

278.1

V20.2

V72.3

174.9

201.9

V22.2

650.0

G44.1

M79.6

M25.4

R53

I51.7

R55

K40

L72.1

N62

E65

Z00.1

Z01.4

C50

C81.9

Z33

080.0

ICD-10 Diagnosis

Delirium Tremens

Headache

Pain in Limb

Effusion of Lower Leg

Joint

Malaise and Fatigue

Cardiomegaly

Syncope and Collapse

Inguinal Hernia (NOS)

Sebaceous Cyst

Hypertrophy of Breast

Localized Adiposity

Routine Infant or Child

Health Check

Gynecological

Examination

Malignant Neoplasm of

Breast (NOS)

Hodgkin's Disease,

Unspecified Type

Pregnant State

Delivery in a Completely

Normal Case

Dental Caries

Chronic Gingivitis

34.

Dental 521.0

523.1

K02

K05.1

*Note

: Only 32 of the 34 markers are currently in use.

When the lenient

diagnostic certainty option is applied, any single diagnosis qualifying for an ADG marker will turn the marker on. However, the stringent

diagnostic certainty option can also be applied. For a subset of diagnoses, there must be more than one diagnosis qualifying for the marker in order for the ADG to be assigned. This was designed to provide greater confidence in the ADGs assigned to a patient. For more information, refer to Chapter

4 in the


.



Major ADGs

3-7

Some ADGs have very high expected resource use and are labeled as Major ADGs.

Table 2:

Major ADGs for Adult and Pediatric Populations presents major ADGs for adult and pediatric populations.

Table 2: Major ADGs for Adult and Pediatric Populations

Pediatric Major ADGs (ages 0-17 years) Adult Major ADGs (ages 18 and up)

3 Time Limited: Major

9 Likely to Recur: Progressive

11 Chronic Medical: Unstable

12 Chronic Specialty: Stable-Orthopedic

13 Chronic Specialty: Stable-Ear, Nose, Throat

18 Chronic Specialty: Unstable-Eye

25 Psychosocial: Recurrent or Persistent, Unstable

32 Malignancy


4 Time Limited: Major-Primary Infections



16 Chronic Specialty: Unstable-Orthopedic

22 Injuries/Adverse Effects: Major

25 Psychosocial: Recurrent or Persistent,

Unstable

32 Malignancy



Step 2: Creating a Manageable Number of ADG Subgroups (CADGs)

The ultimate goal of the ACG algorithm is to assign each person to a single morbidity group

(i.e., an ACG). There are 4.3 billion possible combinations of ADGs, so to create a more manageable number of unique combinations of morbidity groupings, the 32 ADGs are collapsed into 12 CADGs or Collapsed ADGs (

Table 3: Collapsed ADG Clusters and the

ADGs that they Comprise

. Like ADGs, CADGs are not mutually exclusive in that an individual can be assigned to as few as none or as many as 12.

Although numerous analytic techniques could be used to form CADGs from ADGs, the

ACG Case-Mix System has placed the emphasis on clinical cogency. The following three clinical criteria are used:

•

•

The similarity of likelihood of persistence or recurrence

of diagnoses within the ADG, i.e., time-limited, likely to recur, or chronic groupings

The severity

of the condition, i.e., minor versus major and stable versus unstable

• The types of healthcare services required

for patient management--medical versus specialty, eye / dental, psychosocial, prevention / administrative, and pregnancy

ADGs and CADGs can be used for various analytic and research applications that do not require mutually exclusive categories such as multivariate predictive or explanatory models.

See the ACG publication list in Appendix A for additional examples of these approaches.

Table 3: Collapsed ADG Clusters and the ADGs that they Comprise

Collapsed ADG (CADG)

1.

Acute Minor

2.

Acute Major

3.

Likely to Recur

4.

Asthma

5.


6.


7.

Chronic Specialty: Stable

ADGs in Each

1 Time Limited: Minor

2 Time Limited: Minor-Primary Infections

21 Injuries / Adverse Events: Minor

26 Signs / Symptoms: Minor


4 Time Limited: Major-Primary Infections

22 Injuries / Adverse Events: Major

27 Signs / Symptoms: Uncertain

28 Signs / Symptoms: Major

5 Allergies

7 Likely to Recur: Discrete

8 Likely to Recur: Discrete-Infections

20 Dermatologic

29 Discretionary

6 Asthma



32 Malignancy

10 Chronic Medical: Stable

30 See and Reassure

12 Chronic Specialty: Stable-Orthopedic



8.

Eye/Dental

9.

Chronic Specialty: Unstable

10.

Psychosocial

11.

Preventive/Administrative

12.

Pregnancy

13 Chronic Specialty: Stable-Ear, Nose, Throat

14 Chronic Specialty: Stable-Eye

34 Dental

16 Chronic Specialty: Unstable-Orthopedic

17 Chronic Specialty: Unstable-Ear, Nose, Throat

18 Chronic Specialty: Unstable-Eye

23 Psycho-social: Time Limited, Minor

24 Psycho-social: Recurrent or Persistent, Stable

25 Psycho-social: Recurrent or Persistent, Unstable

31 Prevention / Administrative

33 Pregnancy

3-9



Step 3: Frequently Occurring Combinations of CADGs (MACs)

The third step in the ACG categorization methodology assigns individuals into a single, mutually exclusive category, called a MAC. This grouping algorithm is based primarily on the pattern of CADGs.

Table 4: MACs and the Collapsed ADGs Assigned to Them below shows the MACs and the Collapsed ADGs which comprise them.

There are 23 commonly occurring combinations of CADGs which form MACs 1 through 23:

• The first 11 MACs correspond to presence of a single CADG.

• MAC-12 includes all pregnant women, regardless of their pattern of CADGs.

•

•

MACs 13 through 23 are commonly occurring combinations of CADGs.

• MAC-24 includes all other combinations of CADGs.

• MAC-25 is used for enrollees with no service use or invalid diagnosis input data.

MAC-26 includes all infants (age <12 months), regardless of the pattern of CADGs.

Table 4: MACs and the Collapsed ADGs Assigned to Them

MACs CADGs

1.

2.

3.

4.

5.

6.

7.

8.

9.

10.

11.

12.

Acute: Minor

Acute: Major

Likely to Recur

Asthma




Eye/Dental

Chronic Specialty: Unstable

Psychosocial

Prevention / Administrative

Pregnancy

7

8

5

6

3

4

1

2

9

10

11

All CADG combinations that include

CADG 12

1 and 2

1 and 3

1 and 6

13.

Acute: Minor and Acute: Major

14.

Acute: Minor and Likely to Recur

15.

Acute: Minor and Chronic Medical: Stable

16.

Acute: Minor and Eye/Dental

17.

Acute: Minor and Psychosocial

18.

Acute: Major and Likely to Recur

19.

Acute: Minor and Acute: Major and Likely to Recur

20.

Acute: Minor and Likely to Recur and Eye and Dental 1, 3 and 8

21.

22.

Acute: Minor and Likely to Recur and Psychosocial

Acute: Minor and Major and Likely to Recur and Chronic Medical: Stable

23.

Acute: Minor and Major and Likely to Recur

1 and 8

1 and 10

2 and 3

1, 2 and 3

1, 3, and 10

1, 2, 3, and 6

1, 2, 3, and 10



MACs and Psychosocial

24.

All Other Combinations Not Listed Above

25.

No Diagnosis or Only Unclassified Diagnosis

26.

Infants (age less than one year)

CADGs

All Other Combinations

No CADGs

Any CADGs combinations and less than one year old

Step 4: Forming the Terminal Groups (ACGs)

MACs form the major branches of the ACG decision tree. The final step in the grouping algorithm divides the MAC branches into terminal groups, the actuarial cells known as

ACGs. The logic used to split MACs into ACGs includes a combination of statistical considerations and clinical insight. During the ACG development process, the overarching goal for ACG assignment was to identify groups of individuals with similar needs for healthcare resources who also share similar clinical characteristics. Yale University’s

AUTOGRP Software (which performs recursive partitioning) was used to identify subdivisions of patients within a MAC who had similar needs for healthcare resources based on their overall expenditures. The variables taken into consideration included: age, sex, presence of specific ADGs, number of major ADGs, and total number of ADGs. (

Note:

Because prevention / administrative needs do not reflect morbidity, we do not include ADG

31 in the count of total ADGs 5 ).

See

Table 5: The Final ACG Categories, Reference Concurrent Weights, and RUBs below for a complete listing and description of all ACGs.

Concurrent ACG-Weights

A concurrent ACG weight is an assessment of the relative resource use for individuals in the

ACG. The concurrent weight is calculated as the mean cost of all patients in an ACG divided by the mean cost of all patients in the population. A fixed set of concurrent ACG-weights derived from external reference data is available as part of the software output file. Separate sets of weights exist for under age 65 working age populations and for over age 65 Medicare

(United States federal program that pays for certain health care expenses for people aged 65 or older) eligible populations and determined by the Risk Assessment Variables selected during processing. The weights produced by the software are relative weights, i.e., relative to a population mean, and are standardized to a mean of 1.0. An individual weight is associated with each ACG. In the case of an elderly reference set, the weights of some

ACGs (e.g., those associated with pediatrics, pregnancy or newborns) may be extrapolated from an under 65 population. The software-supplied weights may be considered a national reference or benchmark for comparisons with locally calibrated ACG-weights. In some

5 Readers are referred to Weiner (91) and Starfield (91) for more detail on the historical origins of the current system including the original Version 1.0 development process.


3-12 Adjusted Clinical Groups (ACGs ® ) instances (e.g., for those with limited or no cost data), these weights may also be used as a reasonable proxy for local cost data.

Table 5: The Final ACG Categories, Reference

Concurrent Weights, and RUBs provides a complete listing of ACGs and their corresponding nationally representative concurrent ACG-weight from the US Non-Elderly and US Elderly Risk Assessment Variables.(See the following discussion regarding the importance of rescaling so that dollars are not over predicted or under predicted.)

The software-supplied reference ACG-weights are supplied in two forms: unscaled and rescaled. Unscaled ACG-weights are simply the values of the reference ACG-weights applied to a population of interest. The mean value of the unscaled ACG-weights provides a rudimentary profiling statistic. If the mean of the unscaled ACG-weight is greater than 1.0 it indicates the rating population (the population to which the weights are being applied) is sicker than the reference population (the national reference database). If the mean is less than 1.0, it indicates the rating population is healthier. To ensure that dollars in the system are not over-estimated or under-estimated, the ACG System also makes available a rescaled or standardized ACG-weight that mathematically manipulates the unscaled ACG-weight to have a mean of 1.0 in the local population. The steps for performing this manually are discussed in Chapter 3 of the


.

Our experience indicates that concurrent (also referred to as retrospective) ACG-weights, especially when expressed as relative values, have remarkable stability. Where differences in

ACG-weights across plans are present, it is almost universally attributable to differences in covered services reflected by different benefit levels. The software provided concurrent weights associated with the US Non-Elderly Risk Assessment Variables were developed from a nationally representative database comprising approximately 3.5 million lives with comprehensive benefit coverage.

If local cost data are available, the ACG Software also calculates local ACG-weights. These local weights more accurately reflect local benefit levels and area practice patterns. In general it is recommended that the reference population (on which the weights are developed) should be as similar as possible to the assessment population to which the weights are applied. However in the absence of local cost data, the reference weights may prove useful for calculating reasonably representative profiling statistics (see Chapter 3 in the


).



Resource Utilization Bands (RUBs)

ACGs were designed to represent clinically logical categories for persons expected to require similar levels of healthcare resources (i.e., resource groups). However, enrollees with similar overall utilization may be assigned different ACGs because they have different epidemiological patterns of morbidity. For example, a pregnant woman with significant morbidity, an individual with a serious psychological condition, or someone with two chronic medical conditions may all be expected to use approximately the same level of resources even though they each fall into different ACG categories. In many instances it may be useful to collapse the full set of ACGs into fewer categories, particularly where resource use similarity, and not clinical cogency, is a desired objective.

ACGs are collapsed according to concurrent relative resource use in the creation of Resource

Utilization Bands (RUBs). The software automatically assigns six RUB classes:

• 0 - No or Only Invalid Dx

• 1 - Healthy Users

• 2 - Low

• 3 - Moderate

• High

• Very High


1500

1600

1710*

1711

1712

1720*

1721

ACG

0100

0200

0300

0400

0500

0600

0700

0800

0900

1000

1100

1200

1300


The relationship between ACG categories and RUBs is defined in

Table 5: The Final ACG

Categories, Reference Concurrent Weights, and RUBs

below.

Table 5: Final ACG Categories, Reference Concurrent Weights, and

RUBs

Reference Concurrent Weight

1400

1722

1730*

Description

Acute Minor, Age 1

Acute Minor, Age 2 to 5

Acute Minor, Age 6+

Acute Major

Likely to Recur, w/o

Allergies

Likely to Recur, w/

Allergies

Asthma

Chronic Medical:

Unstable



Eye & Dental

Chronic Specialty:

Unstable

Psychosocial, w/o

Psychosocial Unstable

Psychosocial, w/

Psychosocial Unstable, w/o Psychosocial Stable

Psychosocial, w/

Psychosocial Unstable, w/

Psychosocial Stable

Preventive/

Administrative

Pregnancy, 0-1 ADGs

Pregnancy, 0-1 ADGs,

Delivered

Pregnancy, 0-1 ADGs,

Not Delivered

Pregnancy, 2-3 ADGs, no

Major ADGs


Major ADGs, Delivered


Major ADGs, Not

Delivered

Pregnancy, 2-3 ADGs, 1+

Major ADGs

Non-Elderly

(0 to 64 Years)

0.29

0.13

0.14

0.31

0.20

0.22

0.22

1.11

0.32

0.19

0.12

0.19

0.30

0.68

0.72

2.77

1.14

0.11

1.82

2.58

0.41

2.16

3.03

Elderly

(65 Years and Older)

N/A

N/A

0.08

0.14

0.10

0.10

0.13

0.33

0.13

0.21

0.08

0.11

0.12

0.27

N/A

N/A

0.65

0.06

N/A

N/A

N/A

N/A

N/A

RUB

3

3

4

2

3

4

3

1

3

4

2

2

2

1

2

2

1

2

1

3

2

2

2



ACG

1731

1732

1740*

1741

1742

1750*

1751

1752

1760*

1761

1762

1770*

1771

1772

1800

1900

2000

2100

2200

2300


Description




Major ADGs, Not

Delivered


Major ADGs




Major ADGs, Not

Delivered


Major ADGs




Major ADGs, Not

Delivered

Pregnancy, 6+ ADGs, no

Major ADGs




Major ADGs, Not

Delivered

Pregnancy, 6+ ADGs, 1+

Major ADGs




Major ADGs, Not

Delivered

Acute Minor / Acute

Major

Acute Minor / Likely to

Recur, Age 1


Recur, Age 2 to 5


Recur, Age 6+, w/o

Allergy


Recur, Age 6+, w/

Allergy

Acute Minor / Chronic

Medical: Stable

Non-Elderly

(0 to 64 Years)

3.40

0.97

2.45

3.47

1.07

3.21

4.02

1.65

2.94

4.09

1.77

4.64

5.71

0.28

0.31

0.38

3.27

0.45

0.45

0.25

Elderly


N/A

N/A

N/A

N/A

N/A

N/A

N/A

N/A

N/A

N/A

N/A

N/A

N/A

0.12

0.12

0.15

N/A

0.18

N/A

N/A

RUB

3-15

4

2

2

2

3

4

4

2

2

2

3

4

4

3

4

4

4

3

4

4


3100

3200

3300

3400

3500

2700

2800

2900

3000

3600

3700

3800

3900

3-16

ACG

2400

2500

2600



Description

Acute Minor / Eye &

Dental

Acute Minor /

Psychosocial, w/o

Psychosocial Unstable

Acute Minor /

Psychosocial, w/

Psychosocial Unstable, w/o Psychosocial Stable

Acute Minor /

Psychosocial, w/

Psychosocial Unstable, w/

Psychosocial Stable

Acute Major / Likely to

Recur

Acute Minor / Acute

Major / Likely to Recur,

Age 1

Acute Minor / Acute


Age 2 to 5

Acute Minor / Acute


Age 6 to 11

Acute Minor / Acute


Age 12+, w/o Allergy

Acute Minor / Acute


Age 12+, w/ Allergy


Recur / Eye & Dental


Recur / Psychosocial

Acute Minor / Acute

Major / Likely to Recur /


Acute Minor / Acute

Major / Likely to Recur /

Psychosocial

2-3 Other ADG

Combinations, Age 1 to

17

2-3 Other ADG

Combinations, Males Age

18 to 34

Non-Elderly

(0 to 64 Years)

0.21

0.38

0.75

1.30

0.52

0.83

0.52

0.48

0.78

0.77

0.39

0.62

1.36

1.26

0.47

0.56

Elderly


0.12

0.12

0.22

0.28

0.20

N/A

N/A

N/A

0.27

0.27

0.16

0.25

0.45

0.45

N/A

N/A

RUB

2

2

3

3

3

3

3

3

3

3

3

2

3

3

2

3



ACG

4000

4100

4210

4220

4310

4320

4330

4410

4420

4430

4510

4520

4610

4620

4710


Description

2-3 Other ADG

Combinations, Females

Age 18 to 34

2-3 Other ADG

Combinations, Age 35+

4-5 Other ADG


17, no Major ADGs

4-5 Other ADG


17, 1+ Major ADGs

4-5 Other ADG


44, no Major ADGs

4-5 Other ADG


44, 1 Major ADGs

4-5 Other ADG


44, 2+ Major ADGs

4-5 Other ADG

Combinations, Age 45+, no Major ADGs

4-5 Other ADG

Combinations, Age 45+, 1

Major ADGs

4-5 Other ADG

Combinations, Age 45+,

2+ Major ADGs

6-9 Other ADG

Combinations, Age 1 to 5, no Major ADGs

6-9 Other ADG

Combinations, Age 1 to 5,

1+ Major ADGs

6-9 Other ADG


17, no Major ADGs

6-9 Other ADG


17, 1+ Major ADGs

6-9 Other ADG

Combinations, Males,

Age 18 to 34, no Major

ADGs

Non-Elderly

(0 to 64 Years)

0.50

0.70

0.61

1.16

0.69

1.30

2.32

0.88

1.56

2.67

1.08

1.94

1.04

2.45

1.03

Elderly


N/A

0.25

N/A

N/A

N/A

N/A

N/A

0.27

0.46

0.81

N/A

N/A

N/A

N/A

N/A

RUB

3-17

3

3

3

4

3

3

4

3

3

3

3

3

4

3

4


4930

4940

5010

5020

4830

4910

4920

5030

5040

5050

3-18

ACG

4720

4730

4810

4820



Description

6-9 Other ADG


Age 18 to 34, 1 Major

ADGs

6-9 Other ADG


Age 18 to 34, 2+ Major

ADGs

6-9 Other ADG

Combinations, Females,

Age 18 to 34, no Major

ADGs

6-9 Other ADG


Age 18 to 34, 1 Major

ADGs

6-9 Other ADG


Age 18 to 34, 2+ Major

ADGs

6-9 Other ADG


0-1 Major ADGs

6-9 Other ADG


Major ADGs

6-9 Other ADG


Major ADGs

6-9 Other ADG


4+ Major ADGs

10+ Other ADG


17, no Major ADGs

10+ Other ADG


17, 1 Major ADGs

10+ Other ADG


17, 2+ Major ADGs

10+ Other ADG


0-1 Major ADGs

10+ Other ADG


Major ADGs

Non-Elderly

(0 to 64 Years)

1.87

4.15

1.15

1.85

3.33

1.93

3.68

6.35

11.80

2.13

3.78

13.30

3.04

4.78

Elderly


N/A

N/A

N/A

N/A

N/A

0.61

1.09

1.74

2.90

N/A

N/A

N/A

0.92

1.49

RUB

3

4

3

3

4

3

4

5

5

3

4

5

4

4




ACG Description

Non-Elderly

(0 to 64 Years)

Elderly

(65 Years and Older) RUB

5060

5070

5110

5200

5310*

5311

5312

5320*

5321

5322

5330*

5331

5332

5340*

5341

5342

9900

10+ Other ADG


Major ADGs

10+ Other ADG


4+ Major ADGs

No Diagnosis or Only

Unclassified Diagnosis

(two input files)

Non-Users (two input files)

Infants: 0-5 ADGs, no

Major ADGs


Major ADGs, Low Birth

Weight


Major ADGs, Normal

Birth Weight

Infants: 0-5 ADGs, 1+

Major ADGs



Weight


Major ADGs, Normal

Birth Weight

Infants: 6+ ADGs, no

Major ADGs



Weight


Major ADGs, Normal

Birth Weight

Infants: 6+ ADGs, 1+

Major ADGs



Weight


Major ADGs, Normal

Birth Weight

Invalid Age or Date of

Birth

7.72

18.35

0.16

0.00

0.80

2.43

0.78

2.92

9.94

2.09

1.53

4.72

1.48

10.35

29.05

5.74

0.00

2.26

4.66

0.18

0.00

N/A

N/A

N/A

N/A

N/A

N/A

N/A

N/A

N/A

N/A

N/A

N/A

0.00

5

5

1

0

3

4

3

4

5

4

3

4

3

5

5

4

0

Source: IMS Health Payer Solutions, Watertown, MA; Subset of the Patient-Centric Database containing a national cross-section of managed care plans; population of 3,523,473 commercially insured lives (less than 65 years old) and population of 463,126 Medicare beneficiaries (65 years and older), 2008-09.



*Note

: The default is to subdivide these groups on delivery or low birth weight status.

Grouping the ACGs without these divisions is optional and must be turned on in order to be used.



Figure 1: ACG Decision Tree

on the following page illustrates the main branches of the

ACG decision tree. Some MACs are not subdivided by the characteristics listed above because doing so did not increase the explanatory power of the ACG model. Some include only a single CADG: for instance, MAC-02 is composed of individuals with only acute major conditions. Others, such as MAC-01, acute conditions only, are subdivided into three age groups: ACG 0100 (Age = one year), ACG 0200 (Age = two to five years), and ACG

0300 (six or more years) because resource use differs by age for individuals with this pattern of morbidity. MAC-10, including individuals with psychosocial morbidity only and MAC-

17, including individuals with psychosocial and acute minor conditions, are further split by the presence of ADG-24 (recurrent or chronic stable psychosocial conditions) and ADG-25

(recurrent or chronic unstable psychosocial conditions).



Figure 1: ACG Decision Tree

ACG 9900

Missing Age

Entire Population

Age <1 w/ diagnosis

MAC-26

To

MAC-26 tree

Age>= 1

Split into MACs

Based on CADGs

MAC-1

Age

MAC-3

MAC-2

ACG

0400

MAC-4

ACG

0700

ADG

05?

MAC-5

ACG

0800

MAC-6

ACG

0900

MAC-7

ACG

1000

1

ACG 0100

2-5

ACG 0200

6+

ACG 0300

Yes

ACG 0600

No ACG 0500

Key

CADG Collapsed ADG

ADG Aggregated Diagnosis Group

MAC-8

ACG

1100

MAC-9

ACG

1200

ADG

25?

MAC-10

Yes No

ACG 1300

ADG

24?

Yes ACG 1500

No ACG 1400


MAC-11

ACG

1600

MAC-13

ACG

1800

MAC-12

MAC-15

ACG

2300

MAC-14

MAC-17

MAC-16

ACG

2400

MAC-19

MAC-18

ACG

2800

MAC-21

ACG

3500

MAC-23

ACG

3700

MAC-20

ACG

3400

MAC-22

ACG

3600

MAC-25

MAC-24

To

MAC-12 tree

1 ACG 1900

2-5 ACG 2000

6+

Age

Yes

ACG 2200

ADG

05?

No

ACG 2100

ADG

25?

Age

To

MAC-24 tree

Yes No

ACG 2500

ADG

24?

1 ACG 0100

2-5 ACG 0200

6+ ACG 0300

12+

Yes

ACG 2700

No

ACG 2600

Yes

ACG 2200

ADG

05?

1 or 2 input files?

1

ACG 5100

2

Claims

Info?

No

ACG 2100

Yes ACG 5110

No ACG 5200



Figure 2: Decision Tree for MAC-12—Pregnant Women on the following page illustrates the grouping logic for pregnant women. All women with at least one diagnosis code indicating pregnancy are assigned to MAC-12. The ACGs for pregnant women are formed with subdivisions first on total number of ADGs (0-1, 2-3, 4-5, 6+) and second, for individuals with two or more ADGs, a split on none versus one or more major ADGs. These two splits yield seven ACGs for pregnant women.

The standard seven ACGs for pregnant women can be further subdivided according to whether delivery has occurred during the time period of interest, yielding a total of 14 ACGs for women with a diagnosis of pregnancy. Either diagnosis codes for delivery or usersupplied information on delivery (e.g., CPT-4 codes for delivery) can be used to separate pregnant women according to delivery status. Because of the marked differences in resource consumption for women with and without delivery and generally adequate validity of diagnoses associated with delivery, most organizations will find this option desirable to use.

By default, the software will use diagnosis codes to subdivide based on delivery status

Refer to the chapter entitled, “Basic Data Requirements” in the

Installation and Usage Guide for a more detailed discussion of appropriate means of identifying delivery status using userdefined flags.



Figure 2: Decision Tree for MAC-12—Pregnant Women

MAC-12

# of

ADGs

0-1

ACG 1710

0

ACG 1720

# of

Major

ADGs

2-3

1+

ACG 1730 ACG 1740

0

# of

Major

ADGs

4-5

1+

ACG 1750

Note:

This level of branching is optional.

Delivered?

Delivered?

Delivered?

Delivered?

Delivered?

0

# of

Major

ADGs

6+

1+

ACG 1760 ACG 1770

Delivered?

Delivered?

Yes ACG 1711

No

ACG 1712

Key

CADG Collapsed ADG


Yes ACG 1721

No

ACG 1722

Yes ACG 1731

No

ACG 1732

Yes

ACG 1741

No

ACG 1742

Yes

ACG 1751

No

ACG 1752

Yes ACG 1761

No ACG 1762

Yes ACG 1771

No ACG 1772



Figure 3: Decision Tree for MAC-26—Infants

on the following page illustrates the branching algorithm for MAC-26, which includes all infants, regardless of their pattern of

CADGs. The first bifurcation is made on the total number of ADGs. Each group is further subdivided by presence of the number of major ADGs. These two splits yield four ACG groups.

For the infant ACGs, there is an optional additional split on birth weight. If there is accurate birth weight information that can be linked with claims and enrollment files, the four standard infant ACGs can be further split into low birth weight (<2,500 grams) and normal birth weight (>2,500 grams). Our developmental work suggests that this additional split improves the explanatory power of the ACG System. However, two caveats are important to consider before using this ACG option. First, our research indicates poor validity for existing

ICD-9-CM birth weight codes in some administrative data sets. Second, some populations may have such low rates of low birth weight infants that the number of infants grouped into an ACG may be too small for accurate estimates. In general, we recommend that at least 30 individuals per ACG are needed to obtain stable estimates of average resource use for that

ACG. By default, the ACG System will divide infants based upon the presence or absence of a diagnosis code indicating low birth weight.

Refer to the chapter entitled, “Basic Data Requirements” in the

Installation and Usage Guide for a more detailed discussion of appropriate means of identifying low birth weight status using user-defined flags.



Figure 3: Decision Tree for MAC-26—Infants

0

# of

Major

ADGs

0-5

1+

ACG 5310 ACG 5320

Note

: This level of branching is optional.

*

Low birth weight refers to infants weighing less than

2500 grams.

Key

CADG Collapsed ADG


Low

Birth weight?*

Low

Birth weight?*

Yes ACG 5311

No

ACG 5312

Yes ACG 5321

No ACG 5322

MAC-26

# of

ADGs

0

# of

Major

ADGs

6+

1+

ACG 5330 ACG 5340

Low

Birth weight?*

Yes ACG 5331

No

ACG 5332

Low

Birth weight?*

Yes ACG 5341

No ACG 5342



Figure 4: Decision Tree for MAC-24—Multiple ADG Categories

on the following page illustrates the last branch of the ACG tree, MAC-24, which includes less frequently occurring combinations of CADGs. There are 33 ACGs within MAC-24. With MAC-24, the first two splits are total number of ADGs (2-3, 4-5, 6-9, and 10+) and then, within each of these four groups, by age. The age splits separate children (1-17 years) from adults (18+), and in some cases further subdivides within these groups. Additional divisions are made on sex and number of major ADGs.



Figure 4: Decision Tree for MAC-24—Multiple ADG Categories

MAC-24

# of

ADGs

2-3

Age

1-17

ACG 3800

4-5

Age

1-17

# of

Major

ADGs

18-34

Sex

Male

ACG 3900

Female

ACG 4000

18-44

# of

Major

ADGs

35+

ACG 4100

45+

# of

Major

ADGs

Key

CADG Collapsed ADG



0

ACG 4210

1+

ACG 4220

0

ACG 4310

1

ACG 4320

2+

ACG 4330

6-9

Age

1-5

# of

Major

ADGs

6-17

# of

Major

ADGs

18-34

Sex

0

ACG 4410

1

ACG 4420

2+

ACG 4430

35+

# of

Major

ADGs

0

ACG 4510

1+

ACG 4520

0

ACG 4610

1+

ACG 4620

0-1

ACG 4910

2

ACG 4920

3

ACG 4930

4+

ACG 4940

M

# of

Major

ADGs

0

ACG 4710

1

ACG 4720

2+

ACG 4730

F

# of

Major

ADGs

0

ACG 4810

1

ACG 4820

2+

ACG 4830

10+

Age

1-17

# of

Major

ADGs

0

ACG 5010

1

ACG 5020

2+

ACG 5030

18+

# of

Major

ADGs

0-1

ACG 5040

2

ACG 5050

3

ACG 5060

4+

ACG 5070



Clinical Aspects of ACGs

An ACG, the building block of the ACG System, is assigned based on all diagnosis codes assigned to a person by providers during a predetermined period of time. This makes ACGs different from most other case-mix measures (e.g., Diagnosis-Related Groups--DRGs,

Ambulatory Patient Categories--APCs, or Episode Treatment Groups--ETGs). In these other systems, the case-mix unit of analysis is based on a designated service period and usually a single distinct clinical condition. For example, the service period may be defined based on a single procedure or an episode of care. In contrast, ACGs are based on all morbidities for which a person receives services over a defined period of time.

The first step in the ACG assignment process is to categorize every ICD diagnosis code given to a patient into a unique morbidity grouping known as an ADG. Each ADG is a group of ICD diagnosis codes that are homogenous with respect to specific clinical criteria and their demand on healthcare services. Patients with only one diagnosis over a time period are assigned only one ADG, while a patient with multiple diagnoses can be assigned to one or more ADGs:



Example :

A patient with both Obstructive Chronic Bronchitis (ICD-9-CM code 491.2) and Congestive Heart Failure (ICD-9-CM code 428.0) will fall into only one ADG,

Chronic Medical: Unstable (ADG-11), while a patient with Candidiasis of Unspecified

Site (ICD-9-CM code 112.9) and Acute Upper Respiratory Infections of Unspecified Site

(ICD-9-CM code 465.9) will have two ADGs, Likely to Recur: Discrete Infections

(ADG-8), and Time Limited: Minor-Primary Infections (ADG-2), respectively.

ADGs represent a shift from standard methods for categorizing diagnosis codes. A class of diagnoses, such as all those for diabetes or bacterial pneumonia, may indicate a process that affects a specific organ system or pathophysiologic process. The criteria for ADG - assignment depends on those features of a condition that are most helpful in understanding and predicting the duration and intensity of healthcare. Five clinical criteria guide the assignment of each diagnosis code into an ADG -: duration, severity, diagnostic certainty, type of etiology, and expected need for specialty care.

Table 6: Duration, Severity,

Etiology, and Certainty of the ADGs

shows how each of these five clinical criteria is applied to the 32 ADGs.

Duration

What is the expected length of time the health condition will last? Acute conditions are time limited and expected to resolve completely. Recurrent conditions occur episodically with inter-current disease-free intervals. Chronic conditions persist and are expected to require long-term management generally longer than one year.



Severity

What is the expected prognosis? How likely is the condition to worsen or lead to impairment, death, or an altered physiologic state? The ADG-taxonomy divides acute conditions into minor and major categories corresponding to low and high severity, respectively. The system divides chronic conditions into stable and unstable based on the expected severity over time. Unstable conditions are more likely to have complications

(related co-morbidities) than stable conditions and are expected to require more resources on an ongoing basis (e.g., more likely to need specialty care).

Diagnostic Certainty

Some diagnosis codes are given for signs / symptoms and are associated with diagnostic uncertainty. As such, they may require watchful waiting only or substantial work-up. The three ADGs for signs / symptoms are arranged by expected intensity of diagnostic work-up, from low to intermediate to high.

Etiology

What is the cause of the health condition? Specific causes suggest the likelihood of different treatments. Infectious diseases usually require anti-microbial therapy; injuries may need emergency medical services, surgical management, or rehabilitation; anatomic problems may require surgical intervention; neoplastic diseases could involve surgical care, radiotherapy, chemotherapy; psychosocial problems require mental health services; pregnancy involves obstetric services; and, medical problems may require pharmacologic, rehabilitative, or supportive management.

Expected Need for Specialty Care

Would the majority of patients with this condition be expected to require specialty care management from one of the following types of specialized providers: orthopedic surgeon, otolaryngologist, ophthalmologist, or dermatologist? In addition to these subspecialties, certain other ADG categories imply that specialty care is more likely.



Table 6: Duration, Severity, Etiology, and Certainty of the ADGs

Note :

ADGs 15 and 19 are no longer used.

ADG Duration Severity Etiology

1.

Time Limited: Minor

2.

Time Limited: Minor-Primary Infections

3.

Time Limited: Major

4.

Time Limited: Major-Primary Infections

5.

Allergies

6.

Asthma

7.


8.

Likely to Recur: Discrete-Infections

9.


10.


11.


12.

Chronic Specialty: Stable-Orthopedic

13.

Chronic Specialty: Stable-Ear, Nose, Throat

14.

Chronic Specialty: Stable-Ophthalmology

16.

Chronic Specialty: Unstable-Orthopedics

17.

Chronic Specialty: Unstable-Ear, Nose, Throat

Acute

Acute

Acute

Acute

Recurrent

Recurrent or

Chronic

Recurrent

Recurrent

Recurrent

Chronic

Chronic

Chronic

Chronic

Chronic

Chronic

Chronic

Low

High

Low

High

Low

Low

Low

High

High

Low

Low

High

High

Low

Low

Low

Medical, noninfectious

Medical, infectious


Medical, infectious

Allergy

Mixed


Medical, infectious




Anatomic/

Musculoskeletal

Anatomic/Ears,

Nose, Throat

Anatomic/Eye

Anatomic/

Musculoskeletal

Anatomic/Ears,

Diagnostic

Certainty

High

High

High

High

High

High

High

High

High

High

High

High

High

High

High

High

Expected Need for

Specialty Care

Unlikely

Unlikely

Likely

Likely

Possibly

Possibly

Unlikely

Unlikely

Likely

Unlikely

Likely

Likely: orthopedics

Likely: ENT

Likely: ophthalmology

Likely: orthopedics

Likely: ENT

Technical Reference Manual The Johns Hopkins ACG System, Version 10.0


ADG Duration Severity

18.

Chronic Specialty: Unstable-Ophthalmology

20.

Dermatologic

21.

Injuries / Adverse Effects: Minor

22.

Injuries / Adverse Effects: Major

23.

Psychosocial: Time Limited, Minor

24.

Psychosocial: Recurrent or Chronic, Stable

25.

Psychosocial: Recurrent or Persistent, Unstable

26.

Signs / Symptoms: Minor

27.

Signs / Symptoms: Uncertain

28.

Signs / Symptoms: Major

29.

Discretionary

30.

See and Reassure

31.


32.

Malignancy

33.

