The Johns Hopkins ACG® System
Version 11.0 Technical Reference Guide
November 2014
Important Warranty Limitation and Copyright Notices
Copyright 2014, The Johns Hopkins University. All rights reserved.
This document is produced by the Department of Health Policy and Management 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™, Aggregated Diagnostic Groups™, Ambulatory Diagnostic Groups™, Johns
Hopkins Expanded Diagnosis Clusters™, EDCs™, ACG® Predictive Model, Rx-Defined Morbidity
Groups™, Rx-MGs™, ACG® Rx Gaps, 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 co-workers 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
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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
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in connection with the use of the documentation, even if it has been or is hereafter advised of the
possibility of such damages.
The Johns Hopkins ACG® System Version 11.0 Technical Reference Guide
Contents
Contents
Chapter 1: Getting Started ......................................................................................................6
Introduction to The Johns Hopkins ACG® System .........................................................................................6
Customer Commitment and Contact Information.........................................................................................9
Chapter 2: Diagnosis-based Markers .....................................................................................10
Morbidity Types – Aggregated Diagnosis Groups (ADGs) ..........................................................................10
Patterns of Morbidity – Adjusted Clinical Groups (ACGs) .........................................................................17
Clinically Oriented Examples of ACGs ............................................................................................................29
Resource Utilization Bands (RUBs) .................................................................................................................35
Disease Markers - Expanded Diagnosis Clusters (EDCs) .............................................................................41
Special Population Markers .............................................................................................................................49
Chronic Condition Count ............................................................................................................................49
Hospital Dominant Morbidity Types ........................................................................................................55
Frailty Conditions .........................................................................................................................................56
Compassionate Care Allowances (CAL-SSA) ............................................................................................58
Pregnant ........................................................................................................................................................58
Delivered .......................................................................................................................................................58
Pregnancy without Delivery .......................................................................................................................59
Low Birth Weight (Less than 2500 Grams) .............................................................................................59
Chapter 3: Pharmacy-based Markers.....................................................................................60
Rx-Defined Morbidity Groups (Rx-MGs) ........................................................................................................60
Active Ingredient Count ...................................................................................................................................66
Chapter 4: Diagnosis+Pharmacy-based Markers....................................................................67
Condition Markers .............................................................................................................................................67
Pharmacy Adherence ........................................................................................................................................73
Chapter 5: Utilization and Resource Use Markers.................................................................88
Business Rules for Utilization Markers ..........................................................................................................88
All Cause Inpatient Hospitalization Count ...............................................................................................88
Inpatient Hospitalization Count.................................................................................................................88
Inpatient Hospitalization Days ...................................................................................................................89
Unplanned Inpatient Hospitalization Count ............................................................................................89
Readmission 30 Day Count ........................................................................................................................89
Unplanned Readmission 30 Day Count ...................................................................................................89
Emergency Visit Count ................................................................................................................................90
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The Johns Hopkins ACG® System Version 11.0 Technical Reference Guide
Contents
Outpatient Visit Count ................................................................................................................................90
Dialysis Service Marker ...............................................................................................................................90
Nursing Service Marker ..............................................................................................................................91
Major Procedure Marker ............................................................................................................................91
Cancer Treatment Marker ..........................................................................................................................91
Mechanical Ventilation Marker .................................................................................................................92
Psychotherapy Service Marker ..................................................................................................................92
Resource Bands ..................................................................................................................................................93
Chapter 6: Coordination ........................................................................................................94
Assessing Care Coordination ...........................................................................................................................94
Management Visit Count .................................................................................................................................96
Majority Source of Care (MSOC) ....................................................................................................................96
Unique Provider Count .....................................................................................................................................98
Specialty Count ..................................................................................................................................................99
Generalist Seen ................................................................................................................................................101
Risk of Poor Coordination ..............................................................................................................................101
Care Density .....................................................................................................................................................104
Conclusion.........................................................................................................................................................105
Chapter 7: Risk Modeling ....................................................................................................106
Elements of a Risk Model: Five Key Dimensions .......................................................................................106
Concurrent Cost Models ................................................................................................................................110
Prospective Cost Models ................................................................................................................................116
Chapter 8: Predictive Models for Hospitalization ................................................................123
Typology of Acute Care Hospitalizations .....................................................................................................123
How the ACG System Predicts Hospitalization ..........................................................................................123
Predictive Models for Hospitalization include Utilization Measures .....................................................124
Likelihood of Hospitalization .........................................................................................................................125
Empiric Validation of the Likelihood of Hospitalization Model ..............................................................126
Likelihood of Unplanned Readmission within 30 Days ............................................................................127
Empiric Validation of the Readmission Model ...........................................................................................128
Appendix A: Variables Necessary to Locally Calibrate the ACG® System Risk Models ........129
Introduction ......................................................................................................................................................129
Individual ACG Categories Included in the Risk Models ..........................................................................129
ACGs Included in Three Resource Groups ..................................................................................................130
Pregnancy Without Delivery ..........................................................................................................................131
Delivered ...........................................................................................................................................................131
EDCs Included in the Risk Models ................................................................................................................131
© 2014 The Johns Hopkins University. All rights reserved.