Pregnancy

34.

Dental

Chronic

Acute,

Recurrent

Acute

Acute

Acute

Recurrent or

Chronic

Recurrent or

Chronic

Uncertain

Uncertain

Uncertain

Acute

Acute

N/A

Chronic

Acute

Acute,

Recurrent,

Chronic

High

Low to High

Low

High

Low

Low

High

Low

Uncertain

High

Low or High

Low

N/A

High

Low

Low to High

Etiology

Nose, Throat

Anatomic/Eye

Mixed

Injury

Injury

Psychosocial

Psychosocial

Psychosocial

Mixed

Mixed

Mixed

Anatomic

Anatomic

N/A

Neoplastic

Pregnancy

Mixed

Diagnostic

Certainty

Expected Need for

Specialty Care

High

High

High

High

High

High

High

Low

Low

Low

High

High

N/A

High

High

High

Likely: ophthalmology

Likely: dermatology

Unlikely

Likely

Unlikely

Likely: mental health

Likely: mental health

Unlikely

Uncertain

Likely

Likely: surgical specialties

Unlikely

Unlikely

Likely: oncology

Likely: obstetrics

Likely: dental



Clinically Oriented Examples of ACGs

Patients are categorized into an ACG based on the pattern of ADGs experienced over a predetermined interval and, in some cases, their age and sex. This approach focuses on the totality of diseases experienced by a person rather than any specific disease. Because this method diverges from the traditional biomedical, categorical method of examining morbidity, we show how ACGs classify patients with specific types of diseases.

In the examples that follow, we categorize patients by choosing a specific clinical feature that they have, such as a disease, pregnancy, or by their age. These examples show how the presence of other diseases or total number of ADGs changes ACG assignment.

Chronic Illnesses

On the following pages

, Example 1: Hypertension

presents three patients with hypertension and

Example 2: Diabetes Mellitus

presents three patients with diabetes.

These individuals were actual patients selected from a private healthcare organization database. The input data used by the ACG grouping software, the output produced by the software, and the associated resource consumption variables are presented. As these patients demonstrate, there is substantial variability in patterns of morbidity and need for healthcare for different patients classified by a specific condition such as hypertension or diabetes.

Thus, knowing only that a patient has a particular medical problem, even if it is a chronic condition, provides little information about the need for medical services. In general, as the number of different types of morbidities increases, the total number of ambulatory visits increases as does total expenditures. However, the total burden of morbidity as represented by the ACG – that is, the constellation of ADGs and presence of major ADGs is the most important determinant of resource consumption.

In

Example 1: Hypertension

, during the assessment period Patient 1 had diagnosis codes given only hypertension and a routine medical exam and is therefore classified into the ACG for patients with stable, chronic medical conditions (ACG-0900). In contrast, Patient 3 with hypertension is in an ACG that branches from MAC-24 (combinations of ADGs not otherwise classified). This occurs because their combinations of ADGs occur too infrequently to merit a separate ACG. Patients in MAC-24 have both high levels of morbidity and high levels of health need. There is a strong link between the total number of

ADGs / major ADGs and resource consumption.

There are two additional ACGs that describe commonly occurring combinations of morbidity for individuals with stable, chronic medical conditions. ACG-2300 (Chronic Medical--Stable and Acute Minor) is assigned to patients with uncomplicated diabetes, hypertension, or other stable chronic conditions and a minor illness, injury, or symptom. As shown in Patient 2 with Hypertension, individuals in ACG-3600 have four types of morbidities: stable chronic medical conditions (which include the diagnosis of hypertension), acute minor conditions, conditions of low severity likely to reoccur, and acute major conditions.



Example 1: Hypertension

The following patient types demonstrate the levels of hypertension, ADGs, and associated costs.

Patient 1: Low Cost Patient with Hypertension

ACG Output Input Data/Patient

Characteristics

Age / Sex: 51/Male ACG-0900: Chronic Medical, Stable

Conditions: Hypertension, General

Medical Exam

ADGs: 10 and 31.

Chronic Medical: Stable, Prevention /

Administrative

Resource

Consumption

Variables

Total Cost: $128

Ambulatory visits: 1

Emergency visits: 0

Hospitalizations: 0

Patient 2: High Cost Patient with Hypertension

Input Data/Patient

Characteristics

Age / Sex: 54 / Male

Conditions: Hypertension, Disorders of Lipid Metabolism, Low Back

Pain, Cervical Pain Syndromes,

Musculoskeletal Signs and

Symptoms

ACG Output

ACG-3600: Acute Minor/Acute Major /

Likely Recur / Eye & Dental

ADGS: 07, 10, 26, and 27

Likely to Recur: Discrete Chronic Medical:

Stable



Resource

Consumption

Variables

Total Cost: $3,268


Emergency visits: 1

Hospitalizations: 0



Patient 3: Very High Cost Patient with Hypertension


Characteristics

ACG Output

Age / Sex: 52 / Male

Resource Consumption

Variables

Total Cost: $45,937


Emergency visits: 0

Hospitalizations: 1 Conditions: Hypertension,

General medical exam,

Cardiogenic Shock, Asthma,

Low back pain, Peripheral

Neuropathy, Anxiety,

Depression, COPD, Acute

Upper Respiratory Infection,

Gastroesophageal Reflux, Iron

Deficiency, Cervical Pain

Syndromes, Sleep Problems,

Obesity, Sinusitis, Joint Pain

ACG - 5070: 10+Other ADG

Combinations, Age >17, 4+ Major

ADGs

ADGs: 02, 03*, 06, 07, 09*, 10,

11*, 16*, 24, 27, 28, and 31.

Time Limited: Minor-Primary

Infections

Time Limited: Major, Asthma



Chronic Medical: Stable Chronic

Medical: Unstable Chronic

Specialty: Unstable-Orthopedic,


Persistent,

Stable Signs / Symptoms:

Uncertain

Signs / Symptoms: Major, and


*

Major ADG

3-35



Example 2: Diabetes Mellitus

The following patient types demonstrate the levels of diabetes mellitus, ADGs, and associated costs.

Patient 1: Low Cost Patient with Diabetes

ACG Output


Characteristics

Age / Sex: 49 / Female

Conditions: Diabetes mellitus

ACG-0900: Chronic Medical, Stable

ADGs: 1 0


Resource

Consumption

Variables

Total Cost: $296


Emergency visits: 0

Hospitalizations: 0

Patient 2: High Cost Patient with Diabetes


Characteristics


Conditions: Diabetes mellitus,

Disorders of Lipid Metabolism,

Peripheral Neuropathy, Otitis

Media, Gastroesophageal Reflux,

Acute sprain, Joint disorder,

Bursitis, Arthropathy

ACG Output

ACG-3600: Acute Minor / Acute

Major / Likely Recur / Eye & Dental

ADGs: 01, 07, 08, 10, 22*, 26, and 27

Time Limited: Minor



Chronic Medical: Stable,

Injuries / Adverse Effects: Major Signs

/ Symptoms: Minor


Resource

Consumption

Variables

Total Cost: $1,698


Emergency visits: 1

Hospitalizations: 0

*

Major ADG



Patient 3: Very High Cost Patient with Diabetes


Characteristics


Conditions: Diabetes mellitus,

General medical exam, Ischemic

Heart Disease, Hypertension,

Disorders of Lipid Metabolism, Low

Back Pain, Peripheral Neuropathy,

Cerebrovascular Disease, COPD,

Acute Lower Respiratory Tract

Infection, Allergic Rhinitis,

Gingivitis, Otitis Media, Hearing

Loss, Chest Pain, Syncope, Chronic

Cystic Disease of the Breast,

Tobacco Use, Abdominal Pain,

Sinusitis, Sleep Apnea, Contusions and Abrasions, Headache, Cough,

Fatigue

ACG Output

ACG-5070: 10 + Other ADG

Combinations, age >17, 4+ Major

ADGs

ADGs: 01, 02, 03*, 04*, 05, 07, 08,

09*, 10, 11*, 12, 16*, 17, 21, 22*,

23, 26, 27, 28, 29, 30, 31 and 34.

Time Limited: Minor

Time Limited: Minor- Primary

Infections

Time Limited: Major

Time Limited: Major-Primary

Infections, Allergies





Chronic Medical: Unstable Chronic

Specialty: Stable-Orthopedic


Orthopedic

Chronic Specialty: Unstable-Ear,

Nose, Throat

Injuries / Adverse Effects: Minor


Psychosocial: Time Limited, Minor


Signs / Symptoms: Uncertain Signs /

Symptoms: Major,

Discretionary,

See / Reassure, and Prevention /

Administrative

*

Major ADG

Resource

Consumption

Variables

Total Cost: $33,073


Emergency visits: 2

Hospitalizations: 1

3-37



Pregnancy

Using diagnosis codes for pregnancy, the ACG, system identifies all women who were pregnant during the assessment period and places them into the pregnancy MAC. ACGs, are formed based on (1) total number of ADGs, (2) presence of complications (i.e., presence of a major ADG), and (3) whether the woman delivered (the default level of assignment can be overridden).

Example 3: Pregnancy / Delivery with Complications

shows how the ACG, System groups women with a complicated pregnancy / delivery. Both women in the example had

ICD-9-CM codes that map to ADG-03 (an acute major morbidity). The salient difference between the two that explains the difference in resource consumption is that Patient 2 had a greater number of ADGs and more major ADGs and thus fits into a more resource intensive

ACG. That is, Patient 2 had a higher level of morbidity than Patient 1, even though both women experienced a complicated pregnancy / delivery.

Table 7: Clinical Classification of Pregnancy / Delivery ACGs

presents an alternative clinical categorization of the pregnancy/delivery ACGs. Three dimensions are used to classify the ACGs – number of ADGs, presence of major ADGs, and whether the women delivered during the assessment period. Resource consumption increases along each of the three axes: presence of delivery, presence of a major ADG, and number of ADGs. Using various combinations of these ACGs, a clinician, or manager can determine the proportion of women with complicated pregnancies and deliveries overall, and with different levels of morbidity. The need for specialty services will be greatest for those women with higher levels of morbidity and complications as defined by presence of a major ADG.



Example 3: Pregnancy / Delivery with Complications

The following patient types demonstrate the levels of pregnancy and delivery with complications, ADGs, and associated costs.

Patient 1: Pregnancy / Delivery with Complications, Low Morbidity


Characteristics


Conditions: General medical exam, Pregnancy and delivery uncomplicated and Pregnancy and delivery - with complications.

*

Major ADG

ACG Output

ACG-1731: 2-3 ADGs, 1+ Major

ADGs, Delivered

ADGs: 01, 03*, 31, and 33.

Time Limited: Minor, Time Limited:

Major

Prevention / Administrative, and

Pregnancy

Resource

Consumption

Variables

Total Cost: $8,406


Emergency visits: 0

Hospitalizations: 1Y

Patient 2: Pregnancy / Delivery with Complications, High Morbidity


Characteristics

Age/Sex: 36/Female

Resource

Consumption

Variables

Total Cost: $19,714


Emergency visits: 2

Hospitalizations: 1

Conditions: General medical exam,

Hypertension, Low Back Pain,

Urinary tract infection, Renal

Calculi, Cervical Pain

Syndromes, Joint disorder,

Pregnancy & delivery-with complications.

ACG Output

ACG-1771: 6+ ADGs, 1+ Major

ADGs, Delivered

ADGs: 03*, 07, 08, 10, 11*, 21, 22*,

28, 31, and 33.

Time Limited: Major,


Likely to Recur: Discrete-infections


Chronic Medical: Unstable Injuries /

Adverse Effects: Minor


Signs / Symptoms: Major Prevention

/ Administrative, and Pregnancy

*

Major ADG

3-39



Table 7: Clinical Classification of Pregnancy / Delivery ACGs

ACG Levels

Morbidity

Level

Low

(1-3 ADGs)

Mid

(4-5 ADGs)

High

(6+ ADGs)

Pregnancy Only

Uncomplicated

(No Major

ADGs)

1712, 1722

1742

1762

Complicated

(1+ Major

ADGs )

1732

1752

1772

Delivered

Uncomplicated

(No Major

ADGs)

1711, 1721

1741

1761

Complicated

(1+ Major

ADGs)

1731

1751

1771

Infants

The ACG System places all infants into an infant MAC. By definition, all had at least one hospitalization (at time of delivery). ACG groups are formed based on total number of

ADGs and the presence of a complication or major ADG.

Table 8: Clinical Classification of Infant ACGs

provides a clinical classification of the four infant ACGs.

Example 4:

Infants compares an infant in the low morbidity / no complications ACG. (5310, the most frequent infant ACG) with an infant in the higher morbidity / with complications ACG

(5340, the most resource intensive infant ACG.). Infant 1 had a typical course: hospitalization at birth, routine check-ups, and illness management for upper respiratory tract infection and otitis media. Infant 2 presents a completely different picture in terms of pattern of morbidity and resource consumption, both of which are substantially greater in comparison with Infant 1.



Example 4: Infants

The following patient types demonstrate the levels of infants with complications, ADGs, and associated costs.

Patient 1: Infant with Low Morbidity, Normal Birthweight


Characteristics


Conditions: General medical exam,

Otitis media, Acute upper respiratory tract infection, Fungal infection and Gastroesophageal

Reflux

ACG Output

ACG 5312: 0-5 ADGs, No Major

ADGs, Normal Birthweight,

ADGs: 02, 08, 26 ,and 31

Time Limited: Minor


Signs / Symptoms: Minor, and

Prevention / Administration

Resource

Consumption

Variables

Total Cost: $3,208


Emergency visits: 0

Hospitalizations: 0

Patient 2: Infant with High Morbidity, Low Birthweight


Characteristics

Age / Sex: 0 / Male

Conditions: General medical exam, Respiratory symptoms

Congenital Heart Disease,

Cardiac Arrhythmia, Septicemia,

Nausea, vomiting,

Gastroesophageal Reflux,

Neonatal Jaundice, Renal

Disorders, Endocrine disorders,

Vesicouretal reflux

ACG Output

ACG 5341: 6+ ADGs, 1+ Major

ADGs, Low Birthweight

ADGs: 03*, 04, 07 , 10, 11*, 22, 26,

27, 28, and 31

Time Limited: Major

Time Limited: Major-Primary

Infections


Chronic Medical: Stable, Chronic

Medical: Unstable




Signs / Symptoms: Major,

Discretionary, and Prevention /

Administrative

Resource

Consumption

Variables

Total Cost: $165,142


Emergency visits: 0

Hospitalizations: 1

*

Major ADG



Table 8: Clinical Classification of Infant ACGs

Morbidity

Level

Low

(0-5 ADGs)

Mid

(6+ ADGs)

No Complications

(No Major ADGs)

5310

5330

Complication

(1+ Major ADGs)

5320

5340


Expanded Diagnosis Clusters (EDCs TM ) 4-i

4 Expanded Diagnosis Clusters (EDCs

TM

)

Overview ..................................................................................................................... 4-1

Development of EDCs ................................................................................................ 4-1

Understanding How EDCs Work ............................................................................. 4-3

Table 1: ICD-9-CM and ICD-10 Codes Assigned to Otitis Media EDC

(EAR01) ................................................................................................................... 4-3

Table 2: Examples of Diabetes Complications ....................................................... 4-6

Table 3: Annual Prevalence Ratios for Expanded Diagnosis Clusters (

EDCs) for Non-Elderly and Elderly Beneficiaries .................................................. 4-7

Diagnostic Certainty .............................................................................................. 4-15

MEDC Types ......................................................................................................... 4-15

Table 4: MEDC Types ......................................................................................... 4-15

Important Differences between EDCs and ADGs ................................................. 4-16

Important Differences between EDCs and Episode-of-Care Systems ................... 4-16

Applications of EDCs ............................................................................................ 4-17


4-ii Expanded Diagnosis Clusters (EDCs TM )



Expanded Diagnosis Clusters (EDCs TM ) 4-1

Overview

The Johns Hopkins Expanded Diagnosis Clusters (EDCs) TM complement the unique person-oriented approach that underpins the ACG ® System.

EDCs are a tool for easily identifying people with specific diseases or symptoms

so that the organization does not have to create its own algorithms. The EDC methodology assigns International

Classifications of Diseases (ICD) codes found in claims or encounter data to one of 269

EDCs, which are further organized into 27 categories called Major Expanded Diagnosis

Clusters (MEDCs ). As broad groupings of diagnosis codes,

EDCs help to remove differences in coding behavior between practitioners

.



Example :

There are 41 ICD10 codes that practitioners can record as a diagnosis for otitis media. The EDC for otitis media combines these codes into a single rubric, allowing the healthcare analyst to easily summarize information on this condition.

As a stand-alone tool,

EDCs can be used to select patients with a specific condition or combination of conditions and can also be used to compare the distribution of conditions in one population with another. When combined with ACGs, the result is a powerful combination tool for demonstrating variability of cost within disease categories

This is useful for many profiling applications and can help to target individuals for case management purposes.

Development of EDCs

EDCs are built on the methodology developed by Ronald Schneeweiss and colleagues in

1983.

1 In Schneeweiss’s original work, diagnosis codes were classified according to clinical criteria. Schneeweiss’s original 92 categories, called Diagnosis Clusters, underwent considerable updating and extensive expansions based on significant additional development at Johns Hopkins University, led by Dr. Christopher Forrest. In

2000, Dr. Forrest’s team of generalist and specialist physicians used their clinical judgment to assign the most commonly used ICD-9 codes to EDCs . In all, the team assigned approximately 9,400 diagnosis codes to 190 EDCs.

The development team reviewed the Clinical Classification for Health Policy Research produced by the Agency for Health Care Policy and Research, 2 another disease-oriented grouping methodology, to identify additional conditions that were excluded from

Schneeweiss’s original taxonomy. Other modifications made to the original Schneeweiss clusters included deletion of some clusters, expansion of the content of other clusters by

1 Schneeweiss R, Rosenblatt RA, Cherkin DC, Kirkwood R, Hart G. Diagnosis Clusters: Tool for

Analyzing the Content of Ambulatory Medical Care. Med Care 1983; 21:105-122.

2 Elixhauser A, Andrews RM, Fox S. (1993)

Clinical classifications for health policy research: Discharge statistics by principal diagnosis and procedure

(AHCPR Publication No. 93-0043).


4-2 Expanded Diagnosis Clusters (EDCs TM ) updating the range of ICD codes, improving the clinical homogeneity of certain clusters, and splitting existing clusters into new categories.

In addition, each of the EDCs was classified into one of 27 broad clinical categories, termed a Major EDC (MEDC ). For example, three EDCs --allergic reactions, asthma, and allergic rhinitis--all fall within the Allergy MEDC classification.

The original assignments of EDCs to MEDCs were made based on the assessment of our clinician panel as to the physician specialty most likely to provide care for the class of conditions, when other than primary care services were received.



Example :

Prostatic hypertrophy (GUR04) was mapped to the genito-urinary MEDC and gout (RHU02) to the rheumatologic MEDC .

Further review of data and actual specialist use within each EDC category led to changes in the final assignment of EDCs to their respective MEDC. It should be noted that for many EDCs , the most common service provider is the primary care clinician, and not a specialist.

Since the initial development of the EDC typology in 2000, the EDC categories have undergone several revisions and expansions so that now all ICD (-9-CM, 10- and -10-CM version) codes are assigned. There are 269 EDCs in the current version of the ACG

System.

Recently, the ACG developers added several new common pediatric conditions (many of which also occur during adulthood) and added new categories to broaden the spectrum of signs and symptoms covered (e.g., gastrointestinal signs and symptoms, ophthalmic signs and symptoms, musculoskeletal signs and symptoms). For example, cancers were divided into 17 groups: 15 site-specific cancers, and low impact and high impact cancers

(based on the expected costs of treatment). Several EDC conditions were refined to support the pharmacy adherence methodology and identify conditions requiring chronic medication therapy.



Understanding How EDCs Work

Each ICD code maps to a single EDC. ICD codes within an EDC share similar clinical characteristics and are expected to evoke similar types of diagnostic and therapeutic responses.

There are 67 ICD-9-CM codes and 41 ICD-10 codes (shown in

Table 1

:

ICD-10 Codes

Assigned to Otitis Media EDC (EAR01)

below) that practitioners can record as a diagnosis for otitis media. The EDC for otitis media combines these codes into a single rubric, which lessens the impact of physicians’ coding styles on the analyses.

Table 1: ICD-9-CM and ICD-10 Codes Assigned to Otitis Media EDC

(EAR01)

ICD-9-CM Codes Description

055.2 Post-measles otitis media

381 Non-suppurative otitis media and Eustachian tube disorders

381.0 Acute non-suppurative otitis media

381.00 Unspecified acute non-suppurative otitis media

381.01 Acute serous otitis media

381.02 Acute mucoid otitis media

381.03 Acute sanguinous otitis media

381.04 Acute allergic serous otitis media

381.05 Acute allergic mucoid otitis media

381.06 Acute allergic sanguinous otitis media

381.1 Chronic serous otitis media

381.10 Simple or unspecified chronic serous otitis media

381.19 Other chronic serous otitis media

381.2 Chronic mucoid otitis media

381.20 Simple or unspecified chronic mucoid otitis media

381.29 Other chronic mucoid otitis media

381.3 Other and unspecified chronic non-suppurative otitis media

Non-suppurative otitis media, not specified as acute or

381.4 chronic

381.5 Eustachian salpingitis

381.50 Unspecified Eustachian salpingitis

381.51 Acute Eustachian salpingitis

381.52 Chronic Eustachian salpingitis

381.6 Obstruction of Eustachian tube

381.60 Unspecified obstruction of Eustachian tube

381.61 Osseous obstruction of Eustachian tube

381.62 Intrinsic cartilagenous obstruction of Eustachian tube

381.63 Extrinsic cartilagenous obstruction of Eustachian tube

381.7 Patulous Eustachian tube

381.8 Other disorders of Eustachian tube

381.81 Dysfunction of Eustachian tube


4-4 Expanded Diagnosis Clusters (EDCs TM )

ICD-9-CM Codes Description

381.89 Other disorders of Eustachian tube

381.9 Unspecified Eustachian tube disorder

382 Suppurative and unspecified otitis media

382.0 Acute suppurative otitis media

Acute suppurative otitis media without spontaneous rupture

382.00

382.01 of eardrum

Acute suppurative otitis media with spontaneous rupture of eardrum

Acute suppurative otitis media in diseases classified

382.02 elsewhere

382.1 Chronic tubotympanic suppurative otitis media

382.2 Chronic atticoantral suppurative otitis media

382.3 Unspecified chronic suppurative otitis media

382.4 Unspecified suppurative otitis media

382.9 Unspecified otitis media

384 Other disorders of tympanic membrane

384.0 Acute myringitis without mention of otitis media

384.00 Unspecified acute myringitis

384.01 Bullous myringitis

384.09 Other acute myringitis without mention of otitis media

384.1 Chronic myringitis without mention of otitis media

384.2 Perforation of tympanic membrane

384.20 Unspecified perforation of tympanic membrane

384.21 Central perforation of tympanic membrane

384.22 Attic perforation of tympanic membrane

384.23 Other marginal perforation of tympanic membrane

384.24 Multiple perforations of tympanic membrane

384.25 Total perforation of tympanic membrane

384.8 Other specified disorders of tympanic membrane

384.81 Atrophic flaccid tympanic membrane

384.82 Atrophic non-flaccid tympanic membrane

384.9 Unspecified disorder of tympanic membrane

388.6 Otorrhea

388.60 Unspecified otorrhea

388.69 Other otorrhea

388.7 Otalgia

388.70 Unspecified otalgia

388.71 Otogenic pain

388.72 Referred otogenic pain

388.9 Unspecified disorder of ear


Expanded Diagnosis Clusters (EDCs TM )

ICD-10 Codes Description

B05.3 Post-measles otitis media

H65 Non-suppurative otitis media and Eustachian tube disorders

H65.0 Acute serous otitis media

H65.1 Other acute non-suppurative otitis media

H65.2 Chronic serous otitis media

H65.3 Chronic mucoid otitis media

H65.4 Other and unspecified chronic non-suppurative otitis media

H65.9 Non-suppurative otitis media, not specified as acute or chronic

H66 Suppurative and unspecified otitis media

H66.0 Acute suppurative otitis media

H66.1 Chronic tubotympanic suppurative otitis media

H66.2 Chronic atticoantral suppurative otitis media

H66.3 Other chronic suppurative otitis media

H66.4 Suppurative otitis media, unspecified

H66.9 Otitis media, unspecified

H67 Otitis media in diseases classified elsewhere

H67.0 Otitis media in bacterial diseases classified elsewhere

H67.1 Otitis media in viral diseases classified elsewhere

H67.8 Otitis media in other diseases classified elsewhere

H68 Eustachian salpingitis and obstruction

H68.0 Eustachian salpingitis

H68.1 Obstruction of Eustachian tube

H69 Other disorders of Eustachian tube

H69.0 Patulous Eustachian tube

H69.8 Other disorders of Eustachian tube

H69.9 Unspecified Eustachian tube disorder

H72 Perforation of tympanic membrane

H72.0 Central perforation of tympanic membrane

H72.1 Attic perforation of tympanic membrane

H72.2 Other marginal perforation of tympanic membrane

H72.8 Total or Multiple perforations of tympanic membrane

H72.9 Unspecified perforation of tympanic membrane

H73 Other disorders of tympanic membrane

H73.0 Acute myringitis

H73.1 Chronic myringitis

H73.8 Other specified disorders of tympanic membrane

H73.9 Unspecified disorder of tympanic membrane

H92 Otalgia and effusion of ear

H92.0 Otalgia

H92.1 Otorrhea

H92.2 Otorrhagia


4-5



The EDC s related to Diabetes receives special treatment in the EDC assignment process.

Each Diabetes diagnosis is associated with either EDC END06 Type 2 Diabetes without complication or EDC END08 Type 1 Diabetes without complication. The EDC assignment process then looks for the presence of a potential complication diagnosis code. If a complication is found (see

Table 2: Examples of Diabetes Complications below), a patient with END06 will be changed to END07 while a patient with END08 will be changed to END09. The implication of this method is that a patient cannot be assigned to a diabetes condition with and without complications simultaneously.

Table 2: Examples of Diabetes Complications

ICD-9-CM Description

250.1 Diabetes with ketoacidosis

250.2 Diabetes with hyperosmolar coma

250.3 Diabetes with coma NEC

250.4 Diabetes with renal manifestation

410 Acute myocardial infarction

411 Other acute ischemic heart disease

412 Old myocardial infarction

413 Angina pectoris

414 Other chronic ischemic heart disease

581 Nephrotic syndrome

582 Chronic nephritis

583 Nephritis NOS

584 Acute renal failure

585 Chronic renal failure

586 Renal failure NOS

V56 Dialysis encounter

ICD-10 Description

E10(.0-.8) Insulin-dependent diabetes mellitus with complications

E11(.0-.8) Non-insulin-dependent diabetes mellitus with complications

E12(.0-.8) Malnutrition-related diabetes mellitus with complications

E13(.0-.8) Other specified diabetes mellitus with complications

E14(.0-.8) Unspecified diabetes mellitus with complications

I20 Angina Pectoris

I21 Acute Myocardial Infarction

I22 Subsequent Myocardial Infarction

I23 Complications following acute myocardial infarction

I24 Other acute ischaemic heart diseases

I25 Chronic ischaemic heart disease

I51 Complications of heart disease

N00 Acute nephritic syndrome

N01 Rapidly progressive nephritic syndrome

N02 Recurrent and persistent haematuria

N03 Chronic nephritic syndrome

N04 Nephrotic syndrome

N05 Unspecified nephritic syndrome

N17 Acute renal failure

N18 Chronic renal failure



ICD-10 Description

N19 Unspecified renal failure

N28 Other disorders of kidney

Z49 Care involving dialysis

Also, patients will not be assigned to a Type 1 Diabetes EDC and a Type 2 Diabetes EDC concurrently. If the patient has diagnoses indicating Type 1 Diabetes and Type 2

Diabetes, the EDC assignment will reflect Type 1 Diabetes only.

Table 3: Annual Prevalence Ratios for Expanded Diagnosis Clusters

(EDCs) for Non-Elderly and Elderly Beneficiaries

Number of Persons with EDC per 1,000

Enrollees

Expanded Diagnosis Cluster (EDC)

Non-Elderly

(0 to 64 Years)

Elderly


Administrative

Surgical aftercare

Transplant status

Administrative concerns and non-specific laboratory abnormalities

Preventive care

35.53

0.88

141.94

448.76

213.06

1.79

416.81

566.66

Allergy

Allergic reactions

Allergic rhinitis

Asthma, w/o status asthmaticus

Asthma, with status asthmaticus

Disorders of the immune system

Cardiovascular

Cardiovascular signs and symptoms

Ischemic heart disease (excluding acute myocardial infarction)

Congenital heart disease

Congestive heart failure

Cardiac valve disorders

Cardiomyopathy

Heart murmur

Cardiac arrhythmia

Generalized atherosclerosis

Disorders of lipid metabolism

Acute myocardial infarction

Cardiac arrest, shock

Hypertension, w/o major complications

16.41

3.11

2.96

7.78

2.59

4.12

12.93

4.94

115.28

1.26

0.37

117.55

17.09

81.18

41.92

4.13

2.26

37.57

13.37

69.17

47.33

5.43

9.01

125.40

172.14

7.44

62.27

57.97

21.65

11.11

141.27

66.31

440.60

11.67

2.65

543.41




Hypertension, with major complications

Cardiovascular disorders, other

Dental

Disorders of mouth

Disorders of teeth

Gingivitis

Stomatitis

Ear, Nose, Throat

Otitis media

Tinnitus

Temporomandibular joint disease

Foreign body in ears, nose, or throat

Deviated nasal septum

Otitis externa

Wax in ear

Deafness, hearing loss

Chronic pharyngitis and tonsillitis

Epistaxis

Acute upper respiratory tract infection

ENT disorders, other

Endocrine

Osteoporosis

Short stature

Hypothyroidism

Other endocrine disorders

Type 2 diabetes, w/o complication

Type 2 diabetes, w/ complication

Type 1 diabetes, w/o complication

Type 1 diabetes, w/ complication

Eye

Ophthalmic signs and symptoms

Blindness

Retinal disorders (excluding diabetic retinopathy)

Disorders of the eyelid and lacrimal duct

Refractive errors

Cataract, aphakia

Conjunctivitis, keratitis

Glaucoma

Infections of eyelid

Foreign body in eye

Strabismus, amblyopia


Enrollees

Non-Elderly

(0 to 64 Years)

10.80

5.62

Elderly


67.36

36.02

73.53

3.53

2.78

1.45

5.49

12.80

14.20

10.81

6.71

3.69

180.88

15.09

7.22

9.13

0.70

2.23

42.65

0.85

7.83

14.62

73.53

15.05

42.73

15.89

9.44

2.26

6.64

8.46

0.82

30.49

27.34

25.44

9.60

2.94

3.58

11.10

2.94

0.76

2.40

31.32

9.53

2.84

1.49

6.16

12.30

53.73

45.84

1.41

9.95

82.85

24.10

88.30

4.51

62.77

70.70

159.42

260.14

45.44

119.88

26.53

2.90

8.35

89.80

0.02

112.14

49.86

90.68

88.05

5.06

17.81




Traumatic injuries of eye

Diabetic retinopathy

Eye, other disorders

Age-related macular degeneration

Female Reproductive

Pregnancy and delivery, uncomplicated

Female genital symptoms

Endometriosis

Pregnancy and delivery with complications

Female infertility

Abnormal pap smear

Ovarian cyst

Vaginitis, vulvitis, cervicitis

Menstrual disorders

Contraception

Menopausal symptoms

Utero-vaginal prolapse

Female gynecologic conditions, other

Pregnancy with Termination

Gastrointestinal/Hepatic

Gastrointestinal signs and symptoms

Inflammatory bowel disease

Constipation

Acute hepatitis

Chronic liver disease

Peptic ulcer disease

Gastroenteritis

Gastroesophageal reflux

Irritable bowel syndrome

Diverticular disease of colon

Acute pancreatitis

Chronic pancreatitis

Lactose Intolerance

Gastrointestinal/Hepatic disorders, other

General Signs and Symptoms

Nonspecific signs and symptoms

Chest pain

Fever

Syncope

Nausea, vomiting

Debility and undue fatigue


4-9


Enrollees

Non-Elderly

(0 to 64 Years)

5.20

2.77

17.91

1.24

Elderly


5.76

18.15

75.66

61.88

31.731

3.79

13.41

2.01

5.10

17.64

35.61

46.06

8.36

11.16

1.01

0.62

1.25

8.53

39.38

43.89

27.43

7.69

25.96

47.30

26.96

32.31

23.74

17.31

2.39

4.40

2.84

16.22

22.24

2.48

14.73

3.26

11.56

6.50

85.75

6.94

36.99

2.41

8.93

40.96

51.99

120.76

14.58

64.12

3.12

2.92

3.53

25.14

114.01

106.51

18.05

33.63

36.70

105.95

23.30

5.52

0.16

33.60

12.80

4.63

0.10

0.30

8.09

0.23

0.50

0.07

2.63

2.06


4-10


Lymphadenopathy

Edema

General Surgery

Anorectal conditions

Appendicitis

Benign and unspecified neoplasm

Cholelithiasis, cholecystitis

External abdominal hernias, hydroceles

Chronic cystic disease of the breast

Other breast disorders

Varicose veins of lower extremities

Nonfungal infections of skin and subcutaneous tissue

Abdominal pain

Peripheral vascular disease

Burns--1st degree

Aortic aneurysm

Gastrointestinal obstruction/perforation

Genetic

Chromosomal anomalies

Inherited metabolic disorders

Genito-urinary

Vesicoureteral reflux

Undescended testes

Hypospadias, other penile anomalies

Prostatic hypertrophy

Stricture of urethra

Urinary symptoms

Other male genital disease

Urinary tract infections

Renal calculi

Prostatitis

Incontinence

Genito-urinary disorders, other

Hematologic

Other hemolytic anemias

Iron deficiency, other deficiency anemias

Thrombophlebitis

Neonatal jaundice

Aplastic anemia

Deep vein thrombosis




Enrollees

Non-Elderly

(0 to 64 Years)

7.14

9.37

Elderly


7.28

59.66

20.66

1.66

52.38

5.34

6.62

9.02

23.60

3.99

47.24

1.06

131.94

13.47

18.17

10.47

40.80

14.72

0.73

0.30

0.53

9.84

0.83

37.39

13.97

37.35

8.45

3.23

6.96

6.97

0.59

19.66

1.27

2.17

0.51

2.94

36.14

65.07

1.81

0.45

1.09

4.33

0.48

5.20

0.45

0.03

0.10

85.44

4.11

110.26

29.88

92.00

14.79

7.81

34.68

49.07

1.34

101.23

5.07

0.04

3.25

17.14

64.10

95.99

30.79

0.34

17.21

19.82

0.13

15.98




Hemophilia, coagulation disorder

Hematologic disorders, other

Sickle Cell Disease

Infections

Tuberculosis infection

Fungal infections

Infectious mononucleosis

HIV, AIDS

Sexually transmitted diseases

Viral syndromes

Lyme disease

Septicemia

Infections, other

Malignancies

Malignant neoplasms of the skin

Low impact malignant neoplasms

High impact malignant neoplasms

Malignant neoplasms, breast

Malignant neoplasms, cervix, uterus

Malignant neoplasms, ovary

Malignant neoplasms, esophagus

Malignant neoplasms, kidney

Malignant neoplasms, liver and biliary tract

Malignant neoplasms, lung

Malignant neoplasms, lymphomas

Malignant neoplasms, colorectal

Malignant neoplasms, pancreas

Malignant neoplasms, prostate

Malignant neoplasms, stomach

Acute leukemia

Malignant neoplasms, bladder

Musculoskeletal

Musculoskeletal signs and symptoms

Acute sprains and strains

Degenerative joint disease

Fractures (excluding digits)

Torticollis

Kyphoscoliosis

Congenital hip dislocation

Fractures and dislocations/digits only

Joint disorders, trauma related


4-11


Enrollees

Non-Elderly

(0 to 64 Years)

1.46

3.67

0.14

Elderly


9.30

17.70

0.05

0.09

0.44

0.15

0.53

1.33

1.01

0.12

1.93

6.33

3.30

2.57

4.83

1.13

0.41

0.10

0.26

0.44

0.22

4.71

1.81

1.03

7.15

39.67

1.24

1.55

6.10

134.13

75.24

28.46

19.11

1.46

4.14

0.17

4.89

33.23

0.83

3.36

0.88

7.31

7.26

9.96

1.11

37.59

55.20

15.75

15.38

25.26

2.83

1.68

0.82

0.54

8.30

0.44

9.28

0.09

0.39

4.45

12.39

0.89

9.23

13.46

292.47

72.81

173.13

40.50

1.11

7.49

0.03

4.48

36.61


4-12


Fracture of neck of femur (hip)

Congenital anomalies of limbs, hands, and feet

Acquired foot deformities

Cervical pain syndromes

Low back pain

Bursitis, synovitis, tenosynovitis

Amputation status

Musculoskeletal disorders, other

Neonatal

Newborn Status, Uncomplicated

Newborn Status, Complicated

Low Birth Weight

Prematurity

Disorders of Newborn Period

Neurologic

Neurologic signs and symptoms

Headaches

Peripheral neuropathy, neuritis

Vertiginous syndromes

Cerebrovascular disease

Parkinson's disease

Seizure disorder

Multiple sclerosis

Muscular dystrophy

Sleep problems

Dementia and delirium

Quadriplegia and paraplegia

Head injury

Spinal cord injury/disorders

Paralytic syndromes, other

Cerebral palsy

Developmental disorder

Central nervous system infections

Neurologic disorders, other

Migraine Headaches

Nutrition

Failure to thrive

Nutritional deficiencies

Obesity

Nutritional disorders, other

Psychosocial




Enrollees

Non-Elderly

(0 to 64 Years)

0.30

3.20

11.83

65.92

117.59

52.31

0.42

42.04

Elderly


7.17

3.12

41.28

79.60

206.79

107.45

1.73

128.29

7.05

0.73

0.44

0.82

1.46

0.05

0.19

0.01

0.02

0.29

12.74

41.05

35.15

20.31

6.91

0.47

6.46

1.96

0.43

21.93

1.77

0.37

7.06

1.51

0.85

0.51

4.58

0.77

6.43

19.18

2.94

8.51

24.42

0.06

66.60

35.77

88.66

69.76

87.02

12.00

11.98

2.16

1.39

38.06

48.12

0.89

11.41

4.38

7.53

0.17

0.66

2.02

20.73

8.86

3.73

38.07

26.48

0.16




Anxiety, neuroses

Substance use

Tobacco use

Behavior problems

Attention deficit disorder

Family and social problems

Schizophrenia and affective psychosis

Personality disorders

Depression

Psychologic signs and symptoms

Psychosocial disorders, other

Bipolar disorder

Reconstructive

Cleft lip and palate

Lacerations

Chronic ulcer of the skin

Burns--2nd and 3rd degree

Renal

Chronic renal failure

Fluid/electrolyte disturbances

Acute renal failure

Nephritis, nephrosis

Renal disorders, other

Respiratory

Respiratory signs and symptoms

Acute lower respiratory tract infection

Cystic fibrosis

Emphysema, chronic bronchitis, COPD

Cough

Sleep apnea

Sinusitis

Pulmonary embolism

Tracheostomy

Respiratory failure

Respiratory disorders, other

Rheumatologic

Autoimmune and connective tissue diseases

Gout

Arthropathy

Raynaud's syndrome

Rheumatoid arthritis


4-13


Enrollees

Non-Elderly

(0 to 64 Years)

62.02

8.39

21.07

4.81

16.04

2.08

1.41

1.15

29.40

2.38

4.96

6.10

0.19

24.54

2.24

2.20

Elderly


53.93

6.97

19.28

5.46

0.83

1.30

8.34

2.29

63.22

1.51

5.11

4.14

0.02

37.05

22.71

2.06

3.62

13.92

1.44

1.02

2.90

41.73

76.48

0.18

10.05

49.82

16.11

104.33

0.94

0.20

4.61

6.93

6.14

4.95

12.19

0.94

4.01

50.51

71.71

19.24

4.18

10.15

121.79

115.54

0.10

84.34

74.85

31.83

68.24

6.49

0.78

29.50

32.86

19.87

23.46

47.97

2.17

16.09




Skin

Contusions and abrasions

Dermatitis and eczema

Keloid

Acne

Disorders of sebaceous glands

Sebaceous cyst

Viral warts and molluscum contagiosum

Other inflammatory conditions of skin

Exanthems

Skin keratoses

Dermatophytoses

Psoriasis

Disease of hair and hair follicles

Pigmented nevus

Scabies and pediculosis

Diseases of nail

Other skin disorders

Benign neoplasm of skin and subcutaneous tissues

Impetigo

Dermatologic signs and symptoms

Toxic Effects and Adverse Events

Toxic effects of nonmedicinal agents

Adverse effects of medicinal agents

Adverse events from medical/surgical procedures

Complications of mechanical devices


Enrollees

Non-Elderly

(0 to 64 Years)

Elderly


35.97

58.95

1.57

30.46

5.32

11.16

21.83

8.13

30.58

26.09

19.41

4.96

9.92

1.78

1.33

8.20

29.44

49.78

3.66

15.64

3.01

4.54

3.38

3.39

108.56

1.67

30.61

2.72

10.55

11.23

18.26

54.97

87.46

2.01

13.83

11.89

20.24

13.08

19.96

38.43

171.53

78.15

8.75

7.87

2.74

0.63

40.91

65.00

Source: IMS Health Payer Solutions, Watertown, MA; Subset of the Patient-Centric Database containing a national cross-section of managed care plans; population of 3,523,473 commercially insured lives (less than

65 years old) and population of 463,126 Medicare beneficiaries (65 years and older), 2008-09.



Diagnostic Certainty

The recording of provisional diagnosis codes can improperly attribute chronic conditions to a patient subsequently misidentifying individuals for care management programs and elevating cost predictions. The goal of stringent diagnostic certainty is to provide greater certainty of a given diagnosis for subset of chronic conditions.

When the lenient diagnostic certainty option is applied, any single diagnosis qualifying for an EDC marker will turn the marker on. However, the stringent diagnostic certainty option can also be applied. For a subset of diagnoses, there must be more than one diagnosis qualifying for the marker in order for the EDC to be assigned. This was designed to provide greater confidence in the EDC conditions assigned to a patient.

For several conditions, namely Hypertension, Asthma, Diabetes, and Pregnancy, there are multiple EDCs to describe the condition. When applying stringent diagnostic certainty, if multiple diagnoses are required, they must qualify for the same EDC. For example, a single diagnosis for Type 1 Diabetes and a single diagnosis for Type 2 Diabetes will not turn on any diabetes-related EDC TM , but 2 diagnoses for Type 2 Diabetes will turn on

EDC END06. For more information about the inclusion and exclusion of diagnoses, refer to the

Installation and Usage Guide, Chapter 4.

MEDC Types

To assist analysts, the 27 Major EDCs may be further aggregated into five MEDC Types.

Specifically, the categorization is presented in

Table 4: MEDC Types

.

Table 4: MEDC Types

MEDC Types

1.

Administrative

2.

Medical

3.

Surgical

4.

Obstetric/Gynecologic

5.

Psychosocial

MEDC Categorizations

Administrative

Allergy, Cardiovascular, Endocrine, Gastrointestinal/Hepatic, General

Signs and Symptoms, Genetic, Hematologic, Infections, Malignancies,

Neonatal, Neurologic, Nutrition, Renal, Respiratory, Rheumatologic,

Skin, Toxic Effects and Adverse Events

Dental, Ear, Nose, and Throat, Eye, General Surgery, Genito-urinary,

Musculoskeletal, Reconstructive

Female reproductive

Psychosocial

In summary, EDCs can be examined at three levels. The broadest level is the MEDC

Type and includes five categories. MEDC Types are based on 27 clinically oriented groupings of MEDCs . The full EDC taxonomy is composed of 269 groups.



Important Differences between EDCs and ADGs

Both EDCs and Aggregated Diagnosis Groups (ADGs) are aggregations of ICD codes.

However, there is a significant difference in the methodology underlying the grouping of

ICD codes--ADGs, the building block of the ACG System, are groups of diagnoses with similar expected healthcare need

, while EDCs are clinically similar

clusters. When applying the EDCs , it is important to recognize that the criteria used to assign ICD codes are quite different from the criteria used to determine the appropriate ADG. The main criterion used for the ICD-to-EDC assignment is diagnostic similarity, whereas the ICDto-ADG assignments (and the overall ACG System in general) are based on a unique set of clinical criteria that capture various dimensions of the range and severity of an individual’s co-morbidity. This subject is discussed more detail in the chapter entitled,

“Adjusted Clinical Groups.”

In brief, the key dimensions of the ICD to ADG assignment process are:

• Expected duration of illness (e.g., acute, chronic, or recurrent)

•

•

•

Severity (e.g., expected prognosis with respect to disability or longevity)

Diagnostic certainty (i.e., signs/symptoms versus diagnoses)

Etiology (e.g., infectious, neoplastic, psychosocial conditions)

• Expected need for specialist care or hospitalization

In the ADG assignment process, ICD codes representing multiple diseases and conditions may be assigned to the same ADG. This occurs when the diseases are expected to have a similar impact on the need for healthcare resources. In contrast, the EDC assignment does not account for differences in disease severity, chronicity, or the expected need for resources.

Because of the marked differences between the conceptual underpinnings of the EDC and the ADG / ACG framework, each ADG will usually contain ICD codes that map into more than one EDC . (The notable exception is the asthma ADG and asthma EDC, which contain the same ICD codes). Likewise, a single EDC may map into multiple ADGs.

Important Differences between EDCs and Episode-of-Care Systems

It is important to recognize that while the EDC tool is effective for identifying persons with specific conditions, the current configuration of the EDC tool is not an episode-ofcare grouping methodology (i.e., while EDCs can be used to identify persons under treatment for otitis media (or any select condition), there is no count of the number of occurrences otitis media infections during the analysis period). While episode-of-care tools also consider diagnosis codes, their main purpose is to group together services, procedures, drugs, and tests that are provided to a patient in the course of management of a specific disease or condition. Currently, the EDC assignment is based only on diagnosis codes. In the future, as the software’s input stream becomes more sophisticated and date-of-service and/or other data elements become available, the functionality of the

EDC module may be increased.



Sophisticated analysts wishing to process data of shorter durations than the typical 12 month time period could, for example, process data monthly to create monthly EDC indicators. Summing these indicators for each patient by each EDC could provide rough approximations of the number of times a patient received services related to specific

EDCs and proxy clinical treatment groups could be envisioned.

Applications of EDCs

EDCs have many applications, particularly in areas of profiling and disease/case management. EDCs can be used to perform these functions:

1.

Describe the prevalence of specific diseases within a single population

2.

Compare disease distributions across two or more populations

3.

Aid disease management/case management processes by identifying individual patients by condition and displaying a patient condition profile

Each of these applications is highlighted within the


chapters for

Health Status Monitoring and Clinical Screening by Care and Disease Managers.





Medication Defined Morbidity 5-i

5 Medication Defined Morbidity

Objectives ....................................................................................................... 5-1

National Drug Code System ......................................................................... 5-1

Anatomical Therapeutic Chemical (ATC) System..................................... 5-2

Table 1: ATC Drug Classification Levels ................................................. 5-2

Healthcare Common Procedure Coding System (HCPCS) ...................... 5-3

Rx-Defined Morbidity Groups ..................................................................... 5-3

Table 2: Counts of Associated Therapeutic Classes per Rx-Defined

Morbidity Groups ....................................................................................... 5-4

Table 3: Counts of Associated Second Level ATCs per Rx-Defined


Table 4: Counts of Associated Third Level ATCs per Rx-Defined


Table 5: Major Rx-MG Categories ........................................................... 5-7

NDC-to-Rx-MG Assignment Methodology ................................................ 5-9

ATC-to-Rx-MG Assignment Methodology ................................................. 5-9

Table 6: Number of Pharmacy Codes for Each Rx-MG ......................... 5-10

Table 7: Rx-MG Morbidity Taxonomy and Clinical Characteristics of Medications Assigned to Each Rx-MG .............................................. 5-14

Table 8: Annual Prevalence Ratios for Rx-MGs for Non-Elderly and Elderly Beneficiaries ......................................................................... 5-20

Active Ingredient Count Variable ............................................................. 5-23

Conclusion .................................................................................................... 5-23


5-ii Medication Defined Morbidity



Medication Defined Morbidity 5-1

Objectives

This chapter is intended to describe the Pharmacy (RX)-based Morbidity Markers

(Groups), Rx-MGs TM and the clinical criteria used to assign medications to morbidity groups. The Rx-MGs provide further methods to describe the unique morbidity profile of a population and form the basis of the pharmacy-based predictive model, Rx-PM.

Specifics of the predictive model are discussed in greater detail in later chapters discussing Predicting Resource Use and Predicting Hospitalization.

National Drug Code System

The National Drug Code (NDC) system serves as a universal product identifier for human drugs. It is maintained by the Food and Drug Administration (FDA) of the US government. An NDC identifies the following attributes of a medication: generic name/active ingredient; manufacturer; strength; route of administration; package size; and, trade name. The same generic drug generally has many NDC codes, because different manufacturers may produce it, it may come in several strengths, package sizes can vary, etc. Not all the NDC attributes are useful for defining the type of morbidity that the drug is intended to influence. Manufacturer and trade name provide no clinical information, whereas the generic name of the drug gives essential information on its chemical structure and known mechanism(s) of action. The same generic drug can be given via different routes (e.g., by mouth, inhaled, topical). In many cases, the route of administration is critical for identifying the specific morbidity managed. A good example of this is the drug class of corticosteroids, which are powerful anti-inflammatory medications. Oral corticosteroids can be used to manage pulmonary disease that result from airway inflammation, such as asthma, whereas topical forms of the medication are used to manage inflammatory skin conditions, such as eczema. Although medication strength may provide some indication of disease stability, it may also be suggestive of patient non-adherence to medication regimens, inappropriate over-treatment, patient idiosyncratic response, or size of the patient. For these reasons, we elected to ignore strength as a classification criterion, and restricted the assignment process to unique generic drug-route of administration combinations. In so doing, we reduced the nearly

90,000 NDCs to approximately 2,700 units. Each generic drug-route of administration has a set of NDCs associated with it.


5-2 Medication Defined Morbidity

Anatomical Therapeutic Chemical (ATC) System

The Anatomical Therapeutic Chemical (ATC) System serves as the World Health

Organization (WHO) standard for drug consumption studies. It is maintained by the

WHO Collaborating Centre for Drug Statistics Methodology.

In the Anatomical Therapeutic Chemical (ATC) classification system, drugs are divided into different groups according to the organ or system on which they act in addition to their chemical, pharmacological, and therapeutic properties. Drugs are classified into five different group levels. The drugs are divided into fourteen main groups (first level), with one pharmacological / therapeutic subgroup (second level). The third and fourth levels are chemical / pharmacological/therapeutic subgroups and the fifth level is the chemical substance. The second, third, and fourth levels are often used to identify pharmacological subgroups when that is considered more appropriate than therapeutic or chemical subgroups.

The complete classification of Metformin illustrates the structure of the code as shown in

Table 1: ATC Drug Classification Levels

.

Table 1: ATC Drug Classification Levels

Level Description

A

A10

A10B

A10BA

Alimentary tract and metabolism

(1st level, anatomical main group)

Drugs used in diabetes

(2nd level, therapeutic subgroup)

Blood glucose lowering drugs, excluding insulins

(3rd level, pharmacological subgroup)

Biguanides

(4th level, chemical subgroup)

Metformin

(5th level, chemical substance) A10BA02

Therefore, in the ATC system, all plain Metformin preparations are given the code

A10BA02.



ATCs are classified based upon the therapeutic use of the main active ingredient. Unlike

NDCs, the ATC classification has been specifically designed to capture therapeutic use.

In fact, the same generic substance can be given more than one ATC code if it is available in two or more strengths or formulations with clearly different therapeutic uses.

However, an international classification system has the challenges of capturing countryspecific, main therapeutic use of a drug that often results in several different classification alternatives. As a general guideline, the ATC system has attempted to assign such drugs to one code, the main indication being decided on the basis of the available literature.

Healthcare Common Procedure Coding System (HCPCS)

The Healthcare Common Procedure Coding System (HCPCS) is maintained by the U.S.

Centers for Medicare and Medicaid to describe items and services provided in healthcare delivery. The services that it captures include drugs administered by injection in physician’s offices and outpatient clinics. Most, but not all, of the HCPCS codes that are pertinent to pharmacy fall into the J series of these codes. Given the potential importance of these pharmaceuticals, they have been mapped to the ACG System beginning with

Version 10.0.

Rx-Defined Morbidity Groups

Rx-Defined Morbidity Groups (Rx-MGs) represent the building blocks of a pharmacybased predictive model, Rx-PM. Each generic drug / route of administration combination is assigned to a single Rx-MG. We found that generic drug / route of administration combinations within therapeutic classes were sometimes logically assigned to different

Rx-MGs, which further reinforced the need to make assignments at the individual drug level.

Table 2: Counts of Associated Therapeutic Classes per Rx-Defined Morbidity

Groups

shows the number of therapeutic classes represented within Rx-MGs. Just six

Rx-MGs included a single therapeutic class, and about a third of all Rx-MGs included medications from five or more therapeutic classes.



Table 2: Counts of Associated Therapeutic Classes per Rx-Defined

Morbidity Groups

Number of Therapeutic

Classes Represented in an

Rx-MG

Number of

Rx-MG Percent

Cumulative

Frequency

Cumulative

Percent

11

12

14

15

6

7

8

9

10

29

1

2

3

4

5

1

1

2

1

2

1

4

2

1

6

14

7

12

4

6

9.38

21.88

10.94

18.75

6.25

9.38

6.25

3.13

3.13

1.56

1.56

3.13

1.56

1.56

1.56

61

62

63

64

53

55

57

58

59

6

20

27

39

43

49

Although the Rx-MGs generally included medications from more than one therapeutic class, the majority of therapeutic classes were assigned to a single Rx-MG or two Rx-

MGs. Specifically, 50% of therapeutic classes were assigned to a single Rx-MG, 28% had medications grouped to two Rx-MGs, and 22% had medications assigned to three or more Rx-MGs.

9.38

31.25

42.19

60.94

67.19

76.56

82.81

85.94

89.06

90.63

92.19

95.31

96.88

98.44

100.00



Table 3: Counts of Associated Second Level ATCs per Rx-Defined Morbidity

Groups

shows the number of second level (therapeutic subgroup) ATC codes represented within Rx-MGs. A little over half of the all Rx-MGs included medications from five or more second level codes.

Table 3: Counts of Associated Second Level ATCs per Rx-Defined


Number of Second Level

ATCs Represented in an

Rx-MG



7

8

9

11

12

33

1

2

3

4

5

6

Number of

Rx-MGs



Percent

3

1

6

1

15

10

11

7

1

1

3

1

25.00

16.67

18.33

11.67

10.00

1.67

5.00

1.67

5.00

1.67

1.67

1.67

Cumulative

Frequency

49

50

53

54

15

25

36

43

57

58

59

60

Cumulative

Percent

25.00

41.67

60.00

71.67

81.67

83.33

88.33

90.00

95.00

96.67

98.33

100.00



Table 4: Counts of Associated Third Level ATCs per Rx-Defined Morbidity

Groups

shows the number of third level (pharmacological subgroup) ATC codes represented within Rx-MGs. Half of the all Rx-MGs included medications from nine or more third level codes.

Table 4: Counts of Associated Third Level ATCs per Rx-Defined


Number of 3rd Level

ATCs Represented in an

Rx-MG

12

14

15

19

6

7

8

9

1

2

3

4

5

22

26

67

Number of

Rx-MGs Percent

1

1

2

3

1

1

3

1

1

1

10

8

10

3

5

9

16.67

13.33

8.33

15.00

16.67

5.00

1.67

1.67

5.00

1.67

3.33

5.00

1.67

1.67

1.67

1.67

Cumulative

Frequency

53

56

57

58

46

47

50

51

59

60

10

18

23

32

42

45

The specific clinical criteria used to assign medications to an Rx-MG category were the primary anatomico-physiological system, morbidity differentiation, expected duration, and severity of the morbidity type being targeted by the medication. These four clinical dimensions not only characterize medications by morbidity type, they also have major consequences for predictive modeling. Higher levels of differentiation, chronicity, and greater severity would all be expected to increase resource use. Each of these criteria is discussed below.

Primary Anatomico-Physiological System

.

The Rx-MG classification system is organized into 19 Major Rx-MG categories: 16 anatomico-physiological groupings; one general signs and symptoms category; one toxic effects / adverse events group; and one other and non-specific medications category.

Table 5: Major Rx-MG Categories

lists these categories. Medications assigned to the non-specific category are not reflective of any underlying morbidity type; examples of these medication types are diagnostic agents and multi-vitamins.

Cumulative

Percent

16.67

30.00

38.33

53.33

70.00

75.00

76.67

78.33

83.33

85.00

88.33

93.33

95.00

96.67

98.33

100.00


Medication Defined Morbidity

Table 5: Major Rx-MG Categories

Allergy/Immunology

Cardiovascular

Ears-Nose-Throat

Endocrine

Eye

Female Reproductive

Gastrointestinal/Hepatic

General Signs and Symptoms

Genito-Urinary

Hematologic

Infections

Malignancies

Musculoskeletal

Neurologic

Psychosocial

Respiratory

Skin

Toxic effects/adverse events

Other and non-specific medications

5-7



Morbidity Differentiation.

Very few medications are used for a single disease. Even insulin treatment, which is employed in the management of diabetes, does not separate patients into Type 1 and Type 2 diabetes, because it can be used to manage either form.

A single medication is often administered for multiple clinical indications; thus, a medication classification system that is solely disease-based will not validly reflect patient morbidity. Rx-MGs assign medications to a specific disease when there is a logical 1-to-1 assignment (e.g., digoxin  congestive heart failure). However, most medications are assigned to broader morbidity groups, which are reflective of the patient’s underlying pathophysiology. Furthermore, a large amount of medication treatment is for physically (e.g., pain medications) and emotionally (e.g., anxiolytics) experienced body sensations. These symptoms are undifferentiated morbidities that generally require palliative therapy. In contrast, specific medical diseases are more differentiated morbidity types. Because we sought to create a comprehensive classification system of medications, the Rx-MGs include categories for symptoms

(assigned to an acute minor category within an organ system grouping or to the general signs and symptoms category), fully developed diseases that have a 1-to-1 correspondence with medication, and more general morbidity-types.

Expected Duration.

This dimension refers to the expected time period that the morbidity will require treatment and is used to characterize conditions as acute, recurrent, or chronic. An acute condition is a time-limited morbidity that is expected to last less than 12 months. The classic example of acute conditions is infections. Recurrent conditions are episodic health problems. They tend to occur repeatedly, and over time.

Migraine headaches and gout are two examples of recurrent conditions. Each episode is time-limited and, in isolation, could be considered an acute health problem. However, the repetitive occurrences may span several years with asymptomatic inter-current periods; this episodic relapsing nature of the morbidity type is the hallmark feature of recurrent morbidities. Chronic conditions are persistent health states that generally last longer than 12 months.

Severity.

The severity of morbidities refers to somewhat different concepts for acute and recurrent / chronic conditions. For acute problems, morbidity severity is related to the expected impact of the condition on the physiological stability of the patient or the patient’s functional status. Low impact conditions have minimal effects on functioning or physiological stability, whereas high impact problems can have significant effects on the ability of an individual to perform daily activities. For recurrent and chronic conditions, morbidity severity is related to the stability of the problem over several years.

Without adequate treatment, unstable chronic conditions are expected to worsen over time, whereas stable conditions are expected to change less rapidly.



NDC-to-Rx-MG Assignment Methodology

The Rx-MG classification system was developed with an assumption that morbidity categories needed to be broad enough to encompass multiple U.S. Food and Drug

Administration (FDA) approved and common off-label uses of a drug. Each unique generic medication/route of administration combination was assigned to a single Rx-MG according to the clinical criteria described above and the most common evidence-based use of the drug. Both FDA approval and empirical data from the literature were accepted as evidence for these assignments.

A team of Johns Hopkins physicians and pharmacist health professionals made the initial

NDC-to-Rx-MG assignment. Disagreements were reconciled during consensus meetings.

The face validity of the assignments was tested by having another team of physicians and pharmacist health professionals examine the preliminary NDC-to-Rx-MG look-up table.

This second review led to a few modifications, but overall confirmed the validity of the initial assignments.

Most NDC codes representing drugs are assigned to a single Rx-MG, and just 9.8% are mapped to the Non-Specific category. A single NDC may be mapped to two Rx-MGs if the drug is a combination therapy. There are NDC codes representing medical supplies, exclusively over-the counter medications and other non-pharmaceutical items that are not assigned to an Rx-MG. The ACG team updates the assignment of new NDC codes to

Rx-MG regularly.

Table 6: Number of Pharmacy Codes for Each Rx-MG

shows the distribution of the NDC codes assigned to each Rx-MG. The pain, high blood pressure and acute minor infections Rx-MGs have the most NDC codes assigned to them. This is because these categories include some of the active ingredients with the most NDC codes: ibuprofen ranked #1 with 2,295 NDCs; hydrochlorothiazide #2 with 2,069 NDCs; acetaminophen #3 with 3,776 NDCs; amoxicillin #5 with 1,238 NDCs; and, erythromycin #7 with 1,085 NDCs.

ATC-to-Rx-MG Assignment Methodology

The methodology for assigning ATC-to-Rx-MGs is largely the same as that used in making NDC-to Rx-MGs assignments. However, there were four important differences as summarized below:

1.

Larger number of codes assigned

.

In the NDC system, the 100,000 codes can be collapsed to roughly 2,700 unique drug route combinations which then get assigned to an Rx-MG. The ATC assignment involved assigning close to 5,100 ATC codes

(900 4thLevel and nearly 4,200 5th Level ATC codes) to an Rx-MG.

2.

International variability

.

Incorporating international variability and off label use was also considered. Literature was extensively reviewed for evidence of alternate and prevalent international use of medications. Whenever evidence showed that the predominant use of the medication was different from the FDA approved use, the Rx-

MG assignment was modified to reflect this.



3.

Route of administration was not available for some ATC codes

.

Route is not provided in the ATC dictionary for nearly 41% of the ATC codes. However, this was not an impediment in establishing Rx-MG assignments due to the fact that the ATC hierarchy is therapeutic centric; and, in almost every case, it provides much more information than the drug / route combinations.

4.

Use of original Drug / Route-to-Rx-MG Assignments

. In order to preserve assignment consistency from the NDC-based Rx-MGs, Drug / Route-to-Rx-MG assignments were systematically reviewed and were considered as a guide when making ATC-to-Rx-MG assignments.

Once again, a two-step process for Rx-MG assignments was undertaken. A team of international clinicians made the initial NDC-to-Rx-MG assignments. Consensus meetings were held to reconcile disagreements. Afterwards a separate team of six clinicians composed of Johns Hopkins physicians and pharmacists and an international group of physicians made independent assignment to a randomly selected sample of 300 codes. The result of the review led to very few modifications. In fact, 99% of the codes were assigned to the same Rx-MG by the majority of the clinician panel (83% of the codes were assigned unanimously by the six clinicians to a single Rx-MG, 10% received the same Rx-MG assignment by five of the six clinicians, 6% were assigned to the same

Rx-MG by four of the six clinicians, and the remaining 1% was assigned to the same assignment by three of the six clinicians).

Table 6: Number of Pharmacy Codes for Each Rx-MG

Rx-Defined Morbidity Group

Allergy/Immunology

Acute Minor

Chronic Inflammatory

Immune Disorders

Transplant

Cardiovascular

Chronic Medical

Congestive Heart Failure

High Blood Pressure

Disorders of Lipid Metabolism

Cardiovascular / Vascular Disorders

Ears, Nose, Throat

% of All NDC

Codes

2.7

0.6

7.7

1.1

0.7

2.0

1.1

0.1

0.1

% of Fourth and Fifth

Level ATC Codes

2.6

1.0

6.1

1.0

2.3

1.5

0.4

0.2

0.3




Acute Minor

Endocrine

Bone Disorders

Chronic Medical

Diabetes With Insulin

Diabetes Without Insulin

Thyroid Disorders

Growth Problems

Weight Control

Eye

Acute Minor: Curative

Acute Minor: Palliative

Glaucoma


Hormone Regulation

Infertility

Pregnancy and Delivery


Acute Minor

Chronic Liver Disease

Chronic Stable

Inflammatory Bowel Disease

Pancreatic Disorder

Peptic Disease


Nausea and Vomiting

Pain


5-11

% of All NDC

Codes

0.3

0.1

0.8

0.1

1.5

0.9

0.1

0.5

0.5

0.6

0.6

0.6

0.1

0.5

3.5

0.2

0.0

0.1

0.1

1.5

1.4

7.7


Level ATC Codes

0.4

0.3

0.1

1.0

4.5

0.7

0.6

3.2

1.1

0.3

0.7

1.8

1.4

0.9

0.1

0.1

2.2

0.7

0.8

0.5

1.0

0.8


5-12


Pain and Inflammation

Severe Pain

Genito-Urinary

Acute Minor

Chronic Renal Failure

Hematologic

Coagulation Disorders

Infections

Acute Major

Acute Minor

HIV / AIDS

Tuberculosis

Severe Acute Major

Malignancies

Musculoskeletal

Gout

Inflammatory Conditions

Neurologic

Alzheimer's Disease

Chronic Medical

Migraine Headache

Parkinson's Disease

Seizure Disorder

Psychosocial

Acute Minor



% of All NDC

Codes

5.4

1.4

1.2

0.1

0.2

0.2

7.9

0.3

0.2

1.1

1.0

0.3

0.5

0.2

0.1

0.2

0.8

2.2

1.0


Level ATC Codes

0.3

1.0

0.5

0.9

0.3

0.8

0.9

1.3

4.2

7.1

0.7

0.6

1.2

0.4

1.8

1.0

0.2

3.5

0.4




% of All NDC

Codes


Level ATC Codes

Addiction

Anxiety

Attention Deficit Hyperactivity Disorder

Chronic Unstable

Depression

Respiratory

Acute Minor

Airway Hyper-reactivity

Chronic Medical

Cystic Fibrosis

Skin

Acne

Acute and Recurrent

Chronic Medical

Toxic Effects/Adverse Effects

Acute Major

0.1

1.6

0.3

1.8

4.0

0.6

5.4

0.1

4.7

1.3

0.1

0.1

0.3

Other and Non-Specific Medications

Supplies and other non-pharmaceutical codes (not mapped to an Rx-MG)

7.50

9.7

Total Number of Pharmacy Codes

173,477

The final set of 64 Rx-MG categories, along with their distinguishing clinical characteristics, is shown in

Table 7: Rx-MG Morbidity Taxonomy and Clinical

Characteristics of Medications Assigned to Each Rx-MG

.

5,055

0.9

0.8

6.9

0.7

15.6

2.1

0.5

0.0

1.3

2.7

0.3

0.9

0.2

1.4

N/A



Table 7: Rx-MG Morbidity Taxonomy and Clinical Characteristics of Medications Assigned to Each

Rx-MG

Severity


Allergy/Immunology

Acute Minor


Immune Disorders

Transplant

Cardiovascular

Chronic Medical


High Blood Pressure


Vascular Disorders

Ears-Nose-Throat

Acute Minor


Differentiation Duration Acute Conditions: Impact

Symptoms/General

General

General

General

General

Disease

Disease

Disease

General

Symptoms /

General

Acute

Chronic

Chronic

Chronic

Chronic

Chronic

Chronic

Chronic

Chronic

Acute

Low Impact

Low Impact

Chronic and Recurrent

Conditions: Stability

Unstable

Unstable

Unstable

Stable

Unstable

Stable

Stable

Stable




Endocrine

Bone Disorders

Chronic Medical



Thyroid Disorders

Growth Problems

Weight Control

Eye



Glaucoma


Hormone Regulation

Infertility



General

General

Disease

Disease

General

General

General

General

Symptoms

Disease

General

General

Chronic

Chronic

Chronic

Chronic

Chronic

Chronic

Chronic

Acute

Acute

Chronic

Chronic

Acute

Low Impact

Low Impact

High Impact

Severity



Stable

Stable

Unstable

Stable

Stable

Stable

Stable

Stable

Stable





Gastrointestinal / Hepatic

Acute Minor


Chronic Stable


Pancreatic Disorder

Peptic Disease


Nausea and Vomiting

Pain


Severe Pain

Genito-Urinary



General

Symptoms /

General

General

General

Disease

General

Disease

Symptoms

Symptoms

Symptoms

Symptoms

Acute

Acute

Chronic

Chronic

Chronic

Chronic

Recurrent

Acute

Acute

Acute

Acute

High Impact

Low Impact

Low Impact

Low Impact

Low Impact

High Impact

Severity



Unstable

Stable

Stable

Unstable

Stable




Acute Minor


Hematologic


Infections

Acute Major

Acute Minor

HIV / AIDS

Tuberculosis

Severe Acute Major

Malignancies

Malignancies

Musculoskeletal



Symptoms /

General

Disease

General

General

General

Disease

Disease

General

General

Acute

Chronic

Chronic

Acute

Acute

Chronic

Acute

Acute

Chronic

Low Impact

High Impact

Low Impact

Low Impact

High Impact

Severity



Unstable

Unstable

Unstable

Unstable




Gout


Neurologic

Alzheimers Disease

Chronic Medical

Migraine Headache

Parkinsons Disease

Seizure Disorder

Psychosocial

Attention Deficit Disorder

Addiction

Anxiety

Depression

Acute Minor

Chronic Unstable



Disease

General

Disease

General

Disease

Disease

General

Disease

General

Symptoms /

General

Disease

Symptoms /

General

General

Recurrent

Chronic

Chronic

Chronic

Recurrent

Chronic

Chronic

Chronic

Chronic

Recurrent

Chronic

Acute

Chronic

Low Impact

Severity



Stable

Unstable

Unstable

Stable

Stable

Unstable

Stable

Stable

Unstable

Stable

Stable

Unstable




Respiratory

Acute Minor

Airway Hyper-reactivity

Chronic Medical

Cystic Fibrosis

Skin

Acne

Acute and Recurrent

Chronic Medical

Toxic Effects / Adverse Effects

Acute Major



Symptoms /

General

Disease

General

Disease

Disease

Symptoms /

General

General

General

N/A

Acute

Chronic

Chronic

Chronic

Chronic

Acute and

Recurrent

Chronic

Acute

N/A

Low Impact

Low Impact

High Impact

N/A

Severity



Stable

Stable

Unstable

Stable

Stable

N/A



The frequency of occurrence of each Rx-MG is shown in

Table 8: Annual Prevalence

Ratios for Rx-MGs for Non-Elderly and Elderly Beneficiaries.