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The Johns Hopkins ACG® System Version 11.0 Technical Reference Guide
Contents
Rx-Defined Morbidity Groups™ (Rx-MGs) Included in the Risk Models ...............................................136
Special Population Markers ...........................................................................................................................138
Demographic Markers ....................................................................................................................................139
Optional Prior Cost Markers ..........................................................................................................................139
Utilization Markers ..........................................................................................................................................140
Prior Hospitalization Markers ........................................................................................................................140
Appendix B: Acknowledgements .........................................................................................141
Documentation Production Staff ..................................................................................................................141
Dedication .........................................................................................................................................................141
Support ..............................................................................................................................................................141
Third-Party Library Acknowledgements ......................................................................................................141
Index ..................................................................................................................................143
© 2014 The Johns Hopkins University. All rights reserved.
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The Johns Hopkins ACG® System Version 11.0 Technical Reference Guide
Chapter 1: Getting Started
Chapter 1:
Getting Started
Introduction to The Johns Hopkins ACG® System
The ACG System—characterized by excellence in both research and practice—is based at the Johns
Hopkins Bloomberg School of Public Health and has been used in performing risk measurement and casemix categorization for more than 25 years. The ACG System Team’s mission is to support healthcare
systems’ use of their available health information to measure the health care needs of their population,
provide equitable distribution and remuneration of services, and identify ways to improve the efficiency of
the delivery of care, with the primary objective being the improved health status of the population. The
ACG System Team consists of dedicated Johns Hopkins University faculty and staff with broad experience
in US and global health care systems. Their expertise includes extensive research on primary health care
delivery and financing, the ability to interpret morbidity patterns from a clinical as well as managerial
perspective, and the capacity to adapt models to fit the unique aspects of health care systems’ financial
and organizational structures. Moreover, the Team possesses cutting-edge knowledge of electronic health
records and health information technology, advanced analytics including model development, and a
comprehensive hands-on understanding of international coding systems (both diagnostic and
pharmaceutical).
The ACG System has been used to support basic and complex applications in finance, administration, care
delivery, and evaluative research for over two decades. These applications have been both concurrent
(retrospective) and forward-looking (prospective). The applications may involve simple spreadsheet
calculations or complex multi-variate statistical models. No other risk adjustment methodology has been
used for so many purposes in so many places, while at the same time showing such high levels of
quantitative and qualitative success. The flexibility offered by the ACG System demonstrates the
recognition that one size does not fit all. The Risk Adjustment Pyramid figure illustrates the variety of ways
in which risk adjustment is most commonly applied within healthcare organizations today. Some
implementations, such as needs assessments or payment/financial applications apply to the entire
population base. Other implementations, such as care-management or disease-management
interventions, focus only on targeted sub-populations.
© 2014 The Johns Hopkins University. All rights reserved.
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The Johns Hopkins ACG® System Version 11.0 Technical Reference Guide
Chapter 1: Getting Started
Figure 1. Risk Adjustment Pyramid
The Overview of The Johns Hopkins ACG System figure illustrates the inputs to, and outputs from, the
ACG-based analytics. In very simple terms, a patient file (identifying eligible individuals) is merged with
diagnoses and pharmacy codes, to produce a series of risk factors and risk scores. The Installation and
Usage Guide provides basic instructions on how to prepare input data files, select model options with
which to process the data, and describes how to validate the output produced by the ACG Software. The
full listing of risk factors assigned within the software is in Appendix A of the Installation and Usage Guide.
The theoretical and conceptual basis of each of these markers as well as how they are combined and
evaluated for modeling purposes is explicated in the Technical Reference Guide.
© 2014 The Johns Hopkins University. All rights reserved.
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The Johns Hopkins ACG® System Version 11.0 Technical Reference Guide
Chapter 1: Getting Started
Figure 2. Overview of The Johns Hopkins ACG System
Finally, the Common Applications figure describes how the ACG System is typically applied. Each potential
application is described in more detail in the Applications Guide.
Figure 3. Common Applications of the ACG System
The ACG System has been used for a wide variety of applications in many contexts. The full ACG System
bibliography is available in the Resource Center of the ACG website: http://acg.jhsph.org/index.php
/resource-center-83/acg-bibliography
© 2014 The Johns Hopkins University. All rights reserved.
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The Johns Hopkins ACG® System Version 11.0 Technical Reference Guide
Chapter 1: Getting Started
Customer Commitment and Contact Information
As part of our ongoing commitment to furthering the state-of-the-art of risk-adjustment methodology and
supporting users of the ACG System, we will continue to perform evaluation, research, and development.
We will look forward to sharing the results of this work with our user-base through white papers, our web
site, peer-reviewed articles, and in-person presentations. After you have carefully reviewed the
documentation supplied with this software release, we 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 e-mail
JHSPH.askacg@jhu.edu.
We thank you for using the ACG System and for helping us to work toward meeting the ACG Team's
ultimate goal of improving the quality, efficiency, and equity of health care across the United States and
around the globe.
© 2014 The Johns Hopkins University. All rights reserved.
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The Johns Hopkins ACG® System Version 11.0 Technical Reference Guide
Chapter 2: Diagnosis-based Markers
Chapter 2:
Diagnosis-based Markers
The Johns Hopkins ACG® System is a statistically valid, 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.
The ACG System provides a number of markers derived from a patient's diagnosis code history from all
encounters during a 12-month period. This chapter provides definition for the ACG System markers
derived from diagnosis information.
Morbidity Types – Aggregated Diagnosis Groups (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 diagnosis
code to one or more of 32 diagnosis groups referred to as Aggregated Diagnosis Groups, or ADGs. The
diagnosis-to-ADG mapping embedded in the ACG Software includes an ADG assignment for all1 ICD codes.
Where a single diagnosis code indicates more than one underlying morbidity type, more than one ADG
may be assigned. For example, in ICD-10 the code E11.31 (Type 2 diabetes mellitus with unspecified
diabetic retinopathy) would trigger both ADG 18 (Chronic Specialty: Unstable-Eye) and ADG 11 (Chronic
Medical: Unstable).
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). The following table lists the 32 ADGs and exemplary diagnosis codes.
ADGs and Common Diagnosis Codes Assigned to Them
ADGs
ICD9-CM
ICD-10
Diagnosis
1. Time Limited: Minor
558.9
691.0
K52.9
L22
Noninfectious Gastroentritis
Diaper or Napkin Rash
2. Time Limited: Minor-Primary
Infections
079.9
464.4
B09
J05.0
Unspecified Viral Infection
Croup
3. Time Limited: Major
451.2
560.3
I80.3
K56.7
Phlebitis of Lower Extremities
Impaction of Intestine
4. Time Limited: Major-Primary
Infections
573.3
711.0
K75.9
M00.9
Hepatitis, Unspecified
Pyogenic Arthritis
1 Because they indicate the cause of injury rather than an underlying morbidity, ICD-9 codes beginning with E and ICD-10
codes beginning V through Y have generally been excluded from the Diagnosis-to-ADG mapping. The source of codes is
the Center for Medicare and Medicaid Services (CMS) list of ICD-9 and ICD-10-CM codes (available for download at
http://www.cms.gov). ICD-10 codes are sourced from the Official ICD-10 Updates published by the World Health
Organization (WHO).