Table 8: Annual Prevalence Ratios for Rx-MGs for Non-Elderly and

Elderly Beneficiaries

Number of Persons with Rx-MG per 1,000

Enrollees

Rx-Defined Morbidity Group (Rx-MG)

Allergy / Immunology

Acute Minor


Immune Disorders

Transplant

Cardiovascular

Chronic Medical


High Blood Pressure


Vascular Disorders

Ears, Nose, Throat

Acute Minor

Endocrine

Bone Disorders

Chronic Medical



Thyroid Disorders

Weight Control

Growth Problems

Eye



Glaucoma


Hormone Regulation

Infertility


Non-Elderly

(0 to 64 Years)

0.22

35.87

17.31

4.38

10.72

28.04

8.67

27.83

35.55

2.34

61.89

1.04

9.11

13.29

8.01

119.53

80.15

11.43

19.83

85.99

68.06

0.29

0.87



0.09

72.14

55.86

66.45

115.91

65.66

38.43

130.15

151.92

0.86

4.73

0.00

0.41

120.54

99.04

0.83

1.58

155.65

72.95

575.15

424.22

138.68

16.38





Acute Minor


Chronic Stable


Pancreatic Disorder

Peptic Disease


Nausea and Vomiting

Pain


Severe Pain

Genito-Urinary

Acute Minor


Hematologic


Infections

Acute Major

Acute Minor

HIV / AIDS

Tuberculosis

Severe Acute Major

Malignancies

Musculoskeletal

Gout


Neurologic

Alzheimer's Disease

Chronic Medical

Migraine Headache

Parkinson's Disease

Seizure Disorder


Enrollees

Non-Elderly

(0 to 64 Years)

2.93

357.30

1.02

0.87

0.32

4.30

28.14

0.38

0.08

32.81

0.63

0.48

2.97

0.39

63.48

36.45

171.41

92.49

13.83

5.40

5.95

0.28

2.66

13.61

3.43

29.48



5.93

436.60

0.57

1.07

1.37

22.87

154.38

3.32

0.36

95.57

1.53

1.31

7.16

1.88

203.44

58.35

283.47

142.34

32.73

36.04

17.00

32.15

3.20

5.19

21.81

69.12




Enrollees


Psychosocial

Acute Minor

Addiction

Anxiety

Attention Deficit Hyperactivity Disorder

Chronic Unstable

Depression

Respiratory

Acute Minor

Airway Hyperactivity

Chronic Medical

Cystic Fibrosis

Skin

Acne

Acute and Recurrent

Chronic Medical

Toxic Effects / Adverse Effects

Acute Major


Non-Elderly

(0 to 64 Years)

38.79

2.32

47.48

17.85

8.15

94.64

86.45

74.77

4.37

0.21

24.03

104.49

2.85

0.11

41.61



71.08

2.30

83.30

3.07

17.87

167.33

80.46

112.70

40.74

1.82

6.22

192.98

2.52

0.32

168.30





Active Ingredient Count Variable

An active ingredient count is calculated as the count of unique active ingredient / route of administration combinations encountered in the patient’s drug claims. This marker is a proxy for identifying poly-pharmacy members and contributes independently and significantly to the prediction of cost. This marker is incorporated into all pharmacybased predictive models (Rx-PM and DxRx-PM

TM

). Members with a generic drug count of 14 or greater will get additional weight in the predictive models.

Conclusion

The Rx-MGs were created using a clinical framework that makes sense to health professionals and medical managers. As we will discuss in the Chapters on Predicting

Resource Use and Predicting Hospitalizations, pharmacy derived morbidity markers add a great deal of clinical context and improved statistical performance when applied to predictive modeling applications.





Special Population Markers 6-i

6 Special Population Markers

Introduction ................................................................................................... 6-1

Hospital Dominant Morbidity Types .......................................................... 6-1

Table 1: Examples of Diagnostic Codes Included in the Hospital

Dominant Morbidity Types ........................................................................ 6-1

Table 2: Effects of Hospital Dominant Morbidity Types During a Baseline Year on Next Year’s Hospitalization Risk, Total

Healthcare Costs, and Pharmacy Costs ...................................................... 6-2

Frailty Conditions ......................................................................................... 6-3

Table 3: Medically Frail Condition Marker – Frailty Concepts and Diagnoses ............................................................................................ 6-3

Table 4: Effects of Frailty Conditions During a Baseline Year on Next Year’s Hospitalization Risk, Total Healthcare Costs, and Pharmacy Costs ................................................................................... 6-4

Pregnancy without Delivery ......................................................................... 6-5

Chronic Condition Count ............................................................................. 6-5

Table 5: EDCs considered in the Chronic Condition Count Marker ........ 6-6

Table 6: Distribution of the Chronic Condition Count Marker ................. 6-9

Condition Markers ...................................................................................... 6-10

Table 7: Definitions of Condition Markers ............................................. 6-11

The Johns Hopkins ACG ® , System, Version 10.0 Technical Reference Guide

6-ii


Special Population Markers

Technical Reference Guide The Johns Hopkins ACG ® , System, Version 10.0

Special Population Markers 6-1

Introduction

The ACG ® System includes a number of special population markers to identify patient populations requiring specialized care. These markers enhance the clinical screening process by providing meaningful filtering criteria and clinical context. Several of the markers are also independent variables in the predictive models.

Hospital Dominant Morbidity Types

Hospital dominant morbidity types are based on diagnoses that, when present, are associated with a greater than 50 percent probability among affected patients of hospitalization in the next year. All these diagnoses are setting-neutral, i.e., they can be given in any inpatient or outpatient face-to-face encounter with a health professional.

The variable is a count of the number of morbidity types (i.e., ADGs  ) with at least one hospital dominant diagnosis. When the stringent diagnostic certainty option is selected, more than one diagnosis from the hospital dominant condition list may be required for the marker to be turned on.

Table 1: Examples of Diagnostic Codes Included in the

Hospital Dominant Morbidity Types provides examples of diagnostic codes that have been identified as hospital dominant.

Table 1: Examples of Diagnostic Codes Included in the Hospital

Dominant Morbidity Types

Individuals with any one of these diagnostic codes have a greater than 50 %chance of hospitalization in the subsequent year.

ICD-9-CM Code ICD-10 Code Description

162.3

261

2638

284.8

2894

29181

29643

0380

4150

4821

491.21

518.81

5722

C34.1 Malignant Neoplasm, Upper Lobe, Bronchus or

Lung

E41 Nutritional Marasmus

E43 Other Protein Calorie Malnutrition Nec

D61.9 Other specified Aplastic Anemia

D73.1 Hypersplenism

F10.3 Alcohol Withdrawal

F31.6 Bipolar affective disorder, mixed episode

A40 Streptococcal Septicemia

I26.0 Acute cor pulmonale

J15.1 Pseudomonal Pneumonia

J44.1 Obstructive Chronic Bronchitis with acute exacerbation

J96 Acute Respiratory Failure

K72.9 Hepatic coma


6-2 Special Population Markers

ICD-9-CM Code ICD-10 Code Description

584.9

785.4

7895

N17.9 Acute Renal Failure, unspecified

A48.0 Gangrene

R18 Ascites

The risk of hospitalization, total healthcare costs, and medication costs rise dramatically in association with the number of hospital dominant morbidity types (See

Table 2:

Effects of Hospital Dominant Morbidity Types During a Baseline Year on Next

Year’s Hospitalization Risk, Total Healthcare Costs, and Pharmacy Costs

). The count of hospital dominant morbidity types is a powerful predictor of greater resource use next year. Commercially insured individuals who have a single hospital dominant morbidity type have a five-fold greater chance of having at least one hospitalization next year, compared with individuals without such a condition. There is a 10-fold increase for individuals in the group with two or more hospital dominant morbidity types. For commercially insured individuals and Medicare ((United States federal program that pays for certain health care expenses for people aged 65 or older) beneficiaries alike, significant increases in healthcare costs are associated with the presence of one or more hospital dominant morbidity types.

Table 2: Effects of Hospital Dominant Morbidity Types During a

Baseline Year on Next Year’s Hospitalization Risk, Total Healthcare

Costs, and Pharmacy Costs

HOSPITAL DOMINANT MORBIDITY TYPES

Baseline Year Risk Factors

Next Year’s Outcomes

% with 1+

Hospitalization

Mean Total

Healthcare Costs

Mean

Pharmacy Costs

Commercially Insured Individuals (<65 years-old)

Number of hospital dominant morbidity types

None

1

2+

2.1

13.7

31.7

$3,178

$18,094

$47,884

$595

$3,057

$4,500

Number of hospital dominant morbidity types

None

Medicare Beneficiaries (65 years and older)

11.8 $10,266 $1,686

Technical Reference Guide The Johns Hopkins ACG ® System, Version 10.0



1

2+

HOSPITAL DOMINANT MORBIDITY TYPES


% with 1+

Hospitalization

28.3

40.9

Mean Total


$26,688

$44,541

Mean

Pharmacy Costs

$3,581

$3,908



Frailty Conditions

The

Medically frail condition marker is a dichotomous (on / off) variable that indicates whether an enrollee has a diagnosis falling within any one of 12 clusters that represent medical problems associated with frailty. When the stringent diagnostic certainty option is selected, more than one diagnosis from the frailty condition list may be required for the marker to be turned on. Examples of these problems are shown in

Table 3

. In collaboration with a team of Johns Hopkins Geriatricians, we identified diagnostic codes that are highly associated with marked functional limitations among older individuals

(i.e., frailty). Presence of any one of these diagnoses turns on the FRAILTY indicator

(yes/no) variable. Among commercially insured populations, about two percent of individuals have at least one of these frailty diagnoses, whereas among Medicare beneficiaries the proportion is four percent.

Table 3: Medically Frail Condition Marker – Frailty Concepts and

Diagnoses

Frailty Concept

Malnutrition

Dementia

Impaired Vision

Decubitus Ulcer

Incontinence of Urine

Loss of Weight

Incontinence of Feces

Obesity (morbid)

Diagnoses (Examples)

Nutritional Marasmus

Other severe protein-calorie malnutrition

Senile dementia with delusional or depressive features

Senile dementia with delirium

Profound impairment, both eyes

Moderate or severe impairment, better eye/lesser eye: profound

Decubitus Ulcer

Incontinence without sensory awareness

Continuous leakage

Abnormal loss of weight and underweight

Feeding difficulties and mismanagement

Incontinence of feces

Morbid obesity

Lack Of Housing

Inadequate Housing



Frailty Concept Diagnoses (Examples)

Poverty

Barriers to Access of Care

Difficulty in Walking

Fall

Inadequate material resources

No Med Facility For Care

No Med Facilities Nec

Difficulty in walking

Abnormality of gait

Fall On Stairs Or Steps

Fall From Wheelchair

The risk of hospitalization, total healthcare costs, and medication costs rise in association with a frailty condition (See

Table 4: Effects of Frailty Conditions During a Baseline

Year on Next Year’s Hospitalization Risk, Total Healthcare Costs, and Pharmacy

Costs

).

Table 4: Effects of Frailty Conditions During a Baseline Year on Next

Year’s Hospitalization Risk, Total Healthcare Costs, and Pharmacy

Costs

FRAILTY CONDITIONS



% with 1+

Hospitalization

Mean Total


Mean

Pharmacy Costs


Number of frailty conditions:

None

1+

12.1

25.4

$11,037

$20,984

$1,755

$2,729

Source: IMS Health Payer Solutions, Watertown, MA; Subset of the Patient-Centric Database containing a national cross-section of managed care plans; population of 463,126 Medicare beneficiaries (65 years and older), 2008-09.



Pregnancy without Delivery

Pregnancy without delivery is an important condition for predicting future resource use and is a marker used by the ACG Predictive Models.

If date of service is not available, this marker will be set to Y when ACG does not indicate delivery: 1712, 1722, 1732, 1742, 1752, 1762 or 1772.

If date of service is available, this marker will consider the order of events to capture the latest pregnancy status during the observation period in the event that a pregnancy does not continue to delivery or the patient experiences multiple pregnancies during the year.

• If the patient delivered during the year, but there are additional pregnancy diagnoses more than six weeks after the latest delivery diagnosis, the pregnancy without delivery marker will be set to Y.

• If the patient was pregnant during the year, but the pregnancy terminated, the pregnancy without delivery marker will be set to N. If a subsequent pregnancy is identified more than six weeks after the latest termination diagnosis, the pregnancy without delivery marker will be set to Y.

Chronic Condition Count

The ACG System includes a chronic condition count as an aggregate marker of case complexity. A chronic condition is an alteration in the structures or functions of the body that is likely to last longer than twelve months and is likely to have a negative impact on health or functional status.

The ACG System defines a limited set of Expanded Diagnosis Clusters (EDCs) that represent high impact and chronic conditions likely to last more than 12 months with or without medical treatment (see

Table 5: EDCs considered in the Chronic Condition

Count Marker

). From this list of EDCs, individual diagnosis codes were tested against the criteria for chronic conditions stated above. The diagnoses identified by these EDCs were compared against a chronic condition list provided by the Center for Child and

Adolescent Health Policy, Mass General Hospital for Children in Boston, Massachusetts.

Differences between the lists were identified as psychological or medical/surgical conditions. The psychological codes were reviewed by a practicing psychologist and health services researcher for congruence with the operational definition. Several anxiety and substance abuse codes were deleted from the chronic condition list as a result of this review. Several acute conditions of the retina were deleted from the chronic condition list after a similar review of medical/surgical conditions. The Center for Child and

Adolescent Health Policy list and the ACG System differ in definitions related to infectious diseases such as tuberculosis, peptic ulcer disease, congenital heart disease

(which is generally resolved through surgical interventions at birth), gastrointestinal obstructions and perforations (likely to be acute and treatable conditions), osteomyelitis, and prematurity. These conditions are not considered chronic conditions in the ACG

System chronic condition marker.



The chronic diagnosis codes that were identified were further classified and aggregated into EDC categories. Not all diagnosis codes that map to these EDC categories are considered chronic. The chronic diagnosis codes trigger a chronic EDC flag (see

Table

5: EDCs considered in the Chronic Condition Count Marker

).

Table 5: EDCs considered in the Chronic Condition Count Marker

EDC Description

Acute hepatitis

Acute leukemia

Adverse events from medical/surgical procedures


Anxiety, neuroses

Aplastic anemia

Arthropathy


Asthma, with status asthmaticus



Behavior problems

Benign and unspecified neoplasm

Bipolar disorder

Blindness

Cardiac arrhythmia


Cardiomyopathy

Cardiovascular disorders, other

Cataract, aphakia

Central nervous system infections

Cerebral palsy



Chronic cystic disease of the breast






Congenital anomalies of limbs, hands, and feet



Cystic fibrosis

Deafness, hearing loss




Depression


Diabetic retinopathy

EDC Flag

CAR06

CAR07

CAR16

EYE06

NUR20

NUR18

NUR05

GTC01

GSU06

GAS05

GAS12

REN01

REC03

REC01

MUS11

CAR04

CAR05

RES03

EAR08

HEM06

MUS03

NUR11

PSY09

NUR19

EYE13

GAS04

MAL16

TOX03

EYE15

PSY01

HEM05

RHU03

ALL04

ALL05

PSY05

RHU01

PSY04

GSU03

PSY12

EYE02

CAR09



EDC Description

Disorders of lipid metabolism

Disorders of Newborn Period

Disorders of the immune system


Endometriosis

Eye, other disorders

Failure to thrive

Gastrointestinal signs and symptoms

Gastrointestinal/Hepatic disorders, other


Genito-urinary disorders, other

Glaucoma

Gout

Hematologic disorders, other

Hemophilia, coagulation disorder


HIV, AIDS


Hypertension, with major complications

Hypothyroidism


Inherited metabolic disorders

Irritable bowel syndrome


Kyphoscoliosis

Lactose intolerance

Low back pain


Malignant neoplasms of the skin



Malignant neoplasms, cervix, uterus











Migraines

Multiple sclerosis

Muscular dystrophy

Musculoskeletal disorders, other

Musculoskeletal signs and symptoms

The Johns Hopkins ACG ® System, Version 10.0

EDC Flag

CAR11

NEW05

ALL06

RES04

FRE03

EYE14

NUT01

GAS01

GAS14

CAR10

GUR12

EYE08

RHU02

HEM08

HEM07

MAL03

INF04

CAR14

CAR15

END04

GAS02

GTC02

GAS09

CAR03

MUS06

GAS13

MUS14

MAL02

MAL01

MAL18

MAL04

MAL05

MAL12

MAL07

MAL08

MAL09

MAL10

MAL11

MAL06

MAL13

MAL14

MAL15

NUR22

NUR08

NUR09

MUS17

MUS01

6-7


6-8

EDC Description

Nephritis, nephrosis

Neurologic disorders, other

Neurologic signs and symptoms

Newborn Status, Complicated

Obesity

Osteoporosis

Other endocrine disorders


Other male genital disease

Other skin disorders

Paralytic syndromes, other

Parkinson's disease





Psychosocial disorders, other

Pulmonary embolism

Quadriplegia and paraplegia

Renal disorders, other

Respiratory disorders, other




Seizure disorder

Short stature

Sickle cell disease

Sleep apnea

Spinal cord injury/disorders

Strabismus, amblyopia

Substance use

Thrombophlebitis

Tracheostomy

Transplant status

Type 1 diabetes without complication

Type 1 diabetes with complication

Type 2 diabetes without complication

Type 2 diabetes with complication

Vesicoureteral reflux

EDC Flag

PSY11

RES08

NUR12

REN05

RES11

EYE03

RHU05

PSY07

NUR07

END03

HEM09

RES06

NUR16

EYE11

PSY02

HEM03

REN04

NUR21

NUR01

NEW02

NUT03

END02

END05

HEM01

GUR07

SKN17

NUR17

NUR06

NUR03

GSU11

PSY08

GUR04

RES09

ADM03

END08

END09

END06

END07

GUR01




The chronic condition count represents the number of unique chronic EDC Flags present in the individual’s diagnosis history. When the stringent diagnostic certainty option is selected, more than one diagnosis from the chronic condition list may be required for the marker to be turned on.

Table 6: Distribution of the Chronic Condition Count

Marker

provides a representative distribution of the chronic condition count for elderly and non-elderly populations.

Table 6: Distribution of the Chronic Condition Count Marker

Chronic Condition Count Non-Elderly Elderly

7

8

9

10+

2

3

4

0

1

5

6

63.2%

17.3%

8.9%

5.0%

2.7%

1.4%

0.7%

0.4%

0.2%

0.1%

0.1%

14.9%

11.1%

14.4%

15.4%

13.3%

10.2%

7.2%

4.8%

3.2%

2.1%

3.4%





Condition Markers

The ACG System uses condition markers to highlight specific conditions that are high prevalence chronic conditions, commonly selected for disease management or warranting ongoing medication therapy. Conditions that are used to measure pharmacy adherence are discussed in more detail in Chapter 11: Gaps in Pharmacy Utilization.

The specific criteria for each condition are listed in

Table 7: Definitions of Condition

Markers

. The values within these condition markers indicate the evidence used to identify members with the condition. The following values are possible:

•

•

NP

– The condition is not present; there is no evidence of the condition from any data source.

TRT

– The condition was identified according to specific treatment criteria (listed in

Table 7: Definitions of Condition Markers

) which include a minimum of two prescriptions within 120 days of each other (but on different dates of service) from an appropriate chronic medication drug class. Regardless of the diagnostic certainty option selected, minimum visit counts will apply as listed in

Table 7: Definitions of


. It is possible to select the stringent diagnostic certainty option requiring two diagnoses for a related EDC, yet assign a condition marker to the value of TRT based on a single inpatient or emergency department diagnosis.

•

•

•

•

ICD

– The condition was identified only from diagnosis information. Each condition is identified by one or more EDCs. When the stringent diagnostic certainty option is selected, more than one diagnosis may be required for the condition to be identified.

Rx

– The condition was identified only from pharmacy information and minimum treatment criteria were not met.

BTH

– The condition was identified by both diagnosis and pharmacy criteria but minimum treatment criteria were not met.

For each condition, a separate column indicates if the condition is untreated. The specific criteria for an untreated condition are listed in

Table 7: Definitions of


. Regardless of the specific visit criteria specified, the patient must also have the EDC assigned under the diagnostic criteria column to be identified as untreated. It is possible to meet the untreated criteria based on a single inpatient or emergency department diagnosis. If the stringent diagnostic certainty option is applied requiring two diagnoses for a related EDC, the patient will not be designated as untreated. The untreated column will have the following values:

−

−

Y

– The patient has no prescriptions in an appropriate chronic medication drug class.

P

– The patient is classified as potentially untreated based upon one of the following criteria:

1.

The patient has only one prescription in an appropriate chronic medication drug class.



−

2.

The patient has more than one prescription in an appropriate chronic medication drug class but all prescriptions occurred on a single fill date.

3.

The patient has more than one prescription from multiple chronic medication drug classes but does not have two or more from a single chronic medication drug class.

4.

The patient has more than one prescription in an appropriate chronic medication drug class but there are not two prescriptions within 120 days of each other.

N

– The patient meets treatment criteria and has two or more prescriptions in an appropriate chronic medication drug class.

− D

– The patient meets treatment criteria, but it appears all medications within an appropriate chronic medication drug class have been discontinued, and the patient is now likely untreated based on the fact that greater than 120 days have elapsed between the latest supply end date and the observation period end date.

If none of the values apply to a particular member, the untreated column will be blank indicating that there was insufficient evidence to confirm the presence of the specific condition.

Table 7: Definitions of Condition Markers

Condition

Bipolar disorder

Diagnostic

Criteria

PSY12

Pharmacy

N/A

Criteria

Congestive heart failure CAR05 CARx020

Treatment

Criteria

Untreated

Criteria

1 inpatient or ER diagnosis or 2 outpatient diagnoses plus 2 prescription fills

(within 120 days of each other) in at least one of the drug classes:

Anti-convulsants,

Anti-psychotics



ACEI/ARB

Aldosterone

1 inpatient or ER diagnosis or 2 outpatient diagnoses with less than 2 prescription fills (within 120 days of each other) in a single drug class:

Anti-convulsants,

Anti-psychotics


ACEI/ARB

Aldosterone



Condition

Depression

Diabetes


Diagnostic

Criteria

PSY09

END06,

END07,

END08,

END09

CAR11

Pharmacy

Criteria

Treatment

Criteria receptor blockers

Beta-blockers

Diuretics

Inotropic agents

Vasodilators

Untreated

Criteria receptor blockers

Beta-blockers

Diuretics

Inotropic agents

Vasodilators

PSYx040

Meglitinides

Non-sulfonylureas

Sulfonylureas

Thiazolidinediones

Other antihyperglycemic agents

Long and shortacting Insulins

2 prescription fills


Meglitinides

Non-sulfonylureas

Sulfonylureas

Thiazolidinediones




Meglitinides

Non-sulfonylureas

Sulfonylureas

Thiazolidinediones



Bile acid sequestrants

Cholesterol absorption inhibitors

Fibric acid derivatives

HMG-CoA reductase


(within 120 days of each other) in the drug class:

Anti-depressants

1 inpatient or ER diagnosis or 2 outpatient diagnoses with less than 2 prescription fills (within 120 days of each other) in the drug class:

Anti-Depressants






Bile acid



Condition

Glaucoma

Human Immunodeficiency

Virus

Hypertension

Diagnostic

Criteria

Pharmacy

Criteria inhibitors

Miscellaneous antihyperlipidemic agents

Treatment

Criteria


HMG-CoA reductase inhibitors


Untreated

Criteria sequestrants





EYE08 EYEx030

INF04

CAR14,

CAR15

HAART*

CARx030



Ophthalmic glaucoma agents



HAART*


Ophthalmic glaucoma agents


HAART*



ACEI/ARB

Aldosterone receptor blockers

Anti-adrenergic agents

1 inpatient or ER diagnosis or 2 outpatient diagnoses with less than 2 prescription fills (within 120 days of each other) in a single drug class

ACEI/ARB





Condition

Hypothyroidism

Immunosuppression/Transplant

Ischemic heart disease

Osteoporosis

Diagnostic

Criteria

END04

ADM03

CAR03

END02

Pharmacy

Criteria

Thyroid replacement drugs

ALLx050

N/A

ENDx010

Treatment

Criteria

Beta-blockers

Calcium channel blockers

Diuretics

Vasodilators

Untreated

Criteria

Beta-blockers


Diuretics

Vasodilators



Thyroid drugs


Thyroid drugs



Immunologic agents


Immunologic agents



Antianginal agents

Beta-blockers



Antianginal agents

Beta-blockers



(within 120 days of each other) in the

1 inpatient or ER diagnosis or 2 outpatient diagnoses with less than 2 prescription fills (within 120 days of each other)



Condition

Parkinson’s disease

Persistent asthma

Diagnostic

Criteria

NUR06

ALL04,

ALL05

Pharmacy

Criteria

Anticholinergic antiparkinson agents

Dopaminergic antiparkinsonism agents

RESx040

Treatment

Criteria drug class:

Osteoporosis

Hormones

Untreated

Criteria in the drug class:

Osteoporosis

Hormones








1 inpatient or ER principal diagnosis, or 4 outpatient diagnoses, plus 2 prescription fills

(within 120 days of each other) in at least one of the drug classes, or

4 prescription fills in any of the drug classes with at least

2 prescription fills in one of the drug classes other than

Leukotriene modifiers, and at least 2 prescription fills within 120 days of each other in the same drug class:

Long and shortacting Adrenergic bronchodilators

Immunosuppressive monoclonal

1 inpatient or ER principal diagnosis or 4 outpatient diagnoses with less than 2 prescription fills (within 120 days of each other) in a single drug class:

Long and shortacting Adrenergic bronchodilators

Immunosuppressive monoclonal antibodies

Inhaled corticosteroids

Leukotriene modifiers

Mast cell stabilizers

Methylxanthines


6-16

Condition


Schizophrenia

Seizure disorders


COPD



Diagnostic

Criteria

RHU05

PSY07

NUR07

EYE15

RES04

Pharmacy

Criteria

MUSx020

N/A

NURx050

Treatment

Criteria antibodies




Methylxanthines

Untreated

Criteria



Disease-modifying anti-rheumatic drugs (DMARDs)

Immunologic agents


Disease-modifying anti-rheumatic drugs (DMARDs)

Immunologic agents



Anti-psychotics


Anti-psychotics



Anti-convulsants

N/A


Anti-convulsants

N/A Anti-angiogenic ophthalmic agents

N/A N/A N/A



Condition

Diagnostic

Criteria

Pharmacy

Criteria

Treatment

Criteria

Untreated

Criteria


Low back pain

REN01

MUS14

GURx020

N/A

N/A

N/A

N/A

N/A

*HAART represents a multi-drug cocktail that can be delivered in a number of configurations.

1.

HAART (a three drug combination filled with a single prescription)

2.

Any NRTI combination drug including either zidovudine , abacavir or tenofovir with an additional NNRTI, entry inhibitor, integrase inhibitor, or protease inhibitor

3.

A NNRTI plus a ritonavir-boosted protease inhibitor or nelfinavir

4.

Three of more drugs from two or more different drug classes (Entry Inhibitors,

Integrase Inhibitors, NNRTIs, NRTIs, Protease Inhibitors)





Predicting Future Resource Use 7-i

7 Predicting Future Resource Use

Objectives ....................................................................................................... 7-1

Conceptual Basis of Predictive Modeling ................................................... 7-1

What is Predictive Modeling? .................................................................... 7-1

Text Box 1: Definition of Population-Based Predictive Modeling ........... 7-2

Forecasting Future Costs with the Components of the ACG

Case-Mix System ....................................................................................... 7-3

Table 1: Selected ACG Variables versus Hospitalization as

Predictors of High Cost Patients ................................................................ 7-3

Table 2: Proportion of Total Healthcare Costs Consumed by the

Highest Cost 10% and Lowest Cost 50% of Enrollees .............................. 7-4

Multi-Morbidity as the Clinical Basis of PM ............................................. 7-5

Figure 1: Percentage of Patients with Selected Chronic Condition and Their Associated Number of Co-Morbidities ...................................... 7-6

Figure 2: Total Healthcare Costs per Beneficiary by Count of

Chronic Conditions .................................................................................... 7-8

Elements of a Predictive Model--Five Key Dimensions ............................ 7-9

Conceptual Basis ................................................................................... 7-9

Target Population ................................................................................ 7-10

Statistical Modeling Approach ............................................................ 7-11

Data Inputs ........................................................................................... 7-12

Outcomes Assessed ............................................................................. 7-13

Development of ACG Predictive Models .................................................. 7-13

Dx-PM: A Diagnosis-Defined Predictive Model ..................................... 7-13

Figure 3: Dx-PM Risk Factors ................................................................ 7-14

Development of Rx-PM ........................................................................... 7-15

Rx-PM Score ............................................................................................ 7-16

DxRx-PM: Combined ACG Predictive Model ........................................ 7-16

Resource Bands ........................................................................................... 7-16

High, Moderate, and Low Impact Conditions .......................................... 7-17

Table 3: Impact Type ............................................................................... 7-17

High Pharmacy Utilization Model ............................................................. 7-24

High Pharmacy Utilization Model Outputs .............................................. 7-24

Probability of Unexpected Pharmacy Cost .......................................... 7-24

High Risk for Unexpected Pharmacy Cost .......................................... 7-24

Relevant to Clinicians and Case Managers .............................................. 7-25


7-ii Predicting Future Resource Use

Table 4: Overlap of the High Pharmacy Utilization Model and

Dx-PM (Top 1.5% of Scores) .................................................................. 7-25


Predicting Future Resource Use 7-1

Objectives

This chapter is intended to describe the basics of ACG ® Predictive Modeling. The chapter provides background information on the conceptual and clinical basis underlying predictive modeling and provides the history of the development of the suite of

ACG Predictive Models (referred to collectively as ACG-PM).

Conceptual Basis of Predictive Modeling

What is Predictive Modeling?

Predictive modeling (PM) is used in a variety of manufacturing and service sectors to forecast, for example, costs, profitability, financial risk, purchasing behavior, and likelihood of defaulting on a loan. A predictive model comprises a set of variables (often called risk factors) that are selected because they are likely to influence the occurrence of the event or trend of interest. The predictions generated by the PM are used to improve decision-making in the face of uncertainty.

In healthcare, PM has two purposes—one focuses on the individual patient to improve clinical decision-making (i.e., clinical prediction tools) and the other (i.e., populationbased predictive models) draws on data from groups of patients to forecast healthcare cost trends and to identify candidates for healthcare interventions such as care or disease management programs.

In clinical medicine, PM is used to improve decision-making regarding prognosis, need for diagnostic testing, or likelihood of treatment responsiveness for individual patients.

For example, data from the Framingham Heart Study are used to determine an individual’s risk of coronary heart disease according to age, sex, cigarette smoking, LDL, and HDL cholesterol levels, blood pressure, and diabetes history.

1 Each of these risk factors is given an integer weight, and an individual’s probability of future heart disease is related to the sum of the risk factor weights. Patients and health professionals use the

Framingham Heart Disease Prediction Score to guide cardiovascular disease prevention behavior.

Population-based predictive models, such as the ACG models, are used to forecast trends in healthcare costs for groups of patients or to identify candidates for intensive healthcare interventions (

Text Box 1: Definition of Population-Based Predictive Modeling

).

(

Note:

Throughout this manual, we will refer to them simply as predictive models, rather than population-based PMs.) Currently, ACG-PM uses health and healthcare risk factors from administrative claims and encounter data to produce risk scores. Future versions of

ACG-PM will use clinical information from patients’ health records once these data are commonly collected and stored in digital formats.

1 Wilson PWF et al. Prediction of Coronary Heart Disease using risk factor categories. Circulation

1998;97:1837-1847.


7-2 Predicting Future Resource Use

Text Box 1: Definition of Population-Based Predictive Modeling

Definition of Population-Based

Predictive Modeling

Population-based predictive models use health and healthcare information derived from all members of a population to predict future health events or forecast healthcare costs and service use for populations or individual patients.



Forecasting Future Costs with the Components of the ACG Case-Mix

System

Before ACG-PM was developed, users of the ACG System successfully employed ACGs themselves and their building blocks, ADGs, to identify patients at high risk for future costs. A high burden of morbidity, defined by (1) the number of morbidity-types (ADGs) overall, 2) the number of major ADGs (i.e., those ADGs that have a high impact on future resources), or selected ACG categories perform well in predicting the future risk of using healthcare resource. The performance of these components compared to prior hospitalization in predicting high risk users is shown in

Table 1: Selected ACG

Variables versus Hospitalization as Predictors of High Cost Patients

.

Table 1: Selected ACG Variables versus Hospitalization as Predictors of High Cost Patients

Baseline (Year 1) Predictor Variables

Used to Define Risk Groups

Percentage of

Members

Percentage Risk

Group Who are High

Cost (top 5% of the population) in Next

Year (Year 2)

Prior Use

2+ Hospitalizations

Commercial population

Medicare population

Morbidity Measures

10+ Morbidity types (ADGs)


Medicare population

4+ Major Morbidity types (Major ADGs)


Medicare population

Selected ACGs


Medicare population

0.6%

4.1%

2.5%

11.3%

0.4%

4.2%

2.3%

11.2%

39.6%

19.5%

34.2%

12.6%

54.1%

19.5%

35.1%

12.7%

Source: PharMetrics, Inc., a unit of IMS, Watertown, MA; national cross-section of managed care plans; population of 2,259,584 commercially insured lives (less than 65 years old) and population of 93,620

Medicare beneficiaries (65 years and older), 2001-2002.



For example, commercially insured patients who were hospitalized two or more times in the baseline year comprised 0.6% of the total population and had a risk of 39.6% for being in the high cost group (top 5% of total healthcare costs) in the next year. On the other hand, patients with the highest morbidity burden ACG categories, those with four or more major ADGs comprised 0.4% of the total population and had a risk of 54.1% for being in the high cost group. Observations such as these led the ACG team to believe that the clinical logic that underpinned the ACG system could be successfully modified and expanded to form more comprehensive predictive modeling applications.

Retrospectively, we know with certainty which persons were high-cost consumers of health care resources and which were lower cost. One of the most consistent findings about resource utilization is that 10% of people consume about 60% of resources, whereas the lowest cost 50% of consumers account for only about 4% of costs (

Table 2:

Proportion of Total Healthcare Costs Consumed by the Highest Cost 10% and

Lowest Cost 50% of Enrollees

). This concentration of health care resources in a small segment of the population is a universal finding across all populations, and it is the key empirical observation that underpins the conceptual rationale of risk adjustment.

Predictive modeling reduces the uncertainty of who will be high-cost in the future. In terms of statistical performance, the best predictive models are those that are most accurate at either forecasting future costs for groups of patients or classifying patients as potentially high cost.

Table 2: Proportion of Total Healthcare Costs Consumed by the

Highest Cost 10% and Lowest Cost 50% of Enrollees

% Enrolled Population % Total Healthcare Costs

Highest cost 10%

Commercial

Medicare

63%

55%

Lowest cost 50%

Commercial

Medicare

4%

9%



The unique insight provided by the ACG System is that morbidity can be categorized in a way that is both clinically logical and informative of future healthcare resources. The clinical risk factors in ACG-PM are likely to persist over time and are indicative of sustained health need (i.e., capacity to benefit from health services) and demand (i.e., patient or provider desire for services). The predictions from ACG-PM can be used to identify high-cost persons prospectively to better forecast their future costs and to proactively identify individuals whose high intensity healthcare needs may be amenable to organized care management or expanded primary care interventions.



Multi-Morbidity as the Clinical Basis of PM

In the 21 st century, people are living longer and experiencing healthier later lives than their parents and grandparents. These improvements in health are a reflection of advances in public health, medicine, and general improvements in the economy that resulted in greater affluence for all people. With the development of vaccines and antimicrobial therapy, the morbidity profile of patients presenting to medical care has shifted from the common acute diseases of the 1900s to longer term, chronic diseases, such as hypertension, diabetes, or ischemic heart disease. This change in disease patterns has been called the “epidemiological shift.” Along with this greater burden of chronic illness, it became increasingly obvious that individuals are likely to have not just a single chronic condition, but multiple co-occurring chronic diseases, a phenomenon known as multi-morbidity. In fact, the ACG System was developed to classify multi-morbidity, the new clinical reality faced by populations in developed nations.

Among commercially insured populations, just 14.5% of patients with diabetes and

24.7% of those with hypertension had that condition only and no other chronic conditions

(

Figure 1: Percentage of Patients with Selected Chronic Condition and Their

Associated Number of Co-Morbidities

). On the other hand, among Medicare (United

States federal program that pays for certain health care expenses for people aged 65 or older) beneficiaries, 51.3% of those with Diabetes, 59.8% of those with Ischemic Heart

Fisease, 47.1% of those with Arthritis, and 38.2% of those with Hypertension had four or more other chronic conditions. In short, co-morbidity is the norm among adults with chronic illness.



Figure 1: Percentage of Patients with Selected Chronic Condition and

Their Associated Number of Co-Morbidities

Commercially Insured Population (less than 65 years old)

100%

90%

80%

70%

60%

50%

40%

30%

20%

10%

0%

25.1

15.7

22

22.6

14.5

Diabetes

35.2

18.2

21.3

17.4

8

Ischemic Heart

Disease

16.5

12.3

18.6

24.7

28

Arthritis

14.9

12

20.1

28.3

24.7

Hypertension

2

1

0

4+

3


100%

90%

80%

70%

60%

50%

40%

30%

20%

10%

0%

51.3

17.1

15.9

11.2

4.4

Diabetes

59.8

16.5

14.1

7.7

1.9

Ischemic Heart

Disease

47.1

18.1

17.2

12.5

5.1

Arthritis

38.2

17.2

19.6

17.1

7.9

Hypertension

2

1

0

4+

3



Source: PharMetrics, Inc., a unit of IMS, Watertown, MA; national cross-section of managed care plans; population of 2,259,584 commercially insured lives (less than 65 years old) and population of

93,620 Medicare beneficiaries (65 years and older), 2001-2002.

An extensive amount of research produced by our team and others in the academic and commercial sectors has repeatedly shown that multi-morbidity is a common clinical feature of individuals who were high-cost in the past and those likely to be high cost in the future. With each additional chronic condition, health spending per beneficiary rises dramatically (

Figure 2: Total Healthcare Costs per Beneficiary by Count of Chronic

Conditions

). Compared with individuals with no chronic conditions, those with five or more are 25 times more costly among the commercially insured and 20 times more costly among Medicare beneficiaries. In sum, multi-morbidity provides critically important clinical insight into why a small percentage of the population consumes such a large share of resources, and is therefore a logical clinical basis for predictive modeling.



Figure 2: Total Healthcare Costs per Beneficiary by Count of Chronic

Conditions

Commercially Insured Population (less than 65 years old)

20000

18000

16000

14000

12000

10000

8000

6000

4000

2000

0

$725

$2,180

$3,601

$5,537

$7,950

$18,118

0 1 2 3

# chronic conditions

4 5+


14000

$12,429

12000

10000

8000

6000

4000

2000

0

$637

$1,706

$2,482

$3,600

$5,198

0 1 2 3

# chronic conditions

4 5+





Elements of a Predictive Model--Five Key Dimensions

The structure of a predictive model can be characterized in five dimensions: conceptual basis; target population; data inputs; statistical modeling approach; and, outcomes predicted. Each of these dimensions is discussed in more detail below. For each dimension, we provide a specific description of that attribute for ACG-PM.

Conceptual Basis

Dozens of predictive models have been developed and are commercially available to medical managers, actuaries, and other healthcare professionals to predict future costs and identify the highest opportunity patients for care management and other intensive population-based interventions. Most of these PMs, however, are (unfortunately) not based on any conceptual model related to the patient, provider, and health system factors that determine health need, demand, and supply. Because PMs are increasingly being used as supplemental decision tools in the care of high cost patients, to be meaningful to medical managers and health professionals, every PM should be grounded in a strong conceptual framework. For example, ACG-PM is based on a rich and comprehensive set of multi-morbidity clinical frameworks (the logic that classifies diagnostic codes to

ADGs and EDCs, the logic that forms ACG categories, and the logic that classifies pharmacy codes to Rx-MGs) that distinguish ACGs from all other risk adjustment methodologies.

Two important problems result from PMs that are purely data-driven. First, without a conceptual basis for the PM, the meaning of the PM output, commonly referred to as a risk score, is unclear. Does the risk refer to future resource use, future health, future specific utilization events such as hospitalization or something else? A second problem with this approach to PM development is that these models do not bridge the financial and clinical enterprises in health care organizations. Although a risk score that is predictive of future costs may be useful to an actuary interested in small group underwriting, the same score has no meaning to health professionals who are more concerned with clinical factors that are responsible for those costs.

An empirically based argument can be developed for using the current year’s costs to predict future costs. In fact, this has been the strategy for many years. In terms of statistical performance, prior cost is a fairly good predictor of future cost, but it has important limitations. First, prior cost has no inherent clinical meaning, and is therefore of no relevance to clinicians who wish to intervene. It is not tied to morbidity and, thereby, cannot be translated into clinical action. Second, prior cost is subject to the phenomenon of regression to the mean; very high costs in one year tend to drift towards average costs in the subsequent year. This occurs because uncommon and high-cost medical events (e.g., a hospitalization) tend not to repeat themselves. Third, prior-use measures are also not appropriate as risk factors for risk-adjusted rate setting or profiling as they potentially could provide incentives to use excessive and inappropriate resources.



The ACG predictive models have comparable, and in many applications, superior, statistical performance compared with prior cost measures. Clinically-based models, such as ACG-PM, produce risk scores that can be adapted for financial applications such as underwriting and actuarial forecasting. Importantly, these risk scores can be decomposed into the clinical cost drivers. Prior cost measures are useful only for financial forecasting and have little to no relevance for the clinical mission of health care organizations.

ACG-PM is grounded in clinical logic. Each of the risk factors in the models is a clinical variable that has meaning to health professionals and when combined produce a risk score that is a robust predictor of future costs, utilization, and health status. Because of this clinical grounding, ACG-PM can provide a common language between financial and clinical managers in healthcare organizations. This is an important innovation for risk adjustment and health care management..

Target Population

PMs can be developed for a general population (generic approach) or those distinguished by age, sex, disease category, or some other personal characteristic (categorical approach). A significant advantage of the generic approach is that the same set of predictors is used for all modeling applications, which allows one to compare cost drivers across groups. Categorical model development, however, is optimized for sub-groups within a population defined by disease class or age group, for example. Greater predictive accuracy may be achieved for sub-groups if model risk factors are specifically selected for patients within the sub-group. Whether this holds true statistically is unclear and requires further investigation.

ACG-PM applies to a general population, which is consistent with the whole-person orientation of the entire ACG system. However, ACG-PM can be optimized for disease or age groups by fitting the model (i.e., establishing new weights for the risk factor set) for a sub-population of interest. With the availability of laboratory and patient-reported outcomes it seems likely that optimal performance of models will entail more customization to the target population.

Our research has shown that the primary difference between age groups in predictive modeling applications was the distribution of morbidity. Nonetheless, there is a need for a small number of age-specific conditions (e.g., Alzheimer’s disease occurs almost exclusively in older patients, while pregnancy and delivery is a condition of younger women) in PMs that use a generic approach. To address this need, ACG-PM includes all conditions that are important predictors of future health and resource use for any age group. However, for age-specific applications, different weights are attached to the predictors to reflect that the conditions or other clinical variables have different levels of influence on future health and healthcare by age group. (For example, pregnancy as a risk factor would get a weight of zero for an over 65 population.) In effect, ACG-PM is calibrated differently by age with age-specific weights attached to the predictors. The same type of calibration can be done for patients with specific diseases.



Statistical Modeling Approach

Several types of statistical approaches can be used to develop predictive models. Here we summarize the most common.

Algebraic .

The simplest approach is to use current cost data or a count of conditions (or some other algebraically computed clinical marker) as a predictor of future cost. The model might add additional factors such as the rate of medical inflation and, perhaps, demographics such as age and gender. Such models are not well suited to individual predictions and have no inherent clinical meaning. They are also subject to regression to the mean (i.e., the natural tendency of groups of individuals who are high cost one year to move towards mean costs in the following years). Furthermore, the incorporation of prior use potentially conflates demand with the actual need for health care services, which makes such models particularly unsuited for profiling applications in which the goal is to assess the match between services delivered and services needed.

Linear Regression .

Linear regression using ordinary least squares parameter estimation is widely used and provides fairly stable estimates. The individual predictions have an intuitive appeal, because they can readily be translated into dollar values. However, cost data do not fulfill the assumptions for these models (which are best suited for data following a normal distribution with common variance across the variable range) but the models are sufficiently robust that some departures from the assumptions can be tolerated. Because it is important to be cautious about extreme values, or outliers, that could dramatically alter the fitting of the regression line, linear models frequently employ truncation or top coding to minimize outlier effects.

More sophisticated generalized linear models that use transformations of the dependent variable and assume a non-normal distribution of error terms have been used in some predictive modeling applications. The superiority of these alternative models compared with standard ordinary least squares estimation has not been demonstrated as yet. Our work suggests, for example, that generalized linear models using a log link function and a gamma distribution of error terms provide PM estimates that are no better, and in many cases inferior to, standard linear regression methods. Thus, ACG-PM was developed using a standard linear regression approach with ordinary least squares estimation in order to provide risk factor weights (the beta coefficients from the regression equations) with intuitive, real-dollar meaning.

Logistic Regression

.

This regression approach is applied to dichotomous outcomes events such as whether someone is hospitalized or is in a high-cost group (e.g., in the top five percent of the highest cost persons in the population). Logistic regression assumes a specific functional form between predictors and the outcome (a logit relationship) that may not be observed in health care resource use applications. The approach also attenuates differences between cases because the risk output is a predicted probability that ranges between zero and one.



Neural Networks

.

These modeling strategies (also referred to as artificial intelligence approaches) represent collections of mathematical models that attempt to emulate the processes observed in biological nervous systems, including the capacity to adapt and learn from prior experiences or observations. They are fundamentally atheoretical and lack a logical clinical or conceptual model. Neural networks require no specification of the nature of the relationship between predictors and the outcome in advance. An optimally fitted model is created as part of an iterative statistical fitting or learning process. Neural networks are computationally intense, can be difficult to understand, and every solution is unique (which can be a problem for models used in rate setting or comparing data across populations). From the perspective of either empirical accuracy or performance, neural network models have yet to demonstrate any advantage over those developed using linear regression methods.

Data Inputs

One of the main challenges facing PM development is the limited risk factor data on which the models can be based. Currently, most PMs depend on standard medical claims

(age, sex, diagnoses, procedures, outpatient medications, service dates, and costs) available from administrative billing records. ACG-PM uses age, sex, diagnostic codes, medication codes, and optional summary cost variables.

For a given patient, all demographic, diagnostic, and pharmaceutical information available in administrative claims records is used by ACG-PM assignment algorithms.

Alternatively, there are PM approaches that organize care into a number of discrete timedelimited episodes of care. While episode approaches have some appeal to those who envision the care process in these terms, commonly available claims data used for predictive modeling are encouraged as encounters rather than as episodes. This requires episode-based approaches to use complex decision rules to assign services, medications, and diagnostic information to a unique time-dependent cluster, called the episode-of-care.

These algorithms reject large numbers of claims during the assignment process, effectively leaving a substantial amount of clinical data on “the cutting room floor.”

Thus, the clinical picture created by episode of care PMs is incomplete, which contrasts with the use-every-clinical-datum approach of the ACG system. Further, episode-based approaches encourage a disease by disease approach to the care process which, as we have noted above, is best characterized by the simultaneous presence of multiple morbidities.

Several data sources that offer exciting new avenues for model development will soon be available: clinical information from electronic health records; physiological outcome data from laboratory assessments; patient-reported psychosocial outcomes; and, background socio-demographic information. These additional data sources will stimulate development of new generations of PMs that are more individualized to each patient’s context and needs. With the limited source of risk factor data available to the current generation of PMs, model predictive capabilities are limited as well. All predictive models are decision tools that must be used with good clinical and managerial judgment and augmented with other sources of information.



Outcomes Assessed

ACG-PM can predict a large range of financial and clinical outcomes, including:

• Total healthcare costs

• Pharmacy costs

• Outpatient costs (and sub-components, e.g., imaging, physician, laboratory, specialty,

ED)

• Inpatient hospitalization

• ICU admissions

• Physician visits

• Specialty referrals

• Morbidity events (such as acute complications of chronic disease)

• Mortality

Optimal performance of ACG-PM occurs when weights for each of the risk factors are obtained from models that employ the same outcome as that which is being predicted. If the outcome is risk of mortality, using risk factor weights obtained from a test model that regress the occurrence of death on the full set of risk factors will give the most accurate prediction results, and would be superior to utilization-based weights. In this way ACG-

PM can be customized to the outcome of interest.

Development of ACG Predictive Models

The ACG system includes two types of predictive models, Dx-PM and Rx-PM, which differ according to the data inputs. The first is based on diagnostic codes (i.e., ICD9-CM,

ICD-10, ICD-10-CM) and the second is built with medication codes (i.e., NDC codes,

ATC Codes). Both are excellent predictive models for forecasting healthcare costs and identifying high-risk patients. In this section, we describe the clinical basis and development of both model types. The section concludes with a discussion of a combined model, DxRx-PM, that includes both diagnostic and medication code inputs.

Dx-PM: A Diagnosis-Defined Predictive Model

The ACG System’s Dx-PM is a predictive model constructed from diagnoses given to patients in all inpatient and outpatient clinical settings, age, and sex. The Dx-PM modeling strategy makes use of the comprehensive array of metrics available within the entire ACG risk adjustment system. The sole data inputs to the model are age, sex, and diagnostic codes. The set of risk factors that constitute Dx-PM is shown pictorially in

Figure 3: Dx-PM Risk Factors

, and is discussed in detail below.


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Figure 3: Dx-PM Risk Factors

Predicting Future Resource Use

Age and gender are included to assess age-related and gender-based health needs.

Overall morbidity burden is measured using the ACG categorization of morbidity burden. The variable in the model includes three groupings of low resource intensity

ACG categories and 24 individual ACGs that are the most resource intensive morbidity groups.

High-impact chronic conditions were selected for inclusion in the predictive model because when they are present they have a high impact on resource consumption. They are common chronic conditions that are associated with greater than average resource consumption, uncommon diseases with high impact on both cost and health, complications of chronic disease that signify high disease severity (e.g., diabetic retinopathy), or conditions that are a major biological influences on health status (e.g., transplant status, malignancy). Only conditions for which the evidence linking health care to outcomes is strong were included in the model. A sub-set of the ACG system’s

Expanded Diagnostic Clusters (EDCs) is used to identify the high impact conditions in the model.

Hospital dominant condition markers are based on diagnoses that, when present, are associated with a greater than 50 percent probability among affected patients of hospitalization in the next year. All these diagnoses are setting-neutral, i.e., they can be given in any inpatient or outpatient face-to-face encounter with a health professional.

The variable is a count of the number of morbidity types (i.e., ADGs) with at least one hospital dominant diagnosis.



Development of Rx-PM

The first PM that the ACG team developed was based entirely on diagnostic codes, age, and sex (Dx-PM). Although Dx-PM was successfully used in many health care organizations, it was noted that in many cases the ACG system user had access to

NDC/ATC codes, but no ICD information. Some plans had medication data only and others wanted to identify high risk patients (and enroll them proactively in care management programs) before the six months needed to assign Dx-PM had elapsed. Rx-

PM was developed to address these needs.

Incorporating medication data into ACG-PM presented several new opportunities for our development efforts. First, medication-based predictive modeling can be done before the full set of ICD codes are obtained, which is the case for new enrollees in a healthcare organization, for example. We? believe that a thorough clinical history and recording of medications during a single encounter may be sufficient to assign an Rx-PM risk score.

Second, medication data captures a unique constellation of clinical information that overlaps, although not entirely, with diagnostic codes. The combination of the two data sources complement one another, particularly when the outcome of interest is total health care costs, and offer the promise of superior PM performance. Third, we sought to create a clinically-based, medication-defined PM that comprised a set of risk factors that group medications according to the types of morbidity that they are commonly used to treat.

The ACG Team was unaware of any Rx-based PM that used this clinically oriented approach, which we feel is one of the more important innovations presented by the Rx-

PM. Some medication-defined PMs group a sub-set of medication codes into disease entities, whereas other medication-defined PMs are no more than medication therapeutic classes. Both approaches fail to fully capture the unique attributes of morbidity conveyed by medication data, which is the clinical innovation offered by Rx-PM.

Several challenges are posed by medication-only PMs. The sheer volume of drug codes

(there are now over 100,000 NDCs, and over 5,000 fourth and Fifth Level ATC codes) makes creating a streamlined set of risk factors highly challenging. Moreover, medication use is not synonymous with presence of specific disease. One medication can be used to treat many diseases, and one disease often has many medication options for its management. This relationship between medication and disease entities convinced us that an NDC / ATC-based PM should be based on a new way of classifying morbidity-one that captures the unique clinical information embedded in medication use data--rather than an approach that attempts to attach disease labels to medications. Because risk scores based entirely on NDC / ATC codes are subject to artificial inflation associated with excess and inappropriate medication usage, any NDC / ATC grouping algorithm underpinning a PM should be developed in a way to blunt this potential circularity problem.



Rx-PM Score

Rx-PM produces a score that is the sum of the Rx-MG, active ingredient count, age, and sex risk factor weights. Each Rx-MG is entered into the model as an indicator (yes / no) variable. An Rx-MG is either turned on or off, regardless of the number of medications that may be prescribed within that category or the number of times each medication may have been prescribed. This all-or-none attribute of the Rx-MGs minimizes the potential bias introduced by excessive prescribing or medication changing within therapeutic classes, leading to spuriously high risk scores. A prior cost variable can be added as a risk factor and is considered a measure of demand for services not captured by NDC codes. This strategy maximizes model parsimony and clinical utility of the predictor variables while optimizing statistical performance.

DxRx-PM: Combined ACG Predictive Model

When both diagnostic and medication codes are available for a patient population, a model that combines Dx-PM and Rx-PM can be used. We call this combined model

DxRx-PM. This model includes all the Dx-PM and Rx-PM predictors along with the appropriate prior cost variable. Combining these risk factors is clinically logical because the morbidity information obtained from diagnostic codes complements that which is obtained from medication codes. In effect, the combined model provides the fullest clinical picture of the patient’s health needs and demand for care that is possible using administrative data.

The innovation that the Dx-PM, Rx-PM, and DxRx-PM present is their unique clinical logic coupled with their excellent statistical performance. Each employs risk factors that were created using clinical frameworks that make sense to health professionals and medical managers. As we will show in the next section, each has superlative statistical performance. The combination of clinical logic with excellent financial predictions provides a common language that can link medical and financial managers in health care organizations.

Resource Bands

The software incorporates both prior total cost and prior pharmacy cost bands into the

ACG predictive models. They are a useful adjunct to analysts wishing to stratify their populations.

Possible values include:

• 0 – 0 or no pharmacy costs

• 1 – 1-10 percentile







•

•

•

6 – 91-93 percentile




High, Moderate, and Low Impact Conditions

Within the detailed EDC and Rx-MG output from the system as well as in the Patient

Clinical Profile Report, the EDCs and Rx-MGs associated with an individual are categorized as High, Moderate, or Low impact. The definition for these categories is based on the individual contribution of the condition to the predictive modeling score.

The specific criteria are as follows:

• High Impact EDC: Coefficient >1 in the non-elderly Dx-PM without prior cost predicting total cost model

• Moderate Impact EDC: Coefficient between 0.1 and 1.0 in the non-elderly Dx-PM without prior cost predicting total cost model

•

•

Low Impact EDC: Coefficient less than 0.1 or not included in the non-elderly Dx-

PM without prior cost predicting total cost model

• High Impact Rx-MG: Coefficient >1 in the non-elderly Rx-PM without prior cost predicting total cost model

Moderate Impact Rx-MG: Coefficient between 0.1 and 1.0 in the non-elderly Rx-PM without prior cost predicting total cost model

• Low Impact Rx-MG: Coefficient less than 0.1 in the non-elderly Rx-PM without prior cost predicting total cost model

Table: 3 Impact Type lists the EDCs and Rx-MGs in each impact type category.

Table 3: Impact Type

Impact Type

High

EDCs

ADM03 Transplant status

ALL06 Disorders of the immune system

CAR05 Congestive heart failure

CAR13 Cardiac arrest, shock

END08 Type 1 diabetes, w/o complication

END09 Type 1 diabetes, w/ complication

EYE13 Diabetic retinopathy

GAS02 Inflammatory bowel disease

GAS12 Chronic pancreatitis

GTC01 Chromosomal anomalies

HEM05 Aplastic anemia

HEM07 Hemophilia, coagulation disorder

INF04 HIV, AIDS

INF08 Septicemia

MAL02 Low impact malignant neoplasms


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Moderate



MAL03 High impact malignant neoplasms

MAL04 Malignant neoplasms, breast

MAL06 Malignant neoplasms, ovary

MAL07 Malignant neoplasms, esophagus

MAL08 Malignant neoplasms, kidney

MAL09 Malignant neoplasms, liver and biliary tract

MAL10 Malignant neoplasms, lung

MAL11 Malignant neoplasms, lymphomas

MAL12 Malignant neoplasms, colorectal

MAL13 Malignant neoplasms, pancreas

MAL15 Malignant neoplasms, stomach

MAL16 Acute leukemia

MAL18 Malignant neoplasms, bladder

MUS16 Amputation status

NUR08 Multiple sclerosis

NUR09 Muscular dystrophy

NUR12 Quadriplegia and paraplegia

NUR18 Cerebral palsy

REC03 Chronic ulcer of the skin

REN01 Chronic renal failure

RES03 Cystic fibrosis

RES09 Tracheostomy

RHU05 Rheumatoid arthritis

TOX04 Complications of mechanical devices

ALL04 Asthma, w/o status asthmaticus

ALL05 Asthma, with status asthmaticus

CAR03 Ischemic heart disease (excluding acute myocardial infarction)

CAR04 Congenital heart disease

CAR06 Cardiac valve disorders

CAR07 Cardiomyopathy

CAR09 Cardiac arrhythmia

CAR10 Generalized atherosclerosis

CAR12 Acute myocardial infarction

CAR14 Hypertension, w/o major complications

CAR15 Hypertension, with major complications

END02 Osteoporosis

END06 Type 2 diabetes, w/o complication

END07 Type 2 diabetes, w/ complication

EYE03 Retinal disorders (excluding diabetic retinopathy)

GAS04 Acute hepatitis

GAS05 Chronic liver disease

GAS06 Peptic ulcer disease

GAS11 Acute pancreatitis

GSI08 Edema

GSU11 Peripheral vascular disease

GSU13 Aortic aneurysm

GSU14 Gastrointestinal obstruction/perforation

GUR04 Prostatic hypertrophy

HEM01 Other Hemolytic Anemias

HEM03 Thrombophlebitis

HEM06 Deep vein thrombosis

HEM09 Sickle Cell Disease

MAL14 Malignant neoplasms, prostate

MUS03 Degenerative joint disease

MUS10 Fracture of neck of femur (hip)



Low

The Johns Hopkins ACG ®

MUS14 Low back pain

NUR03 Peripheral neuropathy, neuritis

NUR05 Cerebrovascular disease

NUR06 Parkinsons disease

NUR07 Seizure disorder

NUR11 Dementia and delirium

NUR16 Spinal cord injury/disorders

NUR17 Paralytic syndromes, other

NUR19 Developmental disorder

NUT02 Nutritional deficiencies

PSY01 Anxiety, neuroses

PSY02 Substance use

PSY05 Attention deficit disorder

PSY07 Schizophrenia and affective psychosis

PSY08 Personality disorders

PSY09 Depression

PSY12 Bipolar disorder

REC01 Cleft lip and palate

REN02 Fluid/electrolyte disturbances

REN03 Acute renal failure

REN04 Nephritis, nephrosis

RES02 Acute lower respiratory tract infection

RES04 Emphysema, chronic bronchitis, COPD

RES10 Respiratory failure

RHU01 Autoimmune and connective tissue diseases

ADM02 Surgical aftercare

ADM05 Administrative concerns and non-specific laboratory abnormalities

ADM06 Preventive care

ALL01 Allergic reactions

ALL03 Allergic rhinitis

CAR01 Cardiovascular signs and symptoms

CAR08 Heart murmur

CAR11 Disorders of lipid metabolism

CAR16 Cardiovascular disorders, other

DEN01 Disorders of mouth

DEN02 Disorders of teeth

DEN03 Gingivitis

DEN04 Stomatitis

EAR01 Otitis media

EAR02 Tinnitus

EAR03 Temporomandibular joint disease

EAR04 Foreign body in ears, nose, or throat

EAR05 Deviated nasal septum

EAR06 Otitis externa

EAR07 Wax in ear

EAR08 Deafness, hearing loss

EAR09 Chronic pharyngitis and tonsillitis

EAR10 Epistaxis

EAR11 Acute upper respiratory tract infection

EAR12 ENT disorders, other

END03 Short stature

END04 Hypothyroidism

END05 Other endocrine disorders

EYE01 Ophthalmic signs and symptoms

EYE02 Blindness

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EYE04 Disorders of the eyelid and lacrimal duct

EYE05 Refractive errors

EYE06 Cataract, aphakia

EYE07 Conjunctivitis, keratitis

EYE08 Glaucoma

EYE09 Infections of eyelid

EYE10 Foreign body in eye

EYE11 Strabismus, amblyopia

EYE12 Traumatic injuries of eye

EYE14 Eye, other disorders

EYE15 Age-related macular degeneration

FRE01 Pregnancy and delivery, uncomplicated

FRE02 Female genital symptoms

FRE03 Endometriosis

FRE04 Pregnancy and delivery with complications

FRE05 Female infertility

FRE06 Abnormal pap smear

FRE07 Ovarian cyst

FRE08 Vaginitis, vulvitis, cervicitis

FRE09 Menstrual disorders

FRE10 Contraception

FRE11 Menopausal symptoms

FRE12 Utero-vaginal prolapse

FRE13 Female gynecologic conditions, other

FRE14 Pregnancy with termination

GAS01 Gastrointestinal signs and symptoms

GAS03 Constipation

GAS07 Gastroenteritis

GAS08 Gastroesophageal reflux

GAS09 Irritable bowel syndrome

GAS10 Diverticular disease of colon

GAS13 Lactose intolerance

GAS14 Gastrointestinal/Hepatic disorders, other

GSI01 Nonspecific signs and symptoms

GSI02 Chest pain

GSI03 Fever

GSI04 Syncope

GSI05 Nausea, vomiting

GSI06 Debility and undue fatigue

GSI07 Lymphadenopathy

GSU01 Anorectal conditions

GSU02 Appendicitis

GSU03 Benign and unspecified neoplasm

GSU04 Cholelithiasis, cholecystitis

GSU05 External abdominal hernias, hydroceles

GSU06 Chronic cystic disease of the breast

GSU07 Other breast disorders

GSU08 Varicose veins of lower extremities

GSU09 Nonfungal infections of skin and subcutaneous tissue

GSU10 Abdominal pain

GSU12 Burns--1st degree

GTC02 Inherited metabolic disorders

GUR01 Vesicoureteral reflux

GUR02 Undescended testes

GUR03 Hypospadias, other penile anomalies

GUR05 Stricture of urethra



The Johns Hopkins ACG ®

GUR06 Urinary symptoms

GUR07 Other male genital disease

GUR08 Urinary tract infections

GUR09 Renal calculi

GUR10 Prostatitis

GUR11 Incontinence

GUR12 Genito-urinary disorders, other

HEM02 Iron deficiency, other deficiency anemias

HEM04 Neonatal jaundice

HEM08 Hematologic disorders, other

INF01 Tuberculosis infection

INF02 Fungal infections

INF03 Infectious mononucleosis

INF05 Sexually transmitted diseases

INF06 Viral syndromes

INF07 Lyme disease

INF09 Infections, other

MAL01 Malignant neoplasms of the skin

MAL05 Malignant neoplasms, cervix, uterus

MUS01 Musculoskeletal signs and symptoms

MUS02 Acute sprains and strains

MUS04 Fractures (excluding digits)

MUS05 Torticollis

MUS06 Kyphoscoliosis

MUS07 Congenital hip dislocation

MUS08 Fractures and dislocations/digits only

MUS09 Joint disorders, trauma related

MUS11 Congenital anomalies of limbs, hands, and feet

MUS12 Acquired foot deformities

MUS13 Cervical pain syndromes

MUS15 Bursitis, synovitis, tenosynovitis

MUS17 Musculoskeletal disorders, other

NEW01 Newborn Status, Uncomplicated

NEW02 Newborn Status, Complicated

NEW03 Low Birth Weight

NEW04 Prematurity

NEW05 Disorders of Newborn Period

NUR01 Neurologic signs and symptoms

NUR02 Headaches

NUR04 Vertiginous syndromes

NUR10 Sleep problems

NUR15 Head injury

NUR20 Central nervous system infections

NUR21 Neurologic disorders, other

NUR22 Migraines

NUT01 Failure to thrive

NUT03 Obesity

NUT04 Nutritional disorders, other

PSY03 Tobacco use

PSY04 Behavior problems

PSY06 Family and social problems

PSY10 Psychologic signs and symptoms

PSY11 Psychosocial disorders, other

REC02 Lacerations

REC04 Burns--2nd and 3rd degree

REN05 Renal disorders, other

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Impact Type

High



RES01 Respiratory signs and symptoms

RES05 Cough

RES06 Sleep apnea

RES07 Sinusitis

RES08 Pulmonary embolism

RES11 Respiratory disorders, other

RHU02 Gout

RHU03 Arthropathy

RHU04 Raynauds syndrome

SKN01 Contusions and abrasions

SKN02 Dermatitis and eczema

SKN03 Keloid

SKN04 Acne

SKN05 Disorders of sebaceous glands

SKN06 Sebaceous cyst

SKN07 Viral warts and molluscum contagiosum

SKN08 Other inflammatory conditions of skin

SKN09 Exanthems

SKN10 Skin keratoses

SKN11 Dermatophytoses

SKN12 Psoriasis

SKN13 Disease of hair and hair follicles

SKN14 Pigmented nevus

SKN15 Scabies and pediculosis

SKN16 Diseases of nail

SKN17 Other skin disorders

SKN18 Benign neoplasm of skin and subcutaneous tissues

SKN19 Impetigo

SKN20 Dermatologic signs and symptoms

TOX01 Toxic effects of nonmedicinal agents

TOX02 Adverse effects of medicinal agents

TOX03 Adverse events from medical/surgical procedures

Rx-MGs

ALLx040 Allergy/Immunology / Immune Disorders

ALLx050 Allergy/Immunology / Transplant

CARx010 Cardiovascular / Chronic Medical

CARx020 Cardiovascular / Congestive Heart Failure

CARx050 Cardiovascular / Vascular Disorders

ENDx030 Endocrine / Diabetes With Insulin

ENDx060 Endocrine / Growth Problems

GASx020 Gastrointestinal/Hepatic / Chronic Liver Disease

GASx030 Gastrointestinal/Hepatic / Chronic Stable

GASx050 Gastrointestinal/Hepatic / Pancreatic Disorder

GSIx040 General Signs and Symptoms / Severe Pain

GURx020 Genito-Urinary / Chronic Renal Failure

HEMx010 Hematologic / Coagulation Disorders

INFx030 Infections / HIV/AIDS

INFx050 Infections / Severe Acute Major

MALx010 Malignancies

MUSx020 Musculoskeletal / Inflammatory Conditions

NURx020 Neurologic / Chronic Medical

PSYx020 Psychosocial / Addiction

RESx020 Respiratory / Chronic Medical

RESx030 Respiratory / Cystic Fibrosis

TOXx010 Toxic Effects/Adverse Effects / Acute Major



Moderate

Low

ALLx030 Allergy/Immunology / Chronic Inflammatory

CARx030 Cardiovascular / High Blood Pressure

CARx040 Cardiovascular / Disorders of Lipid Metabolism

ENDx010 Endocrine / Bone Disorders

ENDx040 Endocrine / Diabetes Without Insulin

ENDx050 Endocrine / Thyroid Disorders

ENDx070 Endocrine / Weight Control

EYEx030 Eye / Glaucoma

FREx020 Female Reproductive / Infertility

FREx030 Female Reproductive / Pregnancy and Delivery

GASx010 Gastrointestinal/Hepatic / Acute Minor

GASx040 Gastrointestinal/Hepatic / Inflammatory Bowel

Disease

GASx060 Gastrointestinal/Hepatic / Peptic Disease

GSIx010 General Signs and Symptoms / Nausea and

Vomiting

GSIx020 General Signs and Symptoms / Pain

GURx010 Genito-Urinary / Acute Minor

INFx010 Infections / Acute Major

INFx040 Infections / Tuberculosis

MUSx010 Musculoskeletal / Gout

NURx010 Neurologic / Alzheimers Disease

NURx030 Neurologic / Migraine Headache

NURx040 Neurologic / Parkinsons Disease

NURx050 Neurologic / Seizure Disorder

PSYx010 Psychosocial / Attention Deficit Hyperactivity

Disorder

PSYx030 Psychosocial / Anxiety

PSYx040 Psychosocial / Depression

PSYx050 Psychosocial / Acute Minor

PSYx060 Psychosocial / Chronic Unstable

RESx040 Respiratory / Airway Hyperactivity

SKNx030 Skin / Chronic Medical

ALLx010 Allergy/Immunology / Acute Minor

EARx010 Ears, Nose, Throat / Acute Minor

ENDx020 Endocrine / Chronic Medical

EYEx010 Eye / Acute Minor: Curative

EYEx020 Eye / Acute Minor: Palliative

FREx010 Female Reproductive / Hormone Regulation

GSIx030 General Signs and Symptoms / Pain and

Inflammation

INFx020 Infections / Acute Minor

RESx010 Respiratory / Acute Minor

SKNx010 Skin / Acne

SKNx020 Skin / Acute and Recurrent

ZZZx000 Other and Non-Specific Medications

7-23



High Pharmacy Utilization Model

Traditional predictive modeling focuses on using diagnoses and/or pharmacy information to predict future expenditures. Current models work well and have provided a means of identifying individuals at risk for future high pharmacy expenditures. However, a limitation of the current approach is that these models, by definition, have tended to identify a multi-morbid population whose high use of pharmacy services may well be justified; or, more specifically, their disease profile warrants the high use of medications.

The High Pharmacy Utilization model is specifically designed to address this shortcoming and is focused on predicting the subset of the multi-morbid population who are consuming drugs above and beyond what might be anticipated based on their morbidity burden.

High Pharmacy Utilization Model Outputs

Probability of Unexpected Pharmacy Cost

The probability of unexpected pharmacy cost is a numerical probability score representing the result of applying weights from a logistic regression model on the development data set predicting individuals with moderate or high morbidity with unusually large pharmacy expenditures. The model independent variables include age, gender, ACG categories, and select high impact conditions defined by EDCs, Rx-MGs, hospital dominant conditions, and frailty. The markers in the model are the same as the

DxRx-PM model markers without prior cost. In the development data set individuals were deemed to have moderate or multi-morbidity if they were assigned to a moderate or higher resource utilization band and they were considered to have unusually large pharmacy expenditures if their standardized pharmacy expenditures were more than 1.75 standard deviations from their predicted amount based on diagnoses information 2 .

High Risk for Unexpected Pharmacy Cost

High Risk for Unexpected Pharmacy Cost is a binary flag that indicates individuals with a Probability of Unexpected Pharmacy Cost greater than 0.4.

2 This is a laymen’s interpretation of applying methodologies outlined in Gray Woodal’s 1993 article “The

Maximum Size of Standardized and Internally Studentized Residuals in Regression Analyses”.



Relevant to Clinicians and Case Managers

Objectives for success for the High Pharmacy Utilization Model included PPV/SENS and

ROC statistics similar to traditional predictive modeling strategies; however, to warrant inclusion of an additional model in the ACG suite of predictive models the High

Pharmacy Utilization Model needed to identify a different group of individuals than traditional predictive modeling methods.

Table 4: Overlap of the High Pharmacy

Utilization Model and Dx-PM (Top 1.5% of Scores) summarizes the overlap of the

High Pharmacy Utilizaton Model and Dx-PM to identify high pharmacy users with moderate morbidity burden or higher and indicates that the overlap between the two models was only 16%. In short, the High Pharmacy Utilization Model is identifying a new group of patients not identified by our prior methods.