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The Johns Hopkins ACG® System Version 11.0 Technical Reference Guide
Chapter 2: Diagnosis-based Markers
ADGs
ICD9-CM
ICD-10
Diagnosis
5. Allergies
477.9
708.9
J30.0
L50.9
Allergic Rhinitis, Cause
Unspecified
Unspecified Urticaria
6. Asthma
493.0
493.1
J45.0
J45.1
Extrinsic Asthma
Intrinsic Asthma
7. Likely to Recur: Discrete
274.9
724.5
M10.9
M54.9
Gout, Unspecified
Backache, Unspecified
8. Likely to Recur: DiscreteInfection
474.0
599.0
J35.1
N39.0
Chronic Tonsillitis
Urinary Tract Infection
9. Likely to Recur: Progressive
250.10
434.0
E11.1
I66.9
Adult Onset Type II Diabetes
w/Ketoacidosis
Cerebral Thrombosis
10. Chronic Medical: Stable
250.00
401.9
E10.9
I10
Adult-Onset Type 1 Diabetes
Essential Hypertension
11. Chronic Medical: Unstable
282.6
277.0
D57.1
E84.0
Sickle-Cell Anemia
Cystic Fibrosis
12. Chronic Specialty: StableOrthopedic
721.0
718.8
M48.9
M24.9
Cervical Spondylosis Without
Myelopathy
Other Joint Derangement
13. Chronic Specialty: StableEar, Nose, Throat
389.14
385.3
H90.5
H71
Central Hearing Loss
Cholesteatoma
14. Chronic Specialty: StableEye
367.1
372.9
H52.1
H11.9
Myopia
Unspecified Disorder of
Conjunctiva
16. Chronic Specialty: Unstable- 724.02
Orthopedic
732.7
M48.0
M92.8
Spinal Stenosis of Lumbar
Region
Osteochondritis Dissecans
17. Chronic Specialty: Unstable- 386.0
Ear, Nose, Throat
383.1
H81.0
H70.1
Meniere's Disease
Chronic Mastoiditis
18. Chronic Specialty: Unstable- 365.9
Eye
379.0
H40.9
H15.0
Unspecified Glaucoma
Scleritis/Episcleritis
20. Dermatologic
078.1
448.1
A63.0
I78.1
Viral Warts
Nevus, Non-Neoplastic
21. Injuries/Adverse Effects:
Minor
847.0
959.1
S13.4
T09.0
Neck Sprain
Injury to Trunk
22. Injuries/Adverse Effects:
Major
854.0
972.1
S06
T46.0
Intracranial Injury
Poisoning by Cardiotonic
Glycosides and Similar Drugs
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The Johns Hopkins ACG® System Version 11.0 Technical Reference Guide
Chapter 2: Diagnosis-based Markers
ADGs
ICD9-CM
ICD-10
Diagnosis
23. Psychosocial: Time Limited,
Minor
305.2
309.0
F12.1
F32.0
Cannabis Abuse, Unspecified
Brief Depressive Reaction
24. Psychosocial: Recurrent or
Persistent, Stable
300.01
307.51
F41.0
F50.3
Panic Disorder
Bulimia
25. Psychosocial: Recurrent or
Persistent, Unstable
295.2
291.0
F20.2
F10.3
Catatonic Schizophrenia
Alcohol Withdrawal Delirium
Tremens
26. Signs/Symptoms: Minor
784.0
729.5
G44.1
M79.6
Headache
Pain in Limb
27. Signs/Symptoms: Uncertain
719.06
780.7
M25.4
R53
Effusion of Lower Leg Joint
Malaise and Fatigue
28. Signs/Symptoms: Major
429.3
780.2
I51.7
R55
Cardiomegaly
Syncope and Collapse
29. Discretionary
550.9
706.2
K40
L72.1
Inguinal Hernia (NOS)
Sebaceous Cyst
30. See and Reassure
611.1
278.1
N62
E65
Hypertrophy of Breast
Localized Adiposity
31. Prevention/Administrative
V20.2
V72.3
Z00.1
Z01.4
Routine Infant or Child Health
Check
Gynecological Examination
32. Malignancy
174.9
201.9
C50
C81.9
Malignant Neoplasm of Breast
(NOS)
Hodgkin's Disease, Unspecified
Type
33. Pregnancy
V22.2
650.0
Z33
080.0
Pregnant State
Delivery in a Completely Normal
Case
34. Dental
521.0
523.1
K02
K05.1
Dental Caries
Chronic Gingivitis
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 chronic 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 Installation and Usage Guide.