Table 4: Overlap of the High Pharmacy Utilization Model and Dx-PM

(Top 1.5% of Scores)

Identified by

High

Pharmacy

Utilization

Model only

Identified by

Dx-PM only

Identified by

Both High

Pharmacy

Utilization and

Dx-PM Total

Total

20,722

42.07%

20,709

42.05%

7,822

15.88%

49,253

100.00%

There are a variety of reasons why this population subgroup may be of interest and explanations as to why pharmacy utilization might be higher than expected. Sometimes pharmacy costs are higher because of the use of an expensive medication. Other times unanticipated high drug costs may be related to quality issues (i.e., poor or uncoordinated care). Unexpected high pharmacy utilization can also be a result of both cost and quality issues. These can range from poor data (i.e., missing ICD codes), to poor or inadequate care and/or gaps in care. Other times it is simply a matter of polypharmacy and/or patient abuse. The hope is that this model can help clinical or case managers to:

• Better identify those at risk for future high pharmacy utilization

• Provide information about what you can do to improve outcomes and/or reduce cost for those at risk including:

− Providing better coordination

− Refining the drug regimen

− Other interventions yet to be identified.

The ACG Team looks forward to feedback on the success of this model and will continue to explore the methodologies introduced here in anticipation of additional predictive models for target populations.





Predictive Modeling Statistical Performance 8-i

8 Predictive Modeling Statistical Performance

Overview of ACG® Predictive Models Statistical Performance .............. 8-1

Validation Data ............................................................................................. 8-1

Adjusted R-Square Values ........................................................................... 8-1

Table 1: Predictive Models Evaluated ...................................................... 8-2

Table 2: Proportion of Variance Explained (Adjusted R-Square

Values) Using Year 1 Risk Factors and Year 2 Resource Use

Measures for Alternative Predictive Models .............................................. 8-3

Table 3: Proportion of Variance Explained (Adjusted R-Square

Values) Using Year 1 Risk Factors and Year 2 Resource Use

Measures for Prior Cost and ICD- and NDC- Based Combined Models ... 8-5

Interpreting Adjusted R-Square Values ..................................................... 8-6

Predictive Ratios ........................................................................................ 8-6

Table 4: Predictive Ratio by Year-1 Cost Quintile for Commercial and Medicare Populations .......................................................................... 8-7

Sensitivity and Positive Predictive Value .................................................... 8-8

Table 5: Sensitivity and PPV for Top Five Percent .................................. 8-9

ROC Curve .................................................................................................. 8-10

Table 6: C-Statistics ................................................................................ 8-10

Clinical Information Improves on Prior Cost .......................................... 8-11

Figure 1: Venn Diagram Comparing Overlap of Prior, Predicted, and Actual High Cost Users ..................................................................... 8-12


8-ii Predictive Modeling Statistical Performance



Predictive Modeling Statistical Performance 8-1

Overview of ACG

®

Predictive Models Statistical Performance

This section demonstrates the ACG ® predictive models statistical performance while describing the various ways in which they can be applied in health care applications.

In developing the ACG Predictive Modeling suite, the goal was to attain the highest level of statistical performance as measured by commonly used benchmarks such as the percent of variation in cost explained by the model (R-Squared statistic) while retaining the underlying clinical sense and ease of use that are hallmarks of the ACG System.

Validation Data

A very large longitudinal claims database, licensed from PharMetrics, a unit of IMS,

Watertown, MA, was used for the ACG modeling efforts. The database is generally representative of the U.S. commercially insured population. Two populations were used for modeling: (1) commercial (all ages to 65 years old) and (2) Medicare (United States federal program that pays for certain health care expenses for people aged 65 or older)

Advantage (Medicare managed care). Two years of data for the period 2008-2009 were used. Only persons with at least six months of enrollment in year one and one month of enrollment in year two were included. For the pharmacy model, the population was restricted to include enrollees that had both pharmacy and medical eligibility. The full database was used for development and 2007-2008 data was used for validation.

Adjusted R-Square Values

The conventional measure of model performance is the R-Squared statistic. This statistic measures how well the model fits the data and has become a standard measure of performance, especially among underwriters and actuaries who must price products across a range of populations. Comparative performance statistics have been calculated for the models outlined in

Table 1: Predictive Models Evaluated

.

.


8-2 Predictive Modeling Statistical Performance

Table 1: Predictive Models Evaluated

Risk Model Description

Age and Sex

Age, Sex, and ADG

Prior Cost

ACG DxRx-PM

Charlson Index

ACG Dx-PM

Chronic Disease Score

ACG Rx-PM

Simple demographic model

Simple demographic model augmented with ADGs, the ACG building blocks

Total Cost or Pharmacy Cost as appropriate

ICD- and NDC-based predictive model

17 ICD-based high impact conditions

ICD-based predictive model

29 NDC-based disease-oriented markers

NDC-based predictive model

Generally where the data are available it is recommended that a combined model, the

DxRx-PM, be implemented as it takes advantage of all available data streams. Whether or not to include prior cost as a predictor in any model is a more difficult decision.

Including prior cost has the impact of increasing explained variance. Therefore, for financial models the choice is easy, incorporate prior cost. If your focus is explained variance, then including prior cost increases explanatory power. However, having the highest R-Square value does not necessarily always equate to the optimal methodology.

For example, for care management applications it is less clear that using prior cost offers clear advantages. Prior high cost users may or may not be amenable to disease or casemanagement intervention programs. In contrast, individuals identified as high risk using only diagnosis or pharmacy data (and excluding prior cost information), are oftentimes better candidates for such intervention programs. Therefore, for care management purposes, diagnosis or pharmacy based (or combination models) may provide the best performance. More information on model performance under particular circumstances is presented below.

The results of this comparative assessment in both commercial and Medicare populations are shown in

Table 2: Proportion of Variance Explained (Adjusted R-Square

Values) Using Year 1 Risk Factors and Year 2 Resource Use Measures for

Alternative Predictive Models for diagnosis-based and pharmacy-based models.



Table 2: Proportion of Variance Explained (Adjusted R-Square Values) Using Year 1 Risk Factors and Year 2 Resource Use Measures for Alternative Predictive Models

Commercially Insured Enrollees Medicare Beneficiaries

Predictive

Model

Age, Sex

Total

Costs

5%

Pharmacy

Costs

7%

Physician

Visits

5%

ICD / Diagnostic Code Based Models

Age, Sex,

ADG

ACG

Age, Sex,

Charlson

Disease

Indicators

Dx-PM

17%

16%

13%

21%

20%

17%

17%

29%

25%

22%

10%

23%

Predictive

Model

Age, Sex

Total

Costs

1%

Pharmacy

Costs

0%

Physician

Visits

0%

ICD / Diagnostic Code Based Models

Age, Sex,

ADG

ACG

Age, Sex,

Charlson

Disease

Indicators

Dx-PM

11%

10%

12%

16%

7%

5%

7%

10%

25%

22%

11%

26%


8-4

Commercially Insured Enrollees

Predictive

Model

Total

Costs

Pharmacy

Costs

Physician

Visits

NDC / Medication Code Based Models

Age, Sex,

Chronic

Disease

Score

Rx-PM

16%

19%

Notes:

Total costs were truncated at $50,000 and pharmacy costs at $20,000.

For each resource use measure, the highest R2 value is highlighted. Data are from IMS Health

Incorporated.

35%

44%

12%

17%

Predictive Modeling Statistical Performance

Medicare Beneficiaries

Predictive

Model

Total

Costs

Pharmacy

Costs

Physician

Visits

NDC / Medication Code Based Models

Age, Sex,

Chronic

Disease

Score

Rx-PM

10%

12%

Notes:

Total costs were truncated at $50,000 and pharmacy costs at

$20,000. For each resource use measure, the highest R2 value is highlighted. Data are from IMS Health

Incorporated.

23%

27%

13%

16%



Dx-PM is the best performer across populations in terms of predicting total costs. Not surprisingly, Rx-PM is a much better predictor of pharmacy costs.

Where the models are combined we see the best performance of all. For these models we chose to present results from models that both include and exclude prior cost information as it is a powerful predictor of future cost. As can be seen by the results summarized in

Table 3: Proportion of Variance Explained (Adjusted R-Square Values) Using Year

1 Risk Factors and Year 2 Resource Use Measures for Prior Cost and ICD- & and

NDC- Based Combined Models

, prior cost gives a boost to explaining pharmacy costs but appears to have little impact on estimates of total cost. This option is available for both Dx-PM alone and Rx-PM alone.

Table 3: Proportion of Variance Explained (Adjusted R-Square

Values) Using Year 1 Risk Factors and Year 2 Resource Use Measures for Prior Cost and ICD- and NDC- Based Combined Models

Commercially Insured Enrollees Medicare Beneficiaries

Predictive Model

Combined Models

DxRx-PM w/out Prior

Costs

DxRx-PM with Prior

Costs

Total

Costs

24%

26%

Pharmacy

Costs

46%

57%


Combined Models

DxRx-PM w/out Prior

Costs

DxRx-PM with Prior

Costs

Total

Costs

Pharmacy

Costs

19%

19%

28%

38%

Notes:

Total costs were truncated at

$50,000 and pharmacy costs at

$20,000. For each resource use measure, the highest R2 value is highlighted. Data are from IMS

Health Incorporated.

Notes:

Total costs were truncated at

$50,000 and pharmacy costs at

$20,000. For each resource use measure, the highest R2 value is highlighted. Data are from IMS

Health Incorporated.



Interpreting Adjusted R-Square Values

For predictive modeling of future high resource use, we are focused on the small segment of highly co-morbid patients. This represents a very small portion of the range of values being fitted to the regression. A model could perform well for the bulk of the population, but perform poorly for these especially risky individuals (typically a small group) and still provide credible R-Square performance statistics. It is also possible to precisely fit models to any dataset by adding more variables and manipulating these variables mathematically to assume a perfect form which could be logarithmic, exponential, quadratic, or some other transformation. This, in essence, is what happens when advanced neural networking approaches are used. The problem with such approaches is two fold. First, the more convoluted the model, the less sense it will convey to clinicians and other decision makers who are supposed to take action. Second, highly fitted models will change with the dataset, often from year to year, which can introduce considerable instability. Instability in terms of payment applications can adversely impact healthcare organizations and care providers. Instability in terms of performance assessment will undermine credibility among those being assessed.

Predictive Ratios

The credibility of models does not solely rest on regression fit. Regression provides scores related to individuals while most applications of predictive modeling relate to populations of individuals. To determine how well predictions perform in estimating cost across the full range of risk, we also recommend looking at persons in different prior cost cohorts (we use quintiles). Prior to the advent of diagnosis-based predictive models, actuaries often relied on the age and gender distribution of the population as the basis for making financial predictions. The next analysis examines predictive ratios for an age/gender model compared to the suite of ACG predictive models and is summarized in

Table 4: Predictive Ratio by Year-1 Cost Quintile for Commercial and Medicare

Populations

.

Predictive ratios represent the ratio of the expected cost predicted by particular modeling strategies to the observed year two costs. The goal is to approach 1.0 as closely as possible, especially in the high cost quintile. Ratios less than 1.0 mean that the model under-predicts future costs while ratios over 1.0 suggest that the model is over-predicting costs. The following table shows these ratios for predicting total cost for Dx-PM, Rx-

PM, and DxRx-PM. These results are contrasted to a model that that relies solely on age and gender as inputs.



Table 4: Predictive Ratio by Year-1 Cost Quintile for Commercial and

Medicare Populations

Model

Quintile

Predictive Ratio,

Commercial

(quintile=180,800)


Medicare

(quintile=18,700)

Age and Gender

1

2

3

4

5

2.72

1.66

1.17

0.89

0.49

3.35

2.09

1.50

1.05

0.48

Dx-PM w/o Prior

Cost

1

2

3

4

5

1.45

1.31

1.20

1.06

0.84

1.34

1.15

0.99

0.97

0.92

Rx-PM w/o Prior

Cost

1

2

3

4

5

1.56

1.33

1.19

1.09

0.82

1.56

1.29

1.11

1.01

0.77



Model

Quintile


Commercial

(quintile=180,800)


Medicare

(quintile=18,700)

DxRx-PM w/o

Prior Cost

1

2

3

4

5

1.14

1.12

1.11

1.07

0.92

1.13

1.10

1.00

1.00

0.95

There is a tendency for all of the models to somewhat overestimate costs among the lowest cost quintile while somewhat underestimate costs in the highest cost quintile. This effect is most pronounced in the age and gender model. The diagnosis- and pharmacybased models all substantially outperform age and gender. The performance of the combined model is exemplary and especially so for Medicare, yielding predictions close to 1.0 in the three highest cost groups. The diagnostic model performs a little better than the pharmacy-based model, especially for Medicare. If the predicted cost is pharmacy cost, the effectiveness of the Rx-PM model dramatically improves. We chose not to use any model that includes prior cost in these comparisons since using prior cost as the basis for forming the quintiles will favorably bias the results.

Sensitivity and Positive Predictive Value

An increasingly important use of the ACG ® Suite of Predictive Models has been for high risk case identification. For this application, models are used more like a diagnostic test and performance is measured at how well true cases are identified and false positives ignored. The focus tends to be on two key indicators, sensitivity, and positive predictive value.

The computational approach for these indicators is as follows:

•

•

Sensitivity

= True Cases Identified/All True Cases In Population

Positive Predictive Value (PPV)

= True Cases Identified/(True Cases Identified +

False Positives)

Sensitivity is likely to be of greatest interest to epidemiologists and others who are focused in the health of the population since they are considering how well the test captures all of the high risk individuals in a population.

Positive predictive value will likely be of greatest interest to clinicians/care managers who want to know the likelihood that a particular patient is actually high risk.



In evaluating model performance in terms of PPV and sensitivity, the choice of cut points can be very important. The cut point defines the percentage threshold one sets for defining someone as a high cost case (i.e., defining some with the disease of interest). If the cut point for high cost was set to 1%, which bounds the population of interest (true positives) to a relatively small group of very costly individuals, misidentification of cases is likely to be much higher than if the cut point is set to define a larger high risk pool.

Setting a cut point of 5%, for example, will improve sensitivity and PPV by roughly 10 percentage points over an approach using the 1% cut point. Decisions about cut points can be as much economic as they are statistical. If the costs of identifying cases are high

(e.g., identifying persons who will receive one-on-one case management) then a more restrictive cut point is probably the best approach. When screening for large populationbased disease management initiatives, threshold of 5% or even larger may be appropriate.

In the largest populations, distinctions between persons at the 1% and 5% thresholds are likely to blur, they will all tend to be very co-morbid.

Selected model performance for commercial and Medicare populations in terms of their diagnostic capabilities is presented in

Table 5: Sensitivity and PPV for Top Five

Percent

.

Table 5: Sensitivity and PPV for Top Five Percent

Sensitivity/PPV Population Percent

Predictive Model (Predicting Total Cost)

Prior Cost

Age and Gender

Dx-PM w/o Prior Cost

Rx-PM w/o Prior Cost

DxRx-PM w/o Prior Cost

Commercial Medicare

35

20

33

34

37

26

12

28

23

24

Notes:

The true positive rate is the same as the sensitivity and positive predictive value, because the risk score cut-point is set equal to the outcome cut-point (in this example, this is the top 5%).

In this example, the model to beat is prior cost which is a strong predictor of future cost.

None of the ACG models presented here used prior cost as an independent variable although the use of prior cost in our models tends to improve predictive performance.

The ACG models are all strong performers, exceeding the performance of prior cost models under some circumstances. It is important to note that the cases identified by the

ACG models are not always the same as those identified by the prior cost model. They were selected using clinical criteria and are usually much more actionable than those selected with prior cost alone.



Prior cost is a good predictor of future cost and for high risk case identification has been one of the anchors for case selection in the absence of other information. However, prior cost has a number of notable limitations. First, it provides no information on the clinical condition of the patients. Further, some prior cost cases will revert to lower costs in the subsequent year because the period of instability and medical crisis has passed. This phenomenon is referred to as regression to the mean. Selection based upon diagnostic and pharmacy codes, while still susceptible to this phenomenon, is more likely to yield appropriate cases for care management.

ROC Curve

In addition to sensitivity and specificity in assessing the value of a predictive model as a diagnostic test, there is a measure of model fit for case identification, the Receiver

Operating Characteristics Curve or ROC. The ROC was originally developed in World

War II to evaluate the performance of radar units, addressing the fact that as the sensitivity or gain of a test is raised, there is a greater likelihood to introduce false positives or noise. The C-Statistic is a measure of the fit where models produce the greatest signal in relation to noise. A C-Statistic of 0.5 indicates that true cases are indistinguishable from false positives (or a model no better than chance). A C-Statistic of at least 0.8 is widely accepted as a threshold for good test performance. For models predicting total cost with a 5% cut point, (i.e., top 5% of actual year two costs defines high risk), C-statistics are summarized in

Table 6: C-Statistics

.

.

Table 6: C-Statistics

C-Statistic

Model Commercial Medicare

Dx Alone

Dx + Total Cost

Rx Alone

Rx + Rx Cost

Combined Alone

Combined + Total Cost

.820

.833

.797

.802

.830

.835

.755

.760

.718

.719

.764

.765

Model performance overall for the commercial models generally exceeds the 0.8 threshold. One would expect somewhat better performance from the commercial models because the commercial validation dataset is 10 times larger than the Medicare dataset.

The inclusion of prior cost data in the models adds a slight performance boost. Pharmacy data perform well in predicting future high cost users even where the predicted metric is total cost and not pharmacy cost.



Clinical Information Improves on Prior Cost

Some of the challenges of using predictive models for case identification are expressed in the Venn diagram

(Figure 1: Venn Diagram Comparing Overlap of Prior, Predicted, and Actual High Cost Users

on the next page. The three ellipses cover those who are high cost in the prior year, those who the model predicts as being high cost, and those who turn out to be high cost. The G represents a particularly intriguing group since it includes those who are actually high risk and who have been solely identified by the predictive model and, therefore, would have been missed if only prior cost was used. If all the available data is being effectively used, then the area covered by H should be minimized.



Figure 1: Venn Diagram Comparing Overlap of Prior, Predicted, and

Actual High Cost Users

Actual High Cost Year-1

(Prior Use)

Predicted High Risk in

Year-2 (Using Year-1 Data)

A

Not High Risk

B

D

C

E

G

F

H

Actual High

Cost Year-2

High Risk,

Current Costs

Low, Future

Costs High

Some summary statistics can be applied that can help to capture the essence of this diagram. Two perspectives on gauging model performance were taken with respect to this diagram. First, how well does using both ACG predictive models and prior cost do in identifying true high cost cases, the areas covered by D, E, and G in the diagram? Our analyses indicate that by using both approaches one can identify about 45% of true cases where high risk cases are defined as those in the highest 5% of total costs in year two.

Second, we focused on just the G portion of the chart, those cases that were uniquely identified by ACG predictive models. If you used both prior cost and an ACG predictive model as the basis for identifying potentially high risk cases, then the ACG System uniquely identifies around 20% of the patients. In other words, ACG predictive modeling identifies individuals who would NOT have been identified if only a prior cost model had been used. This is an important population for case managers as they represent a highly actionable group of patients, those who are currently not in the highest cost category.

The needs of these case managers might be best met by continuing to use prior cost as one level of screening but to also apply the pure diagnostic (or pharmaceutical) predictive models, where ACG predictive models can provide clinical context for all those cases that they identify. This will leave Group D, those cases only identified by prior cost, for whom there will be no additional clinical detail.


Predicting Hospitalization 9-i

9 Predicting Hospitalization

Introduction ................................................................................................... 9-1

Framework for Predicting Hospitalization Using ACG ............................ 9-1

Why Predict Hospitalization ...................................................................... 9-1

What Hospitalizations are we Predicting ................................................... 9-1

Figure 1: Typology of Acute Care Inpatient Hospitalization .................... 9-2

How is ACG Used to Predict Hospitalization ............................................ 9-2

Figure 2: Schematic Framework for Predicting Hospitalization ............... 9-3

Why Include Utilization in Prediction Models for Hospitalization ........... 9-3

Utilization Markers .................................................................................... 9-3

Likelihood of Hospitalization ..................................................................... 9-5

Empiric Validation of the Likelihood of Hospitalization Model .............. 9-6

Sensitivity and Positive Predictive Value .................................................. 9-6

Table 1: PPV and Sensitivity for Predicting Hospitalization .................... 9-6

C-Statistic ................................................................................................... 9-7

Table 2: C-Statistics for Predicting Hospitalization .................................. 9-7


9-ii


Predicting Hospitalization


Predicting Hospitalization 9-1

Introduction

This chapter introduces the ACG ® model for predicting hospitalization. It describes the framework for predicting hospitalization and concludes with an empiric validation of the model.

Framework for Predicting Hospitalization Using ACG

Why Predict Hospitalization

The ability to predict hospitalizations with some level of accuracy will help improve the allocation of healthcare resources, in budgetary systems and in systems that link financial incentives to efficiency and quality of care.

Inpatient care is part of the healthcare system in every country. A model-based screening strategy can be of value in general for identifying high risk individuals for interventions that are designed to improve population health.

Inpatient care is more resource intensive compared to other types of healthcare. Hospital financing in the U.S. takes the efficiency and quality of inpatient care into consideration.

One particular area of interest related to quality is the rate at which patients are readmitted within a short period after a prior hospitalization. A tool for identifying individuals at risk for re-admission is valuable in this context.

What Hospitalizations are we Predicting

Hospitalizations for childbirth can be anticipated with near certainty. Other hospitalizations are predictable based on prior medical history and a variety of other factors. Our efforts are directed at inpatient hospitalizations that are predictable with a degree of uncertainty. We hypothesize that unanticipated hospitalizations have a potential for being predictable from individual morbidity data. For example, hospitalizations that are clinically attributable to congestive heart failure can have a medical history leading up to the event.

Unanticipated hospitalizations may also have an external cause such as poisonings and injuries suffered in accidents. These hospitalizations are an additional focus of our predictive modeling.

Figure 1: Typology of Acute Care Inpatient Hospitalization gives an overview over a guiding typology of unanticipated hospitalizations.

The hospitalization prediction model differs from the Hospital Dominant Condition marker. Hospital Dominant Conditions represent a very small subset of diagnoses associated with very high rates of admission in the following 12 months (See Chapter 6, for more information). The Hospital Dominant Conditions contribute significantly ACG-

PM, cost predictions. Hospital Dominant Conditions have high Positive Predictive Value

(PPV) but lack sensitivity in identifying the full pool of patients at risk for hospitalization. The Hospitalization Prediction models use ACG- defined co-morbidity and previous utilization (See

Figure 2: Schematic Framework for Predicting

Hospitalization

below) to identify risk of hospitalization across the entire population.


9-2 Predicting Hospitalization

Figure 1: Typology of Acute Care Inpatient Hospitalization

UNANTICIPATED HOSPITALIZATION

N O EXTERNAL CAUSE

Examples:


Elective joint replacement surgery (Arthritis)

W ITH E XTERNAL C AUSE

Examples:

Injuries (due to accidents)

Poisonings

ANTICIPATED HOSPITALIZATION

Example:

Childbirth

How is ACG Used to Predict Hospitalization

There is a conceptual link and an empiric correlation between predicting resource use and predicting inpatient hospitalization. The ACG predictive model framework, first introduced in the chapter on predicting resource use, also applies to the prediction of hospitalization. As mentioned in the earlier chapter, optimal performance of an ACG predictive model occurs when the weighting of risk factors employs the outcome that is being predicted. This fact motivates the formulation of a predictive model that is aimed at hospitalization events.

Figure 2: Schematic Framework for Predicting

Hospitalization

presents a schematic of the ACG framework for predicting hospitalization.

A tiered approach to making predictions about hospitalizations has been adopted. We make predictions about future hospitalization based on whether a person had a hospitalization in the prior time period. Persons who have had a prior hospitalization are thought to have an increased likelihood of hospitalization in a subsequent time period. In the absence of a prior hospitalization, a secondary criterion for making predictions about hospitalization relates to the age of a person. Older persons are thought to have an increased likelihood of hospitalization compared to younger individuals. The ACG

System approach uses age 55 as the threshold for separating the two age groups.

The Johns Hopkins ACG ® System, Version 10.0 Understanding the ACC Markers Manual


Figure 2: Schematic Framework for Predicting Hospitalization

9-3

Why Include Utilization in Prediction Models for Hospitalization

The ACG predictive model framework has been amended to improve the prediction of hospitalization events. The framework for predicting hospitalization includes certain utilization markers which represent a plausible set of valid predictors. For example, emergency care and inpatient hospitalization can be precursors to future inpatient hospitalization. This chapter introduces the utilization markers that are included in the predictive model framework for hospitalization events.

Utilization Markers

Several utilization markers support the calculation of hospitalization models. These markers require detailed dates, place of service, procedures, and revenue codes in the

Medical Services Input File. If the required fields are absent, the markers will not be calculated. Alternatively, these markers can be drawn from the patient file to support the hospitalization models.

Dialysis Service

Dialysis service is a binary flag to indicate that a patient with chronic renal failure (EDC

REN01) has received at least one dialysis service during the observation period.



Nursing Service

Nursing service is a binary flag to indicate that a patient has used services in a nursing home, domiciliary, rest home, assisted living, or custodial care facility during the observation period.

Major Procedure

Major procedure is a binary flag attached to a patient to indicate that a major procedure was performed in an inpatient setting during the observation period. This marker considers several Current Procedural Terminology (CPT) 1 and Healthcare Common

Procedure Coding System (HCPCS) codes as major procedures, according to the

Berenson-Eggers Type of Service (BETOS) classification system.

The BETOS classification system consists of readily understood clinical categories and is widely used by the U.S. Centers for Medicare & Medicare Services (CMS), the Medicare

Payment Advisory Commission, and many health services researchers and organizations as the standard classification system for types of services.

Active Cancer Treatment

Active Cancer Treatment is a binary flag attached to a patient to indicate that chemotherapy and/or radiation therapy was performed during the observation period.

This marker is useful in the context of measuring hospitalization risk as hospitalization rates for cancer patients are highest during the active treatment phase.

Emergency Visit Count

This count is an integer count of emergency room visits that did not lead to a subsequent acute care inpatient hospitalization during the observation period. This marker considers place of service, procedure code, and revenue code to identify emergency room visits.

Inpatient Hospitalization Count

The inpatient hospitalization count is an integer count of inpatient confinements during the observation period. The count of inpatient hospitalizations excludes admissions with a primary diagnosis for pregnancy, delivery, newborns, and injuries. Transfers made within and between providers count as a single hospitalization event. This marker is dependent upon revenue codes and dates of service.

Outpatient Visit Count

The outpatient visit count is an integer count of outpatient encounters with place of service of physician office (11), outpatient hospital (22), and other (24, 25, 26, 50, 53, 60,

62, 65, 71, 72) place of service codes. Visits are counted a maximum of once per date of service and provider.

1 CPT codes copyright 2009 American Medical Association. All rights reserved. CPT is a trademark of the

AMA.



Likelihood of Hospitalization

Hospitalization prediction is a refinement to the ACG predictive models specifically calibrated to identifying patients with risk of future hospitalization. The hospital prediction models focus on unanticipated hospitalizations and are complementary to the

ACG cost prediction models.

There are five predictive model outputs related to the likelihood of hospitalization. These models are intended to be used for the indicated outcome. A value of providing multiple model outputs is greater sensitivity of each model calibrated to a particular outcome, as compared to using a single model.

These are logistic models that generate a probability score indicating the likelihood of a future hospitalization event. These models incorporate the utilization markers of inpatient hospitalizations, emergency department visits, outpatient visits, dialysis services, nursing services, and major procedures, in addition to traditional ACG predictive modeling variables. Hospitalization models require the Medical Services Input

File and optionally include the Pharmacy Input File when available. The model outputs are discussed below.

Probability IP Hospitalization Score

Probability IP hospitalization score is the probability score for an acute care inpatient hospital admission within the 12 months subsequent to the observation period.

Probability IP Hospitalization Six Months Score

The probability IP hospitalization six months score is the probability score for an acute care inpatient hospital admission within the six months subsequent to the observation period.

Probability ICU Hospitalization Score

The probability ICU hospitalization score is the probability score for an Intensive Care

Unit or Critical Care Unit admission within the 12 months subsequent to the observation period.

Probability Injury Hospitalization Score

The probability injury hospitalization score is the probability score for an injury-related admission within the 12 months subsequent to the observation period.

Probability Extended Hospitalization Score

The probability extended hospitalization score is the probability score for being admitted to an acute care hospital for 12 or more days (across one or more admissions) within the

12 months subsequent to the observation period.



Empiric Validation of the Likelihood of Hospitalization Model

A longitudinal healthcare claims database, licensed from Phar-Metrics, a unit of IMS,

Watertown, MA, was used for the ACG model development and validation. The database is generally representative of the U.S. insured population in commercial and

Medicare managed care plans. Data for the period 2008-2009 were used for model development, and data for the period 2007-2008 were used for model validation. Persons included in the analysis had at least six months of enrollment in the first year and one or more months of enrollment in the second year. The validation database included 3.5 million persons under age 65 and 463,000 Medicare (United States federal program that pays for certain health care expenses for people aged 65 or older) eligible beneficiaries.

Sensitivity and Positive Predictive Value

An important use of the ACG Predictive Models is for high risk case identification. For this application, model performance is measured by how well true cases are identified and false positives are avoided. The focus is on two key indicators, sensitivity and positive predictive value:

Sensitivity

= True Cases Identified / All True Cases In Population

Positive Predictive Value

= True Cases Identified / (True Cases Identified + False

Positives)

Sensitivity measures how well the model captures all hospitalized individuals in a population. Positive predictive value measures the likelihood that a particular patient is being hospitalized in the next time period.

Table 1: PPV and Sensitivity for Predicting Hospitalization illustrates, using sensitivity and positive predictive value measures, that the ACG ® models are strong performers compared to using prior cost alone. The top 5% of hospitalization probability scores define cases that are identified.

Table 1: PPV and Sensitivity for Predicting Hospitalization

Predictive Model Positive Predictive Value Sensitivity

IP Hospitalization with prior cost and diagnosis and pharmacy data input

IP Hospitalization with prior cost and diagnosis data input

Prior Cost

(as a benchmark to the IP

Hospitalization model)

21.2%

20.8%

14.2%

33.3%

32.6%

22.4%



C-Statistic

The C-Statistic is a measure of model fit. Chapter 8 on Predictive Modeling Statistical

Performance describes the relationship between this summary measure and the area under the ROC curve. A C-Statistic of 0.5 indicates that true cases are indistinguishable from false positives, or in other words, the test model is no better than tossing a fair coin. A

C-Statistic of 0.8 is widely accepted as a threshold for good model performance.

C-Statistics for models that predict hospitalization are summarized in

Table 2:

C-Statistics for Predicting Hospitalization

. The table presents results for each of the models and population groups included in the tiered approach to making predictions about hospitalization. The predictive model is IP Hospitalization with predictors that are based on prior cost and diagnosis and pharmacy data.

Table 2: C-Statistics for Predicting Hospitalization


Persons with Prior

Hospitalization

Persons Age

55 or older

Persons Age less than 55

IP Hospitalization

IP Hospitalization Six Months

ICU Hospitalization

Injury Hospitalization

Extended Hospitalization

.751

.754

.805

.808

.842

.718

.728

.754

.748

.793

The C-Statistics for predicting hospitalization in

Table 2: C-Statistics for Predicting

Hospitalization are above 0.7 for most outcomes and groups. Several plausible patterns emerge from the empiric validation. First, ACG better predicts hospitalization events for persons who had a hospitalization within the previous 12 months, compared to persons who did not. Second, ACG better predicts specialized hospitalization events, compared to a more general hospitalization event. This finding validates the fact that optimal performance of an ACG predictive model occurs when the weighting of risk factors employs the outcome that is being predicted.

.741

.747

.757

.668

.721


9-8




Coordination 10-i

10 Coordination

Introduction ................................................................................................. 10-1

Assessing Care Coordination ..................................................................... 10-1

Table 1: Face-to-Face Physician Visits ................................................... 10-3

Table 2: Eligible Specialties .................................................................... 10-4

Table 3: Specialties not Eligible .............................................................. 10-5

Table 4: Generalist Specialties ................................................................ 10-5

Majority Source of Care (MSOC) Marker ............................................... 10-6

Figure 1: Majority Source of Care .......................................................... 10-6

Figure 2: Distribution of the Majority Source of Care in a Large,

Commercial Managed Care Population ................................................... 10-7

Management Visit Count ............................................................................ 10-8

Unique Provider Count (UPC) Marker .................................................... 10-8

Figure 3: Unique Provider Count ............................................................ 10-8

Figure 4: Distribution of the Unique Provider Count within a Large,

Commercial Managed Care Population ................................................... 10-9

Specialty Count (SC) Marker .................................................................. 10-10

Figure 5: Specialty Count ...................................................................... 10-10

Figure 6: Distribution of the Specialty Count in a Large, Commercial

Managed Care Population ...................................................................... 10-11

Generalist Seen (GS) Marker ................................................................... 10-12

Figure 7: Generalist Seen ...................................................................... 10-12

Risk of Coordination ................................................................................. 10-13

Table 5: Coordination Risk ................................................................... 10-13

Figure 8: Impact of Coordination on High Morbidity Users ................ 10-14

Figure 9: Impact of Coordination on ED Use by Asthma Patients ....... 10-15

Conclusion .................................................................................................. 10-15


10-ii


Coordination


Coordination 10-1

Introduction

The Johns Hopkins ACG ® System has introduced ACG ® Coordination Markers in order to identify populations that are at risk for poorly coordinated care. With the ACG System

10.0, we have also introduced a three level coordination score that can be used to group patients based on their likelihood of having coordination issues. Used by themselves or in conjunction with other special population markers (See the


,

Chapter 6), this set of markers adds another dimension so as to enhance the clinical screening process. The basic premise behind the creation of ACG Coordination Markers is that individuals receiving poorly coordinated care have worse clinical outcomes and have higher medical expenses than individuals who are being provided coordinated care.

Assessing Care Coordination

Up to now, care coordination has been assessed by instruments that have not been able to use administrative claims information. Traditional coordination assessment approaches have been survey based targeting the patient, family member’s or providers perceptions of care collaboration. Chart reviews have also been used to assess the information exchanged between physicians, teamwork processes, and performance. Finally, resources and structures that support care coordination have also been used to indirectly evaluate coordination. Surveys, chart reviews, and resource information are not readily available for coordination assessment, making it challenging to incorporate this dimension of care into the clinical screening process.

Using only administrative claims information, ACG Coordination Markers are able to assess whether an individual is at risk for receiving poorly coordinated care. The following four patient markers make up the ACG Coordination Markers:

• Majority Source of Care (MSOC):

This marker provides an assessment of the level of participation of those providers that provided care to each patient.

• Unique Provider Count (UPC):

This marker provides a count of the number of unique providers that provided care to the patient.

• Specialty Count (SC):

This marker provides a count of the number of specialty types (not the same as number of specialists seen) that provided care to the patient.

• Generalist Seen (GS):

This marker indicates a generalist’s participation in a patient’s care.

A coordination risk combines these markers to determine whether a person has a likely, possible, or unlikely coordination issue.


10-2 Coordination

The ACG Coordination Markers consist of the following data sources:

• Current Procedural Technology (CPT) 1 (Procedure Code field of the Medical

Services Input File) codes provide standard Evaluation and Management procedures.

• National Provider Identifier (NPI) or National Uniform Claim Code Committee

(NUCC) taxonomy codes 2 (ProviderSpecialty_NPI field of the Medical Services

Input File) and / or the U.S. Centers for Medicare and Medicaid Services (CMS) specialty codes provide standardized definitions for provider specialties.

Each of the ACG Coordination Markers limits the evaluation to outpatient face-to-face physician visits as defined by CPT Evaluation and Management Codes.

1 CPT codes copyright 2009 American Medical Association (AMA). All rights reserved. CPT is a trademark of the AMA.

2 Provider Taxonomy copyright 2009 American Medical Association on behalf of the NUCC. All rights reserved. Current code lists are available at http://www.nucc.org/


Coordination 10-3

Table 1: Face-to-Face Physician Visits

CPT Codes Defining Face-to-Face

Physician Visit

99341

99342

99343

99344

99345

99347

99348

99349

99218

99219

99220

99241

99242

99243

99244

99245

99350

99354

99355

99374

99375

99377

99378

99379

99204

99205

99211

99212

99213

99214

99215

99217

92002

92004

92012

92014

99201

99202

99203

CPT Codes Defining Face-to-Face

Physician Visit

99391

99392

99393

99394

99395

99396

99397

99401

99380

99381

99382

99383

99384

99385

99387

99402

99403

99404

99411

99412

99420

99429

99455

99456

99499


10-4 Coordination

The Majority Source of Care (MSOC), Unique Provider Count (UPC), and Specialty

Count (SC) markers further consider only those visits provided by eligible specialties

(i.e., specialties that could reasonably manage the overall care of a patient). Examples of eligible specialties are provided in

Table 2

:

Eligible Specialties

.

Table 2: Eligible Specialties

Eligible Specialties

Allergy & Immunology.

Colon & Rectal Surgery

Family Medicine

Internal Medicine

Neurological Surgery

Neuro-musculoskeletal Medicine

Nuclear Medicine

Obstetrics & Gynecology

Ophthalmology

Oral & Maxillofacial Surgery

Orthopedic Surgery

Otolaryngology

Pain Medicine

Pediatrics

Physical Medicine & Rehabilitation

Plastic Surgery

Preventive Medicine

Psychiatry and Neurology

Radiology

Surgery

Thoracic Surgery (Cardiothoracic Vascular Surgery)

Transplant Surgery

Urology

Advanced Practice Midwife

Clinical Nurse Specialist

Nurse Practitioner

Physician Assistant


Coordination 10-5

Face-to-face visits with provider specialties that are not eligible are not considered as part of the Majority Source of Care (MSOC), Unique Provider Count (UPC), and Specialty

Count (SC) markers. Examples of specialties that are not considered eligible are provided in

Table 3: Specialties not Eligible

.

Table 3: Specialties not Eligible

Specialties not Eligible

Ambulance Services .

Agencies

Dental Providers

Dermatologists

Dietary & Nutritional Service Providers

Facilities (Clinics, Ambulatory Health Centers, Emergency Departments,

Hospitals, Assisted living facilities)

Laboratories

Managed Care Organizations

Pharmacy Service Providers

Podiatrists

Psychologists

Social Service Providers

Speech, Language and Hearing Service Providers

Suppliers of Durable Medical Equipment & Supplies

Therapists (Physical – Respiratory)

Technicians

A subset of provider specialties defines generalists. Patients with at least one outpatient face-to-face visit to a generalist will have the Generalist Seen marker set to Y. Patients without outpatient face-to-face visits to a generalist will have the Generalist Seen marker set to N.

Table 4: Generalist Specialties

provides the list of generalist specialties for this marker.

Table 4: Generalist Specialties

Generalist Specialties

Family Medicine .

Internal Medicine

Geriatricians

Pediatricians

Preventive Medicine

Nurse Practitioners

Obstetrics and Gynecology


10-6 Coordination

Majority Source of Care (MSOC) Marker

The Majority Source of Care marker will determine the percent of the outpatient visits provided by eligible physicians that saw the member most over the measurement period.

For each patient, the application determines the following:

•

•

The percentage of care provided from a majority source.

The provider ID that is responsible for the majority source of care. In the event that more than one provider is assigned the same MSOC percentage, the application will identify all the providers as being the majority source of care.

Figure 1: Majority Source of Care

shows two patients. The patient on the left saw three eligible providers and received eight of his 10 outpatient services from the geriatrician. In this case, the geriatrician is identified as the MSOC provider with a score of 0.80 (80%). The patient on the right saw four eligible providers and received four of her outpatient services from the endocrinologist. The endocrinologist provider is identified as the MSOC with a score of 0.40 (40%).

Figure 1: Majority Source of Care


Coordination 10-7

Figure 2: Distribution of the Majority Source of Care in a Large, Commercial

Managed Care Population shows the characteristics of Majority Source of Care assignments in a large commercial managed care population. Most (38%) of the patients in this population see a single physician for all of their care. This would be expected for a relatively healthy working age population. Roughly 40% of patients have a physician who handles at least 50% of their visits but not all of their visits. This leaves a relatively small segment whose care is distributed more evenly among a group of physicians.

These patients are likely to be those who have more co-morbidities, are likely to be more expensive, and who are more prone to coordination issues.

Figure 2: Distribution of the Majority Source of Care in a Large,

Commercial Managed Care Population

Majority Source of Care

40.00%

35.00%

30.00%

25.00%

20.00%

15.00%

10.00%

5.00%

0.00%

0.00% 0.61%

5.31%

9.38%

6.43%

20.38%

11.35%

4.49%

3.30%

0.32%

38.41%

9

MSOC Range


10-8 Coordination

Management Visit Count

The denominator of the majority source of care percent is a management visit count.

This count represents the total number of face-to-face visits with an eligible specialty.

Unique Provider Count (UPC) Marker

The Unique Provider Count (UPC) marker determines the number of unique eligible providers that imparted outpatient care over the measurement period for any condition.

In

Figure 3: Unique Provider Count

the patient on the left saw three eligible providers giving him a UPC of 3. The patient on the right saw four eligible providers and received a UPC of 4.

Figure 3: Unique Provider Count


Coordination 10-9

The prevalence of different levels of unique providers seen is show in

Figure 4:

Distribution of the Unique Provider Count within a Large, Commercial Managed

Care Population

. Consistent with

Figure 2: How Majority Source of Care is

Distributed in a Large Commercial Managed Care Population

, most patients in this relatively healthy population see only one provider. The distribution has a very long tail with nearly 1% seeing nine or more unique physicians. The larger the number of unique providers involved the greater the likelihood of coordination issues.

Figure 4: Distribution of the Unique Provider Count within a Large,

Commercial Managed Care Population


10-10 Coordination

Specialty Count (SC) Marker

The Specialty Count (SC) marker determines the number of eligible specialty types that provided outpatient care over the measurement period for any condition.

In

Figure 5: Specialty Count the patient on the left saw two distinct specialty types.

Geriatricians and internists are both considered generalists so they are only counted once.

The patient on the right saw four eligible providers. This patient received an SC of 4.

Figure 5: Specialty Count

Generalists

Geriatrician Internist

8 visits

I saw 2 Specialty

Types

1 visit

Ophthalmologist

1 visit

I saw 4.

4 visits 3 visits 2 visits 1 visit

Endocrinologist Cardiologist Pulmonologist Neurologist


Coordination 10-11

The distribution of Specialty Count in a large, commercial managed care population is show in

Figure 6: Distribution of the Specialty Count in a Large, Commercial

Managed Care Population

. The results are consistent with the presented in

Figure 4:

Distribution of the Unique Provider Count within a Large, Commercial Managed

Care Population

. Nearly two percent had six or more specialties and represent the population segment most likely to experience coordination issues.

Figure 6: Distribution of the Specialty Count in a Large, Commercial

Managed Care Population


10-12 Coordination

Generalist Seen (GS) Marker

The Generalist Seen (GS) marker determines whether or not a patient has been provided outpatient care by a Generalist in the measurement period.

In

Figure 7: Generalist Seen,

the patient on the left saw a generalist for outpatient care.

The patient on the right did not see a generalist. Analysis of a large, commercial managed care population shows that 76% of all patients see a generalist in a year. The remainder may be at risk for coordination issues.

Figure 7: Generalist Seen


Coordination 10-13

Risk of Coordination

The ACG Coordination Markers can be used together to provide a comprehensive picture of coordination of care. The ACG System assigns patients into three levels indicating the risk for coordination issues based on the grid shown in

Table 5: Coordination Risk

.

Table 5: Coordination Risk

Coordination

Risk

Unique

Provider

Count

Specialty

Count

Majority

Source of

Care

Percent

Generalist

Seen

Likely

Coordination

Issue

Possible

Coordination

Issue

Unlikely

Coordination

Issue

PCI

PCI

UCI

UCI

UCI

PCI

PCI

PCI

PCI

LCI

LCI

LCI

PCI

LCI

LCI

LCI

LCI

2-6

2-6

2-6

2-6

<=1

2-6

2-6

2-6

2-6

>=7

>=7

>=7

>=7

>=7

>=7

>=7

>=7

<5

<5

<5

<5

-

>=5

>=5

>=5

>=5

<5

<5

<5

<5

>=5

>=5

>=5

>=5

<=28

>28

<=28

>28

-

<=28

<=28

>28

>28

>28

>28

<=28

<=28

<=28

<=28

>28

>28

N

N

Y

Y

-

N

Y

N

Y

N

Y

N

Y

N

Y

N

Y


10-14 Coordination

The categorization into Coordination Risk categories was derived empirically based upon the correlation between each marker and the total costs for high morbidity users. A high

Unique Provider Count is the major risk factor for being a potential coordination issue with one exception.

Figure 8: Impact of Coordination on High Morbidity Users

considers only members with high morbidity levels (in Resource Utilization Bands 4 and 5) to control for illness burden. There is a dramatic impact on year one and year two median total costs for members at high risk for poor coordination. Persons who presented a coordination risk carried higher costs into the following year.

Figure 8: Impact of Coordination on High Morbidity Users

Median Total Expenditures

$18,000

$16,000

$14,000

$12,000

$10,000

$8,000

$6,000

$4,000

$2,000

$0

$16,344

$11,636

Likely

Coordination

Issue

$10,250

$7,137

Possible

Coordination

Issue

$7,555

$4,286

Unlikely

Coordination

Issue

Year 1

Year 2


Coordination 10-15

Coordination is also expected to influence aspects of the quality of care provided.

Considering members of a large commercial population with asthma,

Figure 9: Impact of Coordination on ED Use by Asthma Patients shows the percentage of emergency department (ED) visits over a one year period. Patients at the highest level of risk for coordination issues experience twice the percentage of ED visits during the measurement period.

Figure 9: Impact of Coordination on ED Use by Asthma Patients

Percentage of ED Visits by Level of

Coordination Risk

5.1%

10.2%

Likely Coordination

Issue

Possible

Coordination Issue

Unlikely

Coordination Issue

6.0%

Conclusion

The combination of the ACG Coordination Markers provides a meaningful way to identify members at risk for poor coordination. The coordination score provides a quick means for identifying those members most at risk of coordination issues. Members with poorly coordinated care are more likely to have excess utilization as a result of redundant testing, potentially harmful drug-disease interactions and overall lower quality care.

These markers complement the ACG-PM risk scores and additional system markers in further defining the at risk population.


10-16


Coordination


Pharmacy Adherence 11-i

11 Pharmacy Adherence

Objectives ..................................................................................................... 11-1

Significance of Pharmacy Adherence for Effective Care ........................ 11-1

Development of Possession/Adherence Markers ...................................... 11-2

Figure 1: Gap in Medication Possession ................................................. 11-2

Figure 2: Gap in Medication Possession Following Oversupply ............ 11-3

Figure 3: Gap in Medication Possession Following Hospitalization ...... 11-3

Pharmacy Adherence Defined ................................................................... 11-4

Table 1: Conditions that Require Chronic Administration of

Medication ................................................................................................ 11-4

Table 2: Condition-Drug Class Pairings ................................................. 11-5

Table 3: Markers of Pharmacy Adherence / Possession ......................... 11-8

Table 4: MPR Example 1 ...................................................................... 11-10



Table 7: CSA Example 1 ....................................................................... 11-11



Reporting of Possession ......................................................................... 11-12

End of Period Possession ....................................................................... 11-13

Validation and Testing .............................................................................. 11-14

Table 10: Possible Types of Analyses ................................................... 11-14


11-ii


Pharmacy Adherence


Pharmacy Adherence 11-1

Objectives

This chapter is intended to introduce the concept of medication possession and to describe the methods used by the ACG ® System to measure pharmacy adherence. The pharmacy adherence output provides additional criteria for identifying candidates for care management programs, as well as information that can be used to manage patient care.

Significance of Pharmacy Adherence for Effective Care

There is a considerable literature on pharmacy adherence and a substantial body of evidence that high levels of medication adherence yield improved therapeutic outcomes and more cost effective treatment. Medication adherence represents an important dimension of effective disease management.

Medication adherence is one dimension of patient compliance with therapy. Optimally, medication adherence means taking the correct drugs, for the correct indications, at the correct times, at the correct dose, and under the proper conditions for safe and effective use (storage, shelf life, avoiding substances that could affect efficacy, etc.)

The only way to accurately assess medication adherence on all of these dimensions is through direct patient observation, a highly impractical approach. Researchers have developed a number of less intrusive strategies for assessing medication adherence including patient logs, periodic assessments of the patient’s medication supply, and electronic sensors in the medication bottles that record when the bottle was opened.

These approaches constitute indicators of medication adherence, but none fully address all of the dimensions of medication adherence defined above.

A significant amount of medication adherence research relies on information that is reported on pharmacy claims. Claims data provides an indicator of medication possession. The pharmacy claim captures the date that the medication prescription was filled and the number of days supply. There is substantial literature on medication possession that details a number of widely used metrics. For an excellent review, see

Hess et al. (Annals of Pharmacotherapy, 40:1280-1288, 2006). The ACG System pharmacy adherence markers were developed in line with these existing methodologies.


11-2 Pharmacy Adherence

Development of Possession/Adherence Markers

Generally, measurement strategies have targeted specific possession events (i.e., gaps) or average time of possession expressed as a ratio (supply over prescribing period).

The ACG System employs both of these strategies as they address different dimensions of adherence. Gaps in filling medication prescriptions capture acute occurrences and may represent a therapeutically significant event that could be overlooked if only averages were considered. Gaps, if captured on a timely basis, are amenable to care management intervention. Averages represent how well medication is supplied over a span of time (i.e., one year). They are a good summary indicator of possession that can assist in assessing the overall compliance of a patient, but are less amenable to immediate clinical action.

Both approaches follow the same conceptual model.

Figure 1: Gap in Medication

Possession

depicts a gap event:

Figure 1: Gap in Medication Possession

The analysis begins with a prescription for a targeted medication during the time period of interest. The prescription covers a span of time that is defined by the associated days supply. The interval between the end of the days supply of the first prescription and the onset of a new prescription for the same medication represents a gap.

A common approach to gap measurement is to include a grace period at the end of the days supply to ensure that the reported gap is clinically significant. Thus, a patient could be reported as having no gaps in possession, but actually have a number of small gaps that fall within the grace period. In this example, the grace period is 15 days. In most instances, a 15-day grace period has been employed within the ACG System. A 30-day grace period is used when the impact of a longer gap is considered to be minimal or it is necessary to accommodate attributes of dispensing.



Certain events which can complicate the measurement of gaps and potentially introduce false positives are addressed as follows. The first scenario in which a patient renews a prescription before their current supply has been completely exhausted, is presented in

Figure 2: Gap in Medication Possession Following Oversupply

.

Figure 2: Gap in Medication Possession Following Oversupply

Under this circumstance there is still supply on hand when the prescription is refilled.

This surplus is added to the days supply of the refill. The assessment for a gap considers both the surplus and the 15 day grace period.

The second scenario addresses supply on hand if a patient is hospitalized. Customarily, the hospital pharmacy is responsible for all prescriptions during the period of hospitalization, as shown in

Figure 3: Gap in Medication Possession Following

Hospitalization

.

Figure 3: Gap in Medication Possession Following Hospitalization

If a hospitalization occurs during the prescribing period, the supply on hand when hospitalized is presumed to still be available after discharge. This remaining supply plus the 15-day grace period must be exhausted before a gap is counted.



Pharmacy Adherence Defined

Medication possession can have different implications for various conditions and medications. The majority of drugs are given acutely and in these situations, the concept of medication adherence has little relevance. What distinguishes the ACG System pharmacy adherence analyses from other approaches commonly used in the literature or provided by pharmacy benefits managers, is the effort dedicated to identifying conditions and drugs that are intended to be administered chronically. The ACG System measures adherence for 17 conditions where the chronic administration of medication is, in most instances, appropriate. These 17 conditions are listed below:

Table 1: Conditions that Require Chronic Administration of

Medication

• Bipolar Disorder

• Congestive Heart Failure

• Depression

• Diabetes ◄

•

•

•

Glaucoma

Human Immunodeficiency Virus ◄

Disorders of Lipid Metabolism◄

• Hypertension

• Hypothyroidism◄

• Immunosuppression / Transplant

• Ischemic Heart Disease

• Osteoporosis

• Parkinson’s Disease

•

•

•

•

Persistent Asthma

Rheumatoid Arthritis

Schizophrenia

Seizure Disorders

Each targeted condition is associated with one or more target drug classes identified by our clinician advisors as a subset of drugs that once started, should be given continuously.

The resultant condition-drug class pairings are presented in

Table 2: Condition-Drug

Class Pairings

.

Note:

The measurement of adherence is confined to only these condition-drug class pairings. It is possible for patients to take multiple ingredients within the same drug class and / or temporarily substitute one ingredient for another within the drug class. To prevent the appearance of oversupply when multiple ingredients are prescribed, calculations are performed at the ingredient (not drug class) level.



Table 2: Condition-Drug Class Pairings

Condition

Bipolar Disorder

Bipolar Disorder







Depression

Diabetes

Diabetes

Diabetes

Diabetes

Diabetes

Drug Category

Anti-convulsants

Anti-psychotics

ACEI/ARB


Beta-blockers

Diuretics

Inotropic agents

Vasodilators

Anti-depressants

Insulins

Meglitinides

Miscellaneous antidiabetic agents

Non-Sulfonylureas

Other Anti-Hyperglycemic Agents

Condition

Hypertension

Hypertension

Hypertension

Hypertension

Hypertension

Hypertension

Hypothyroidism

Ischemic Heart Disease



Osteoporosis

Parkinson's Disease

Parkinson's Disease

Persistent Asthma

Diabetes

Diabetes



Sulfonylureas

Thiazolidinediones

Glaucoma Ophthalmic glaucoma agents

Human Immunodeficiency Virus HAART* (see below)








Hypertension


ACEI/ARB

Persistent Asthma

Persistent Asthma

Persistent Asthma

Persistent Asthma

Persistent Asthma



Schizophrenia

Seizure Disorder

Immunosuppression/Transplant

*HAART represents a multi-drug cocktail that can be delivered in a number of configurations:

Drug Category



Beta-blockers


Diuretics

Vasodilators

Thyroid drugs

Antianginal agents

Beta-blockers


Hormones



Adrenergic bronchodilators

Immunosuppressive monoclonal antibodies




Methylxanthines

Disease-modifying anti-rheumatic drugs

(DMARDs)

Immunologic agents

Anti-psychotics

Anti-convulsants

Immunologic agents



1.

HAART (a three drug combination filled with a single prescription)

2.

Any NRTI combination drug including either Zidovudine , Abacavir or Tenofovir with an additional NNRTI, entry inhibitor,

Integrase inhibitor or Protease inhibitor

3.

A NNRTI plus a Ritonavir-boosted Protease inhibitor or Nelfinavir

4.

Three of more drugs from two or more different drug classes (Entry Inhibitors, Integrase Inhibitors, NNRTIs, NRTIs, Protease

Inhibitors)



Note:

Pharmacy adherence calculations are ONLY performed for members with a

TRT condition marker for one of the 17 conditions highlighted above. However, the assignment of a TRT condition marker does not guarantee that adherence will be calculated.

To reduce the number of false positives in identifying patients with a given condition, and to avoid misidentification based on drugs that are used to treat more than one condition, the majority of the conditions assessed for a TRT assignment will require an inpatient diagnosis, an emergency department diagnosis or a minimum of two outpatient diagnoses for a condition assignment. Alternately, when medication usage patterns are clear, a minimum of two prescriptions on different dates of service within an applicable drug class can be used to identify a condition (see conditions marked with an arrow in

Table 1: Conditions that Require Chronic Administration of Medication.

The general rules for identifying patients as treated (i.e., TRT) and including them in the adherence calculations are as follows:

•

•

For Diabetes, HIV, Disorders of Lipid Metabolism, and Hypothyroidism, patients may be identified as treated with a minimum of two prescriptions within 120-days of each other (but with different fill dates) in an applicable drug class.

For Persistent Asthma, there must be a minimum of two prescriptions within 120days of each other (but with different fill dates) in an applicable drug class (other than

Leukotriene Modifiers) plus any one of the following criteria:

–

An inpatient discharge with a PRINCIPAL diagnosis of asthma

–

An ER visit with a PRINCIPAL diagnosis of asthma

–

At least four outpatient visits with asthma listed as any diagnosis

–

Two additional asthma medication prescribing events for a total of four asthma prescriptions in the observation period

• All other conditions will be satisfied with a minimum of two prescriptions within

120-days of each other (but with different fill dates) in an applicable drug class plus either a single inpatient diagnosis, single emergency department diagnosis or a minimum of two outpatient diagnoses.

Detailed condition marker definitions are included in Chapter 6 of the

Technical

Reference Guide

.

Note:

There are situations where a member may receive a TRT condition marker, but pharmacy adherence will NOT be assessed. Some examples include:

• The prescribed active ingredient falls within a drug class that can be used to identify that the condition is present, but is not eligible for adherence calculations, i.e., shortacting adrenergic bronchodilators for persistent asthma or short-acting insulins for diabetes.

• The patient did not have at least 120 days supply within a targeted drug class during the observation period, the minimum amount required for the adherence calculations.



• The patient had two prescriptions within the same drug class, but not for the same active ingredient.

Table 3: Markers of Pharmacy Adherence / Possession

Four markers have been adopted for inclusion in the pharmacy adherence module. Each marker represents a slightly different perspective on adherence.

Note that gaps

identify significant intervals during the observation period where there is no medication supply and indicate individual problematic events, while averages

provide a perspective on adherence over time. The averaging and gaps approaches are intended to complement each other. It is possible to have an acceptable average and still experience a major gap in medication supply. Averages can be used to measure adherence at a population level.

Marker

Number of Gaps

Medication Possession Ratio

(MPR)

Continuous Single-Interval

Measure of Medication

Availability (CSA)

Untreated

Definition Application/Interpretation

Count of occurrences where the time interval between the end of supply of one prescription and the onset of the next prescription for the same medication (active ingredient) is more than the grace period.

To be significant, the gap must extend beyond a grace period

(currently 15 or 30 days depending on the condition and drug class pair).

Total number of days for which medication is dispensed

(excluding final prescription) divided by the total number of days between the first and last prescription. If a patient is on multiple medications for a single condition, the days supply and prescribing days are totaled across all and averaged.

Ratio of days supply to days until the next prescription averaged across all prescriptions.

The Medication Possession Ratio

(MPR) represents an average medication possession over the prescribing period. In common usage, an MPR of .80 or higher is considered good adherence. The

MPR can exceed 1.0 if there is excess supply. In general, this measure is more sensitive to large gaps than to frequent gaps.

No current evidence of treatment with a designated class of pharmaceuticals. (Refer to

Chapter 6 of the

Reference Guide

Technical

for Definitions of the Untreated Condition

The Continuous Single Interval

Measure of Medication

Acquisition (CSA), also an average, considers adherence over discrete prescribing events.

This approach equally weights each prescribing event. This metric may also exceed 1.0. In general, this measure is more sensitive to frequent gaps than to large gaps.

The untreated marker indicates instances where although chronic drug administration may be warranted for a condition, there is no evidence of such through the pharmacy claims. Non-treatment


Pharmacy Adherence

Marker


11-9

Definition

Marker.)

The untreated marker can have the following values:

• Y – The patient has no prescriptions in a targeted drug class.

• P - The patient is classified as potentially untreated based upon one of the following criteria:

• The patient has only one prescription in a targeted drug class.

• The patient has more than one prescription in a targeted drug class but all prescriptions occurred on a single fill date.

• The patient has more than one prescription from multiple targeted drug classes but does not have two or more from a single targeted drug class.

• The patient has more than one prescription in a targeted drug class but there are not two prescriptions within 120 days of each other.

• D – The patient meets treatment criteria, but it appears all medications within an appropriate chronic medication drug class have been discontinued, and the patient is now likely untreated based on the fact that greater than

120 days have elapsed between the supply end date and the observation period end date.

• N – The patient meets the treatment criteria.

If none of the previous values apply to a particular member, the untreated column will be blank

Application/Interpretation represents another potential care management issue. It should be noted, however, that there are situations where prescribing can occur, but not be captured in the pharmacy claims. Examples:

• Pharmacy benefits outsourced or absent

• Enrollment gaps or discontinuation

• Samples given in physician’s office

• Discount drug programs

Additional research may be necessary before trying to draw conclusions from this metric.



Marker Definition indicating that there was insufficient evidence to confirm the presence of the specific condition.

Application/Interpretation

Medication Possession Ratio (MPR)

Medication possession ratio is calculated for patients taking at least one chronic medication within the condition-drug class pairings listed in

Table 2: Condition-Drug

Class Pairings

. For each condition, there is a marker with the naming convention of the specific condition appended with _MPR. This marker contains the calculation of the ratio of days supply to total prescribing days. If a person is taking multiple medications

(active ingredients) for a single condition, the days supply and total prescribing days are totaled across all of the medications and then averaged.

Table 4: MPR Example 1

,


, and


all provide examples of

MPR calculations.


Rx_fill_date Days_Supply Supply Available

Upon Refill

1/15/2009

2/10/2009

4/2/2009

5/19/2009

30

30

30

30

MPR=90 days’ supply / (5/19/2009 – 1/15/2009) = 0.73


4

Days Exceeding

Grace Period

2

2

Rx_fill_date Days_Supply

Supply Available

Upon Refill

1/15/2009

2/10/2009

3/18/2009

4/19/2009

30

30

30

30

MPR=90 days’ supply / (4/19/2009 – 1/15/2009) = 0.96

4

Days Exceeding

Grace Period






Upon Refill

Days Exceeding

Grace Period

1/15/2009

2/10/2009

3/10/2009

4/06/2009

30

30

30

30

4

6

9

MPR=90 days’ supply / (4/06/2009 – 1/15/2009) = 1.11

In general, this marker is more sensitive to large gaps than to frequent gaps. MPR can be greater than 1.0 if the member consistently refills prior to exhausting supply on hand.

Additional patient-level detail at the active ingredient / drug class level for each prescription evaluated in the adherence calculations is available in the Pharmacy Spans

Export File.

Continuous, Single-interval Measure of Medication Availability (CSA)

The Continuous, Single-interval Measure of Medication Availability is calculated for patients taking at least one chronic medication within the condition-drug class pairings listed in

Table 2: Condition-Drug Class Pairings

. For each condition, there is a marker with the naming convention of the specific condition appended with _CSA.

Table 7: CSA Example 1

,

Table 8 : CSA Example 2

, and

Table 9: CSA Example 3 all provide examples of CSA calculations.




Upon Refill

Days Exceeding

Grace Period

1/15/2009

2/10/2009

4/2/2009

5/19/2009

30

30

30

30

4

2

2

CSA = ((30 / (2/10/2009 – 1/15/2009)) + (30 / (4/2/2009 – 2/10/2009)) + (30 / (5/19/2009

– 4/2/2009)))/3 = 0.79






Upon Refill

Days Exceeding

Grace Period

1/15/2009

2/10/2009

3/18/2009

4/19/2009

30

30

30

30

4

CSA = ((30 / (2/10/2009 – 1/15/2009)) + (30 / (3/18/2009 – 2/10/2009)) + (30 /

(4/19/2009 – 3/18/2009)))/3 = 0.97




Upon Refill

Days Exceeding

Grace Period

1/15/2009

2/10/2009

3/10/2009

4/06/2009

30

30

30

30

6

9

4

CSA = ((30 / (2/10/2009 – 1/15/2009)) + (30 / (3/10/2009 – 2/10/2009)) + (30 / (4/6/2009

– 3/10/2009)))/3 = 1.11

The CSA, since it is an average, shows more sensitivity to extremes and thus is more suited to identifying frequent gaps in medication possession. CSA can be greater than 1.0 if the member consistently refills prior to the exhausting supply on hand. Additional patient-level detail at the active ingredient / drug class level is available in the Pharmacy

Spans Export File.

Reporting of Possession

Reporting of possession is summarized at the condition level within the patient file. A number of conditions have only one condition-drug class pair (e.g., osteoporosis), while others have multiple pairs (e.g., persistent asthma). If a condition is associated with multiple drug classes, all gaps associated with these drugs are reported. Further, if a gap occurs for a combination drug, the gap is reported for each component that falls into a separate drug class. Specific drugs and prescribing periods are stored and made available in the Pharmacy Spans Export File which is described in more detail in Appendix A of the


.



In addition, reporting of possession is available at the drug class level within the

Comprehensive Patient Clinical Profile Report and the related Drug Class Summary

Export which is described in more detail in Appendix A of the

Installation and Usage

Guide

. Both the maximum gap (largest gap, in days, for a medication within the specific drug class) and average gap size (in days) are reported within this analysis. Gaps may differ greatly in terms of duration and the duration has potential implications for care management. The intervention for someone with an extended gap in possession may be different than for someone experiencing a one-time shorter gap.

In some cases, a span may not (N) be included in the adherence calculations, may be included in possession (P) calculations only, or may be included in gaps (G) calculations only. Examples include:

• N: The drug class is not eligible for adherence calculations, i.e., short-acting adrenergic bronchodilators or short-acting insulins.

• N: There is an extended gap (greater than 60 days exceeding the grace period).

•

•

N: The required minimum prescribing period (120-day supply) at the drug class level was not met AND there were spans with at least one day exceeding the grace period, but the patient has a fill for a different ingredient within the same drug class (potential substitution).

G: The required minimum prescribing period (120-day supply) at the drug class level was not met.

• P: Spans with at least one day exceeding the grace period, but the patient has a fill for a different ingredient within the same drug class (potential substitution).

Excluded spans are flagged in the Rx Eligible for Adherence column within the

Pharmacy Spans Export File, which is described in more detail in Appendix A of the


.

Note

: There must be at least one eligible span in order for adherence to be calculated.

End of Period Possession

The pharmacy adherence methodology assesses gaps and possession ratios between the first prescription fill and the last prescription refill. When assessing the last fill, it is helpful to differentiate patients that have supply through the full observation period from patients that are without supply at the end of the observation period. If a member is without supply at the end of the period, further investigation may be needed to determine if the member has stopped therapy, if the member has started an alternate therapy or if the member is non-adherent with the current therapy.

For each ingredient that is evaluated, the supply of medications is tracked through the

Pharmacy Spans Export File. By comparing the supply end date to the observation period end date, possession at the end of the period can be calculated. The Drug Class

Summary Export File captures an End of Period Possession field (Y / N) to indicate if there is still supply available as of the observation end date.



If more than 120 days have elapsed between the supply end date and the observation period end date for all drug classes within a given condition, the individual is likely now untreated and the untreated marker will be set to D for discontinued. The patient will still have the condition marker set to TRT if all other criteria are met.

Validation and Testing

Pharmacy adherence / possession has strong face validity among clinicians. If patients fail to comply with therapy, there are likely to be future consequences. For some conditions, these consequences will be felt immediately; however, for others years may pass before serious adverse events are experienced.

As part of the validation of the pharmacy adherence markers, the relationship between gap counts and several important outcomes was assessed. The purpose of this validation exercise was to determine whether or not there was an association between numbers of gaps and either concurrent or prospective total cost. Internal consistency of the markers was also assessed by looking at the correlation between gaps and possession (i.e., a higher number of gaps resulted in a lower MPR).

Table 10: Possible Types of Analyses

suggests some validation analyses. The example evaluates patients with osteoporosis who were prescribed hormones, and the data shown are for persons in the highest two Resource Utilization Band (RUB) categories (RUBs 4 and 5).


DEPENDENT EFFECTS

Number of Cases

Median Total Cost Year One

Median Pharmacy Cost Year One

Median Total Cost Year Two

Median Pharmacy Cost Year Two

Median MPR

Maximum Gap Duration

No Gap <7 days 8-29 Days

2,844

$7,789

$2,787

$7,178

$2,842

0.99

496

$7,656

$2,671

$6,863

$2,724

0.90

1,035

$8,640

$2,653

$7,319

$2,625

0.83

Number of Gaps

30+ Days

1,167

$8,094

$2,426

$7,907

$2,645

0.59

DEPENDENT EFFECTS

Number of Cases

Median Total Cost Year One

Median Pharmacy Cost Year One

Median Total Cost Year Two

Median Pharmacy Cost Year Two

Median MPR

No Gap One Gap Two Gaps

2,844

$7,789

$2,787

$7,178

$2,842

0.99

1,710

$8,154

$2,529

$7,345

$2,637

0.84

590

$8,255

$2,514

$7,322

$2,622

0.69

3+ Gaps

398

$8,416

$2,729

$8,517

$2,910

0.57

Source: IMS Health Payer Solutions, Watertown, MA; national cross-section of managed care plans; population of 4,740,000 commercially insured lives (less than 65 years old) and population of 257,404

Medicare beneficiaries (65 years and older), 2007.



This particular extract used patients who had a minimum of 120 prescribing days in order to improve the homogeneity of the patient population. Setting such a threshold also eliminates persons who are receiving prophylactic treatment. For example, short courses of HAART are routinely prescribed to persons who have been exposed to the HIV virus.

These data exemplify some of the complexity involved in gap-related data. Patients with many gaps and gaps of long duration show a different pattern than patients with moderate gaps and gaps of moderate duration. These high gap patients may reflect treatment uncertainty, with factors that contra-indicate the use of the particular medication

(hormones), or some other complicating factor. Their year two total costs are higher than the other groups, suggesting a need for monitoring. Persons familiar with the use of the

MPR will know that a value of 0.8 is widely accepted as a good level of medication possession.


shows that an MPR of 0.8 can be present along with potential gap-related issues that impact the costs of care. MPR alone should not be used to determine appropriate medication possession.

Medication possession is a complex issue that will require a close partnership between analysts and clinicians. There are subtle distinctions in the significance of gaps depending upon the condition. Further, gaps may potentially be indicative of good management. For persons with persistent asthma, gaps in adrenergic bronchodilators could mean that the asthma is under good control and over-supply could mean the converse, poor control.

Because of the subtleties in interpreting gaps, it is highly recommended that ACG System results be shared with one or more clinical reviewers before identifying which conditiondrug class pairs are targeted for potential intervention.


11-16


Pharmacy Adherence


Appendix A: ACG ® Publication List A-1

Appendix A: ACG

®

Publication List

December 2011

Corrections or updates should be forwarded to askacg@jhsph.edu

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AdvanceMed Corp., a DynCorp Company. (2003). “Applicability of Predictive Risk

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Aguado, A, Guinó E, Mukherjee B, Sicras A, Serrat J, Acedo M, Ferro JJ, Moreno V.

(2008). “Variability in prescription drug expenditures explained by adjusted clinical groups (ACG) case-mix: A cross-sectional study of patient electronic records in primary care.” BMC Health Services Research. 2008 Mar 4;8:53. http://www.ncbi.nlm.nih.gov/pubmed/18318912

Anderson, G., Weller, W. (1999). “Methods of Reducing the Financial Risk of Physicians

Under Capitation.” AMA, Archives of Family Medicine , 8 (2), 149-155. http://www.ncbi.nlm.nih.gov/pubmed/10101986

Ash, A, McCall, N. “Risk Assessment of Military Populations to Predict Health Care

Cost and Utilization.” Final report prepared for Center for Health Care

Management Studies, HPA&E, TRICARE Management Activity, RTI Project

Number 08490.006. November, 2005.

Austin PC, Walraven C. (2011). “The Mortality Risk Score and the ADG Score: Two

Points-Based Scoring Systems for the Johns Hopkins Aggregated Diagnosis

Groups to Predict Mortality in a General Adult Population Cohort in Ontario,

Canada”. Med Care. 2011 Oct;49(10):940 http://www.ncbi.nlm.nih.gov/pubmed/21921849

Austin PC, Van Walraven C, Wodchis WP, Newman A, Anderson GM. (2011). “Using the Johns Hopkins Aggregated Diagnosis Groups (ADGs) to Predict Mortality in a General Adult Population Cohort in Ontario, Canada”. Med Care. 2011 Apr 6.

[Epub ahead of print] http://www.ncbi.nlm.nih.gov/pubmed/21478773

Baldwin L.M., Klabunde C.N., Green P., Barlow W., Wright G. (2006). “In Search of the

Perfect Comorbidity Measure for Use With Administrative Claims Data. Does it

Exist?” Med Care, 44 (8), 745-753. http://www.ncbi.nlm.nih.gov/pubmed/16862036


A-2 Appendix A: ACG® Publication List

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(2011). “Working Well - The Texas Demonstration to Maintain Independence and

Employment: 18-month outcomes”. Journal of Vocational Rehabilitation 34

(2011) 97-106 DOJ: 1 0,3233nYR-20 I 0-0538. http://iospress.metapress.com/content/m60166134k49v864/

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Psychopharmacology 2008 Aug;28(4):384-91. http://www.ncbi.nlm.nih.gov/pubmed/18626264

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Bono, C., Shenkman, E., Hope-Wegener, D. (2000). “The Actual Versus Expected Health

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169(18): 1692-7. http://www.ncbi.nlm.nih.gov/pubmed/19822826

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(Millwood). 2010 Aug;29(8):1432-41 http://www.ncbi.nlm.nih.gov/pubmed/20679646

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Conditions & Co-morbidity.” Presentation. Centre for Health Services and Policy

Research .



Broemeling, A., Watson, D., and Black, C. (2005). “Chronic conditions and comorbidity among residents of British Columbia.” Centre of for Health Services and Policy Research, University of British Columbia.

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Aragon, Spain: the impact of a local calibration”. Aragon Health Science

Institute. http://www.ncbi.nlm.nih.gov/pubmed/20092654

Carlin C, Town R. (2009). “Adverse Selection, Welfare and the Optimal Pricing of

Employer-Sponsored Health Plans”. University of Minnesota and NBER. https://editorialexpress.com/cgibin/conference/download.cgi?db_name=NASM2009&paper_id=199

Carlsson, L., Borjesson, U., Edgren, L. (2002). ”Patient based 'burden-of-illness' in

Swedish primary health care. Applying the Johns Hopkins ACG case-mix system in a retrospective study of electronic patient records”. International Journal of

Health Planning and Management, 17 (3), 269-282. http://www.ncbi.nlm.nih.gov/pubmed/12298147

Carlsson L. (2004). ”Burden of Illness in Defined Populations”. Department of Clinical

Services, Center of Family Medicine Karolinska Institutet.

Carlsson, L., Strender, Lars-Erik, Fridh, Gerd and Nilsson, Gunnar (2004). ”Types of

Morbidity and Categories of Patients in a Swedish County: Apply the Johns

Hopkins Adjusted Clinical Groups System to Encounter Data in Primary Health

Care.” Scandinavian Journal of Primary Health Care, 22 , 174-179. http://www.ncbi.nlm.nih.gov/pubmed/15370795

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Karolinska Institutet, Stockholm, Sweden. http://www.ncbi.nlm.nih.gov/pubmed/16483353



Carmona M, Garcia-Olmosb LM, Alberquillac A, Munoz A, Garcia-Sagredo P,

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Oxford Journal of Medicine, Family Practice Advance

Access10.1093

/fampra/cmq084 http://fampra.oxfordjournals.org/content/early/2010/10/26/fampra.cmq084.abstrac

t

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Update, ACGs: An Ambulatory Care Case-mix Measure.” CHQOER Quarterly ,

ENRM VA Hospital, Bedford, MA. Fall, 1997.

Center for Health Program Development and Management University of Maryland,

Baltimore County and Actuarial Research Corporation Annandale, VA. (2003).

“A Guide to Implementing a Health-based Risk-Adjusted Payment System for

Medicaid Managed Care Programs”. Centers for Medicare and Medicaid Services

Baltimore, MD. http://www.chpdm.org/publications/GuideToImplementingAHealth-BasedRisk-

AdjustedPaymentSystemForMedicaidManagedCarePrograms-March2003.pdf

Chang RE, Lin W, Hsieh CJ, Chiang TL. (2002). “Healthcare Utilization Patterns and

Risk adjustment Under Taiwan’s National Health Insurance System”. Journal of

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Chang RE, Lai CL. (2005). “Use of diagnosis-based risk adjustment models to predict individual health care expenditure under the National Health Insurance system in

Taiwan”. J Formos Med Assoc.

2005 Dec;104(12):883-90. http://www.ncbi.nlm.nih.gov/pubmed/16607444

Chang HY, Lee WC, Weiner JP. (2010). “Comparison of alternative risk adjustment measures for predictive modeling: high risk patient case finding using Taiwan's

National Health Insurance claims”. BMC Health Serv Res . 2010 Dec 20;10:343. http://www.ncbi.nlm.nih.gov/pubmed/21172009

Chang, HY. (2011). “Morbidity Trajectories as Predictors of Utilization: Multi-year

Disease Patterns in Taiwan’s National Health Insurance Program”. Med Care.

2011 May 13. http://www.ncbi.nlm.nih.gov/pubmed/21577165

Chase Deborah. (2011). “Patients Gain Information and Skills to Improve Self-

Management Through Innovative Tools”. The Commonwealth Fund. http://www.commonwealthfund.org/Content/Newsletters/Quality-

Matters/2010/December-January-2010.aspx?view=newsletter_print

Chochinov HM, Martens PJ, Prior HJ, Fransoo R, Burland E. (2009). Does a diagnosis of schizophrenia reduce rates of mammography screening?” A Manitoba populationbased study . Schizophr Res . 2009 Aug;113(1):95-100. http://www.ncbi.nlm.nih.gov/pubmed/19427766



Clark, D. O., Von Korff, M., Saunders, K., Baluch, W. M., Simon, G. E. (1995). “A

Chronic Disease Score with Empirically Derived Weights.” Medical Care, 33 (8),

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“Primary Care Physician Compensation Method in Medical Groups: Does It

Influence The Use and Cost of Health Services For Enrollees in Managed Care

Organizations?” JAMA, 279 (11), 853-858. http://www.ncbi.nlm.nih.gov/pubmed/9516000

Conrad D, Fishman P, Grembowski D, Ralston J, Reid R, Martin D, Larson E, Anderson

M. (2008). “Access Intervention in an Integrated, Prepaid Group Practice: Effects on Primary Care Physician Productivity”. Health Services Research, Volume

43 (5p2) 1888-1905. http://www.ncbi.nlm.nih.gov/pubmed/18662171

Cooke AL, Metge C, Lix L, Prior HJ, Leslie WD. (2008). “Tamoxifen use and osteoporotic fracture risk: a population-based analysis”. J Clin Oncol . 2008 Nov

10;26(32):5227-32. Epub 2008 Oct 6. http://www.ncbi.nlm.nih.gov/pubmed/18838712

Cramtom,C, C., Davis, P., Lay-Yee, R., Raymont, A., Forrest, C., Starfield, B. (2004).

“Comparison of Private For-Profit with Private Community-Governed Not-For-

Profit Primary Care Services in New Zealand”. Journal of Health Services

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Medicine, 162 , 2269-2276. http://www.ncbi.nlm.nih.gov/pubmed/12418941

Woolf S.H., Rothemich S.F., Johnson R.E., Marsland D.W. (2000). “Selection Bias from

Requiring Patients to Give Consent to Examine Data for Health Services

Research”. AMA, Archives of Family Medicine, 9 (10), 1111-1118. http://www.ncbi.nlm.nih.gov/pubmed/11115216

Zielinski A., Kronogård M., Lenhoff H., Halling A. (2009). “Validation of ACG casemix for equitable resource allocation in swedish primary health care.” BMC

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Appendix B: Variables Necessary to Locally Calibrate the ACG® Predictive Models

Appendix B: Variables Necessary to Locally

Calibrate the ACG

®

Predictive Models

B-i

Introduction .......................................................................................................1

Table 1: Individual ACG Categories Included in the Predictive Models .....1

Table 2: ACGs Included in Three Prospectively Calibrated Resource

Utilization Bands (RUBs) ..............................................................................2

Table 3: Pregnancy Without Delivery ...........................................................2

Table 4: EDCs Included in the Predictive Models ........................................3

Table 5: RX-Defined Morbidity Groups  (Rx-MGs) Included in the

Predictive Models ...........................................................................................6

Table 6: Special Population Markers ............................................................7

Table 7: Demographic Markers .....................................................................7

Table 8: Optional Prior Cost Markers* .........................................................7

Table 9: Utilization Markers** .....................................................................8


B-ii Appendix B: Variables Necessary to Locally Calibrate the ACG® Predictive Models



Appendix B: Variables Necessary to Locally Calibrate the ACG® Predictive Models B-1

Introduction

These tables indicate the variables used in the ACG Predictive Models. The ACG

Predictive Models have been developed using a reference population provided by

PharMetrics, a unit of IMS in Watertown, MA. Modest performance improvements may be attained when predictive models are calibrated against a local population. The ACG

Software facilitates local calibration of predictive models with the export of the independent variables as model markers. For reference, the independent variables used in the ACG Predictive Models are presented in the following tables.

U.S. and international copyright law protect these assignment algorithms. It is an infringement of copyright law to develop any derivative product based on the grouping algorithm or other information presented in this document without the written permission of The Johns Hopkins University.

Copyright © The Johns Hopkins University, 1995, 1997, 1998, 1999, 2000, 2001, 2002,

2003, 2004, 2005, 2007, 2008, 2009, and 2011. All Rights Reserved.

Table 1: Individual ACG Categories Included in the Predictive Models

4730

4830

4910

4920

4930

4940

5010

5020

4220

4330

4420

4430

4510

4520

4610

4620

5030

5040

5050

5060

5070

5320

ACG Description

4-5 Other ADG Combinations, Age 1-17, 1+ Major ADGs


4-5 Other ADG Combinations, Age >44, 1 Major ADGs

4-5 Other ADG Combinations, Age >44, 2+ Major ADGs

6-9 Other ADG Combinations, Age 1-5, No Major ADGs


6-9 Other ADG Combinations, Age 6-17, No Major ADGs


6-9 Other ADG Combinations, Male, Age 18-34, 2+ Major ADGs

6-9 Other ADG Combinations, Female, Age 18-34, 2+ Major ADGs

6-9 Other ADG Combinations, Age >34, 0-1 Major ADGs



6-9 Other ADG Combinations, Age >34, 4+ Major ADGs

10+ Other ADG Combinations, Age 1-17 No Major ADGs

10+ Other ADG Combinations, Age 1-17, 1 Major ADGs

10+ Other ADG Combinations, Age 1-17, 2+ Major ADGs

10+ Other ADG Combinations, Age 18+, 0-1 Major ADGs

10+ Other ADG Combinations, Age 18+, 2 Major ADGs

10+ Other ADG Combinations, Age 18+, 3 Major ADGs

10+ Other ADG Combinations, Age 18+, 4+ Major ADGs

Infants: 0-5 ADGs, 1+ Major ADGs


B-2 Appendix B: Variables Necessary to Locally Calibrate the ACG® Predictive Models

5321

5322

5330

5331

5332

5340

5341

5342

ACG Description

Infants: 0-5 ADGs, 1+ Major ADGs, low birth weight

Infants: 0-5 ADGs, 1+ Major ADGs, normal birth weight

Infants: 6+ ADGs, No Major ADGs

Infants: 6+ ADGs, No Major ADGs, low birth weight

Infants: 6+ ADGs, No Major ADGs, normal birth weight

Infants: 6+ ADGs, 1+ Major ADG

Infants: 6+ ADGs, 1+ Major ADG, low birth weight

Infants: 6+ ADGs, 1+ Major ADG, normal birth weight

Table 2: ACGs Included in Three Prospectively Calibrated Resource

Utilization Bands (RUBs)

ACG Prospective RUB Levels

ACG RUB Level 1 (included in Intercept)

ACG RUB Level 2

ACG RUB Level 3

Reference Group

ACG 0100 0200 0300 1100 1200 1600 5100

5110 5200 9900

ACG 0400 0500 0600 0900 1000 1300 1800

1900 2000 2100 2200 2300 2400 2500 2800

2900 3000 3100 3400 3900 4000 1711 1721

1731 1741

ACG 0700 0800 1400 1500 2600 2700 3200

3300 3500 3600 3700 3800 4100 4210 4310

4320 4410 4710 4720 4810 4820 5310 1751

1761 1771

Table 3: Pregnancy Without Delivery

Description

All non-delivered pregnancy related ACGs (1712, 1722, 1732, 1742, 1752, 1762 and 1772. This marker further evaluates pregnancy ACGs to exclude terminated pregnancies identified by the Expanded Diagnosis Cluster include pregnancies subsequent to a delivery.

™ (EDC) FRE14 and to



Table 4: EDCs Included in the Predictive Models

EDC Description

END06

END07

END08

END09

EYE03

EYE13

EYE15

GAS02

GAS04

GAS05

GAS06

GAS10

GAS11

GAS12

GSI08

GSU11

ADM03

ALL04

ALL05

ALL06

CAR03

CAR04

CAR05

CAR06

CAR07

CAR09

CAR10

CAR12

CAR13

CAR14

CAR15

END02

GSU13

GSU14

GTC01

GUR04

HEM01

HEM03

HEM05

HEM06

HEM07

HEM09

INF04

INF08

Transplant Status


Asthma, WITH status asthmaticus

Disorders of the Immune System





Cardiomyopathy

Cardiac arrhythmia


Acute Myocardial Infarction

Cardiac arrest, shock


Hypertension, WITH major complications

Osteoporosis

Type 2 Diabetes, w/o Complication

Type 2 Diabetes, w/ Complication

Type 1 Diabetes, w/o Complication

Type 1 Diabetes, w/ Complication


Diabetic Retinopathy

Age-related Macular Degeneration


Acute hepatitis


Peptic ulcer disease

Diverticular disease of colon

Acute pancreatitis


Edema


Aortic aneurysm

Gastrointestinal Obstruction/Perforation




Thrombophlebitis

Aplastic anemia


Hemophilia, coagulation Disorder

Sickle Cell Disease

HIV, AIDS

Septicemia



EDC Description

MUS10

MUS14

MUS16

NUR03

NUR05

NUR06

NUR07

NUR08

NUR09

NUR11

NUR12

NUR15

NUR16

NUR17

NUR18

NUR19

MAL02

MAL03

MAL04

MAL06

MAL07

MAL08

MAL09

MAL10

MAL11

MAL12

MAL13

MAL14

MAL15

MAL16

MAL18

MUS03

NUT02

PSY01

PSY02

PSY03

PSY05

PSY07

PSY08

PSY09

PSY12

REC01

REC03

REC04

REN01

REN02














Acute Leukemia



Fracture of neck of femur (hip)

Low back pain

Amputation Status



Parkinson's disease

Seizure disorder

Multiple sclerosis

Muscular dystrophy


Quadriplegia and Paraplegia

Head Injury

Spinal Cord Injury/Disorders

Paralytic Syndromes, Other

Cerebral Palsy


Nutritional deficiencies

Anxiety, neuroses

Substance use

Tobacco abuse




Depression

Bipolar Disorder



Burns--2nd and 3rd degree


Fluid/electrolyte disturbances


Appendix B: Variables Necessary to Locally Calibrate the ACG® Predictive Models

EDC Description

REN03

REN04

RES02

RES03

RES04

RES08

RES09

RES10

RHU01

RHU05

TOX02

TOX04

Acute renal failure

Nephritis/Nephrosis

Acute lower respiratory tract infection

Cystic fibrosis


Pulmonary embolism

Tracheostomy

Respiratory arrest



Adverse effects of medicinal agents

Complications of Mechanical Devices

B-5



Table 5: RX-Defined Morbidity Groups



(Rx-MGs) Included in the

Predictive Models

Rx-MG Description

ALLx010 Allergy/Immunology / Acute Minor

ALLx030 Allergy/Immunology / Chronic

Inflammatory

ALLx040 Allergy/Immunology / Immune

Disorders

ALLx050 Allergy/Immunology / Transplant

CARx010 Cardiovascular / Chronic Medical

CARx020 Cardiovascular / Congestive Heart

Failure

CARx030 Cardiovascular / High Blood Pressure

CARx040 Cardiovascular / Hyperlipidemia

CARx050 Cardiovascular / Vascular Disorders

EARx010 Ears, Nose, Throat / Acute Minor

ENDx010 Endocrine / Bone Disorders

ENDx020 Endocrine / Chronic Medical

ENDx030 Endocrine / Diabetes With Insulin

ENDx040 Endocrine / Diabetes Without Insulin

ENDx050 Endocrine / Thyroid Disorders

ENDx060 Endocrine / Growth Problems

ENDx070 Endocrine / Weight Control

EYEx010 Eye / Acute Minor: Curative

EYEx020 Eye / Acute Minor: Palliative

EYEx030 Eye / Glaucoma

FREx010 Female Reproductive / Hormone

Regulation

FREx020 Female Reproductive / Infertility

FREx030 Female Reproductive / Pregnancy and

Delivery

GASx010 Gastrointestinal/Hepatic / Acute Minor

GASx020 Gastrointestinal/Hepatic / Chronic Liver

Disease

GASx030 Gastrointestinal/Hepatic / Chronic Stable

GASx040 Gastrointestinal/Hepatic / Inflammatory

Bowel Disease

GASx050 Gastrointestinal/Hepatic / Pancreatic

Disorder

GASx060 Gastrointestinal/Hepatic / Peptic Disease

GSIx010 General Signs and Symptoms / Nausea and Vomiting

GSIx020 General Signs and Symptoms / Pain

GSIx030 General Signs and Symptoms / Pain and

Inflammation

Rx-MG Description

GSIx040 General Signs and Symptons / Severe

Pain

GURx010 Genito-Urinary / Acute Minor

GURx020 Genito-Urinary / Chronic Renal Failure

HEMx010 Hematologic / Coagulation Disorders

INFx010 Infections / Acute Major

INFx020 Infections / Acute Minor

INFx030 Infections / HIV/AIDS

INFx040 Infections / Tuberculosis

INFx050 Infections / Severe Acute Major

MALx010 Malignancies

MUSx010 Musculoskeletal / Gout

MUSx020 Musculoskeletal / Inflammatory

Conditions

NURx010 Neurologic / Alzheimer's Disease

NURx020 Neurologic / Chronic Medical

NURx030 Neurologic / Migraine Headache

NURx040 Neurologic / Parkinsons Disease

NURx050 Neurologic / Seizure Disorder

PSYx010 Psychosocial / Attention Deficit

Hyperactivity Disorder

PSYx020 Psychosocial / Addiction

PSYx030 Psychosocial / Anxiety

PSYx040 Psychosocial / Depression

PSYx050 Psychosocial / Acute Minor

PSYx060 Psychosocial / Chronic Unstable

RESx010 Respiratory / Acute Minor

RESx020 Respiratory / Chronic Medical

RESx030 Respiratory / Cystic Fibrosis

RESx040 Respiratory / Airway Hyperactivity

SKNx010 Skin / Acne

SKNx020 Skin / Acute and Recurrent

SKNx030 Skin / Chronic Medical

TOXx010 Toxic Effects/Adverse Effects / Acute

Major



Table 6: Special Population Markers

Special Population Marker

HOSDOM0 (included in Intercept)

HOSDOM1

HOSDOM2

HOSDOM3+

Frailty Marker

Active Ingredient Count

Use in the Predictive Model

Boolean indicator for 0 HOSDOM

Boolean indicator for 1 HOSDOM indicator

Boolean indicator for 2 HOSDOM indicators

Boolean indicator for 3 or more HOSDOM indicators

Boolean indicator

Boolean indicator for 14 or more Active

Ingredients

Table 7: Demographic Markers

Demographic Marker

Ten age groups

Female

Use in the Predictive Model

0-4; 5-11; 12-17; 18-34; 35-44; 45-54; 55-69

(Included in the Intercept); 70-74; 75-79; 80-

84; 85+

Boolean indicator

Table 8: Optional Prior Cost Markers*

Prior Cost Marker

Total Expense, Non-users (Included in the Intercept)

Total Expense, 1-10th Percentile




Total Expense, 76-91st Percentile

Total Expense, 91-93rd Percentile




Pharmacy Expense, Non-users (Included in the Intercept)

Pharmacy Expense, 1-10th Percentile




Pharmacy Expense, 76-91st Percentile

Pharmacy Expense, 91-93rd Percentile




*Either pharmacy expense markers or total expense markers will be used depending on the model variant, but pharmacy expense and total expense will not be used simultaneously. Prior pharmacy expense is preferred when the predicting pharmacy expense and prior total expense is preferred when predicting total expense.



Table 9: Utilization Markers**

Utilization Markers

Dialysis Service

Nursing Service

Major Procedure

Active Cancer Treatment

1 Inpatient Hospitalization

2 Inpatient Hospitalizations



5+ Inpatient Hospitalizations

1 Emergency Department Episode

2 Emergency Department Episodes



5+ Emergency Department Episodes

1-9 Outpatient Visits




60+ Outpatient Visits

**Utilization markers are used only in the Hospitalization Prediction Models.


Index IN-1

A

ACG assignment process, 3-3 clinical aspects, 3-29 clinically oriented examples, 3-33 code set support, 2-1 concurrent ACG weights, 3-11 coordination markers, 10-1 decision tree, 3-22 development, 3-1 forecasting future costs with the components of the

ACG case mix system, 7-3 how ACGs work, 3-2 infants, 3-40 overview, 3-1 pregnancy, 3-38 pregnancy without delivery, 6-5 terminal groups, 3-11

ACG-PM conceptual basis, 7-9 data inputs, 7-12 definition of population-based predictive modeling, 7-2 development of ACG predictive models, 7-13 elements of a predictive model – five key dimensions,

7-9 multi-morbidity as the clinical basis, 7-5 outcomes assessed, 7-13 predictive modeling, 7-1 statistical modeling approach, 7-11 target population, 7-10

Active cancer treatment , 9-4

Active ingredient count variable , 5-23

ADG subgroups (CADG), 3-8

Adjusted R-square values predictive modeling statistical performance, 8-1

Applications guide content , 1-4 introduction, 1-4

ATC

Anatomical Therapeutic Chemical (ATC) System, 5-2 to RX-MG assignment methodology, 5-9 diagnostic certainty, 3-30 duration, 3-29 etiology, 3-30 major, 3-7 mapping ICD codes to a parsimonious set of aggregated diagnosis groups (ADGs), 3-4 severity, 3-30 specialty care, 3-30


Index

C using Anatomical Therapeutic Chemical Codes (ATC),

2-6

CADG creating a manageable number of ADG subgroups

(CADGs), 3-8 occurring combinations, 3-10

Chronic condition count , 6-5

Chronic illnesses , 3-33

Clinical aspects of ACGs , 3-29

Clinical information improves on prior cost predictive modeling statistical performance, 8-11

Clinically oriented examples chronic illnesses, 3-33 diabetes mellitus, 3-36 hypertension, 3-34 infants, 3-41 pregnancy/delivery with complications, 3-39

Coding , 2-2

Combined ACG predictive model (DxRx-PM ) , 7-16

Concurrent ACG Weights , 3-11

Condition markers , 6-10

Continuous single-interval measure of medication availability (CSA) , 11-11

Coordination assessing care coordination, 10-1 conclusion, 10-15 generalist seen (GS) marker, 10-12 introduction, 10-1 risk of, 10-13 specialty count (SC) marker, 10-10

Customer commitment and contact information , 1-5

D

Decision tree for MAC-12--pregnant women, 3-24 for MAC-26--infants, 3-26

Development of ACG-PM , 7-13

Development of possession/adherence markers , 11-2

Development of Rx-PM , 7-15

Diagnosis and code sets

Anatomical Therapeutic Chemical Codes (ATC), 2-6 coding issues using the International Classification of

Diseases (ICD), 2-2 introduction, 2-1

National Drug Codes (NDC), 2-4 provisional, 2-3 rule-out, 2-3


IN-2 suspected, 2-3 three and four digits, 2-2 using ICD-9 and ICD-10 simultaneously, 2-4

Dialysis service , 9-3

Dx-PM diagnosed-defined predictive model, 7-13

Dx-PM risks factors age and gender, 7-14 high-impact chronic conditions, 7-14 hospital dominant condition markers, 7-14 overall morbidity burden, 7-14

DxRx-PM combined ACG predictive model, 7-16

E

EDC application of, 4-17 development, 4-1 diagnostic certainty, 4-15 important differences between EDCs and ADGs, 4-16 important differences between EDCs and episode-ofcare systems, 4-16

MEDC types, 4-15 overview, 4-1 understanding the workings, 4-3

Elements of a predictive model – five key dimensions , 7-9

Emergency visit count , 9-4

Empiric validation of the likelihood of hospitalization model , 9-6

C-statistics, 9-7 sensitivity and positive predictive value, 9-6

F

Figures

ACG decision tree, 3-22 decision tree for MAC-12--pregnant women, 3-24 decision tree for MAC-24--multiple ADG categories,

3-28 decision tree for MAC-26--infants, 3-26 distribution of the majority source of care in a large, commercial managed care population, 10-7 distribution of the specialty count in a large, commercial managed care population, 10-11

Dx-PMrisk factors, 7-14 gap in medication possession, 11-2 gap in medication possession following hospitalization,

11-3 gap in medication possession following oversupply,

11-3 generalist seen (GS), 10-12 impact of coordination on ED use by asthma patients,

10-15 impact of coordination on high morbidity users, 10-14 majority source of care, 10-6 percentage of patients with selected chronic condition and their associated number of co-morbidities, 7-6 schematic framework for predicting hospitalization, 9-3 specialty count (SC), 10-10


Index total healthcare costs per beneficiary by count of chronic conditions, 7-8 typology of acute care inpatient hospitalization, 9-2 unique provider count, 10-8

Forming the terminal groups (ACGs) , 3-11

Frailty conditions , 6-3

Framework for prediction hospitalization using ACG how is ACG used to predict hospitalization, 9-2 likelihood of hospitalization, 9-5 utilization markers, 9-3 what hospitalizations are we predicting, 9-1 why include utilization in prediction models for hospitalization, 9-3 why predict hospitalization, 9-1

Frequently occurring combinations of CADGS (MACs) , 3-10

G

Gaps in pharmacy utilization validation and testing, 11-14

Generalist seen (GS) marker , 10-12

H

Healthcare common procedure coding system (HCPCS) ,

5-3

High pharmacy utilization model , 7-24

High pharmacy utilization model outputs high risk for unexpected pharmacy cost, 7-24 probability of unexpected pharmacy cost, 7-24 relevant to clinicians and case managers, 7-25

High risk for unexpected pharmacy cost , 7-24

High, moderate, and low impact conditions , 7-17

Hospital dominant conditions , 6-1

I

ICD

10, 2-4

10 users special note, 2-4

9, 2-4 mapping to ADGs, 3-4

Infants , 3-40

Inpatient hospitalization count , 9-4

Installation and usage guide content , 1-3 introduction, 1-3

Interpreting adjusted R-square values predictive modeling statistical performance, 8-6

Introduction applications guide content, 1-4 customer commitment and contact information, 1-5 diagnosis and code sets, 2-1 installation and usage guide content, 1-3 objective of the technical reference guide, 1-1

The Johns Hopkins ACG system, 1-1

L

Likelihood of hospitalization , 9-5 probability extended hospitalization score, 9-5


Index probability ICU hospitalization score, 9-5 probability injury hospitalization score, 9-5 probability IP hospitalization score, 9-5 probability IP hospitalization six months score, 9-5

M

MAC occurring combinations, 3-10

Major procedure , 9-4

Management visit count , 10-8

Mapping ICD codes to a parsimonious set of aggregated diagnosis groups (ADGs) , 3-4

Markers generalist seen (GS), 10-12 majority source of care (MSOC), 10-6 unique provider count (UPC), 10-8

Medication defined morbidity active ingredient count variable, 5-23 conclusion, 5-23 healthcare common procedure coding system (HCPCS),

5-3

National Drug Code System, 5-1 objectives, 5-1

Medication possession ratio (MPR) , 11-10

N

Navigation technical reference guide navigation, 1-1

NDC

National Drug Code System, 5-1 to Rx-MG assignment methodology, 5-9 using National Drug Codes, 2-4

New condition markers continuous single-interval measure of medication availability (CSA), 11-11 medication possession ratio (MRP), 11-10

Nursing service , 9-4

O

Objective of the technical reference guide , 1-1

Objectives predicting future resource use, 7-1

Outpatient visit count , 9-4

Overview

ACG, 3-1

ACG assignment process, 3-3 predictive modeling statistical performance, 8-1

P

Pharmacy adherence defined, 11-4 development of possession/adherence markers, 11-2 objectives, 11-1 significance of pharmacy adherence for effective care,

11-1

Predicting , 9-3


IN-3

Predicting future resource resource bands, 7-16

Predicting future resource use , 7-1 high pharmacy utilization model, 7-24 high, moderate and low impact conditions, 7-17

Predicting hospitalization , 9-1

C-statistics, 9-7 empiric validation of the likelihood of hospitalization model, 9-6 how, 9-2 introduction, 9-1 what, 9-1 why, 9-1

Predictive modeling , 7-1

Predictive modeling statistical performance adjusted R-square values, 8-1 clinical information improves on prior cost, 8-11 interpreting adjusted R-square values, 8-6 overview, 8-1 predictive ratios, 8-6

ROC curve, 8-10 sensitivity and positive predictive value, 8-8 validation data, 8-1

Predictive ratios predictive modeling statistical performance, 8-6

Pregnancy , 3-38

Pregnancy without delivery , 6-5

Probability extended hospitalization score, 9-5

ICU hospitalization score, 9-5 injury hospitalization score, 9-5

IP hospitalization score, 9-5

IP hospitalization six months score, 9-5 unexpected pharmacy costs, 7-24

Provisional diagnosis , 2-3

R

Resource bands , 7-16

Resource utilization bands (RUBS) , 3-13

Risk of coordination , 10-13

ROC curve predictive modeling statistical performance, 8-10

Rule-out diagnosis, 2-3 suspected and provisional diagnoses, 2-3

Rx-defined morbidity groups , 5-3

Rx-MG

ATC to Rx-MG assignment methodology, 5-9 expected duration, 5-8 morbidity differentiation, 5-8

NDC to Rx-MG assignment methodology, 5-9 primary anatomico-physiological system, 5-6 severity, 5-8

Rx-PM development, 7-15

Rx-defined morbidity groups, 5-3 score, 7-16


IN-4

S

Sensitivity and positive predictive value empiric validation of the likelihood of hospitalization model, 9-6 predictive modeling statistical performance, 8-8

Significance of pharmacy adherence for effective care ,

11-1

Special population markers chronic condition count, 6-5 condition, 6-10 frailty conditions, 6-3 hospital dominant conditions, 6-1 introduction, 6-1

Statistical modeling approach , 7-11 algebraic, 7-11 linear regression, 7-11 logistic regression, 7-11 neural networks, 7-12

Subgroups (CADG)

ADG, 3-8

Suspected diagnosis , 2-3

T

Tables

ADGs and common ICD-10 codes assigned to them,

3-4 annual prevalence ratios for expanded diagnosis clusters

(EDCs) for non-elderly and elderly beneficiaries,

4-7 annual prevalence ratios for Rx-MGs for non-elderly and elderly beneficiaries,

ATC drug classification levels, classification of metformin – anatomical therapeutic chemical (ATC) codes,

8-10

5-6

11-11

11-12

5-20

2-6

5-2 classification of metformin – National Drug Codes,

3-8

5-5

5-4

11-5

6-11

3-42

9-7

3-31

2-5

6-9 clinical classification of infant ACGs, clinical classification of pregnancy/delivery ACGs, 3-40 collapsed ADG clusters and the ADGs that they comprise, condition-drug class pairings, conditions that require chronic administration of medication, 11-4 counts of associated second level ATCs per Rx-defined morbidity groups, counts of associated therapeutic classes per Rx-defined morbidity groups, counts of associated third level ATCs per Rx-defined morbidity groups,

CSA example 1,

CSA example 2,

C-statistics,

C-statistics for prediction hospitalization, definitions of condition markers, distribution of the chronic condition count marker, duration, severity, etiology, and certainty of the aggregated diagnosis groups (ADGs),


Index effects of frailty conditions during a baseline year on next year’s hospitalization risk, total healthcare costs, and pharmacy costs, 6-4 effects of hospital dominant morbidity types during a baseline year on next year’s hospitalization risk, total healthcare costs, and pharmacy costs, 6-2 eligible specialties, 10-4 examples of diagnosic codes included in the hospital dominant morbidity types, 6-1 examples of ICD-10 code for diabetes complications,

4-6 examples of ICD-9-CM code for diabetes complications, final ACG categories, reference concurrent weights, and high cost patient with diabetes, 3-36 high cost patient with hypertension, 3-34

ICD-10 codes assigned to otitis media EDC TM

EDC (EAR01), impact type, 7-17 infant with high morbidity, low birthweight, 3-41 infant with low morbidity, normal birthweight, 3-41 low cost patient with diabetes, 3-36 low cost patient with hypertension, 3-34

MACs and the collapsed ADGs assigned to them,

MEDC types,

MPR,

MPR example 1,

4-15

4-6

4-3 face-to-face physician visits,

RUBs, 3-14 generalist specialties,

(EAR01),

MPR example 2, number of pharmacy codes for each Rx-MG, predictive models evaluated,

7-25

ICD-9-CM and ICD-10 codes assigned to otitis media

PPV and sensitivity for predicting hospitalization,

3-10 major ADGs for adult and pediatric populations, 3-7 major Rx-MG categories, 5-7 markers of pharmacy adherence/possession, 11-8 medically frail condition marker-frailty concepts and diagnoses,

11-10 ,

4-5

6-3

11-11

10-5

11-12

11-10

5-10 overlap of High Pharamacy Utilization Model and DX-

PM (top 1.5% of scores,

10-iii

8-2

9-6 predictive ratio by year 1 cost quintile for commercial and Medicare populations, 8-7 pregnancy/delivery with complications, high morbidity,

3-39 pregnancy/delivery with complications, low morbidity,

3-39 proportion of total healthcare costs consumed by the highest cost 10% and lowest cost 50% of enrollees,

7-4 proportion of variance explained (adjusted R-square values) using year 1 risk factors and year 2 resource measures for prior cost and ICD & NDC based combined models, 8-5


Index proportion of variance explained (adjusted r-square values) using year 1 risk factors and year 2 resource use measures for alternative predictive models,

Rx-MG morbidity taxonomy and clinical characteristics of medication assigned to each Rx-MG, selected ACG variables versus hospitalization as predictors of high cost patients,

10-5

2-1

7-3 sensitivity and PPV for top five percent, introduction, 1-2

The Johns Hopkins ACG system introduction, 1-1

8-9

5-14 specialties not eligible, supported coding systems, very high cost patient with diabetes, 3-37 very high cost patient with hypertension, 3-35

Target population , 7-10

Technical reference guide navigation, 1-1

Technical reference guide content , 1-2

8-3

U

Using ICD-9 and ICD-10 simultaneously , 2-4

Utilization markers , 9-3 active cancer treatment, 9-4 dialysis service, 9-3 emergency visit count, 9-4 inpatient hospitalization count, 9-4 major procedure, 9-4 nursing service, 9-4 outpatient visit count, 9-4

V

Validation and testing , 11-14

Validation data predictive modeling statistical performance, 8-1

IN-5


IN-6


Index