ADGs are distinguished by several clinical characteristics (time limited or not, requiring primary care or
specialty care, or addressing physical health or psycho-social needs) and the degree of refinement of the
problem (diagnosis or symptom/sign). ADGs 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.
© 2014 The Johns Hopkins University. All rights reserved.
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The Johns Hopkins ACG® System Version 11.0 Technical Reference Guide
Chapter 2: Diagnosis-based Markers
The need for healthcare resources is primarily determined by the likelihood of persistence of problems
and their level of severity.
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.
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 resources. 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. The Duration, Severity, Etiology, and Certainty of the ADGs
table illustrates 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 intermediate 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 (i.e., more likely to need specialty care).
Diagnostic Certainty
Will a diagnostic evaluation be needed or will treatment be the primary focus? 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.
© 2014 The Johns Hopkins University. All rights reserved.
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The Johns Hopkins ACG® System Version 11.0 Technical Reference Guide
Chapter 2: Diagnosis-based Markers
Expected Need for Specialty Care
Would the majority of patients with this condition be expected to require specialty care management
from a non-primary care provider? The routine course of care for some ADG categories implies that
specialty care is more likely.
Duration, Severity, Etiology, and Certainty of the ADGs
Note: ADGs 15 and 19 are no longer used.
Diagnostic
Certainty
Expected Need
for Specialty
Care
ADG
Duration
Severity
Etiology
1. Time Limited:
Minor
Acute
Low
Medical, noninfectious
High
Unlikely
2. Time Limited:
Minor-Primary
Infections
Acute
Low
Medical,
infectious
High
Unlikely
3. Time Limited:
Major
Acute
High
Medical, noninfectious
High
Likely
4. Time Limited:
Major-Primary
Infections
Acute
High
Medical,
infectious
High
Likely
5. Allergies
Recurrent
Low
Allergy
High
Possibly
6. Asthma
Recurrent or
Chronic
Low
Mixed
High
Possibly
7. Likely to
Recur: Discrete
Recurrent
Low
Medical, noninfectious
High
Unlikely
8. Likely to
Recur: DiscreteInfections
Recurrent
Low
Medical,
infectious
High
Unlikely
9. Likely to
Recur:
Progressive
Recurrent
High
Medical, noninfectious
High
Likely
10. Chronic
Medical: Stable
Chronic
Low
Medical, noninfectious
High
Unlikely
11. Chronic
Medical:
Unstable
Chronic
High
Medical, noninfectious
High
Likely
12. Chronic
Specialty: StableOrthopedic
Chronic
Low
Anatomic/Muscu
loskeletal
High
Likely:
orthopedics
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The Johns Hopkins ACG® System Version 11.0 Technical Reference Guide
Chapter 2: Diagnosis-based Markers
ADG
Duration
Diagnostic
Certainty
Expected Need
for Specialty
Care
Severity
Etiology
13. Chronic
Chronic
Specialty: StableEar, Nose, Throat
Low
Anatomic/Ears,
Nose, Throat
High
Likely: ENT
14. Chronic
Specialty: StableOphthalmology
Chronic
Low
Anatomic/Eye
High
Likely:
ophthalmology
16. Chronic
Specialty:
UnstableOrthopedics
Chronic
High
Anatomic/Muscu
loskeletal
High
Likely:
orthopedics
17. Chronic
Specialty:
Unstable-Ear,
Nose, Throat
Chronic
High
Anatomic/Ears,
Nose, Throat
High
Likely: ENT
18. Chronic
Specialty:
UnstableOphthalmology
Chronic
High
Anatomic/Eye
High
Likely:
ophthalmology
20. Dermatologic
Acute, Recurrent
Low to High
Mixed
High
Likely:
dermatology
21.
Injuries/Adverse
Effects: Minor
Acute
Low
Injury
High
Unlikely
22.
Injuries/Adverse
Effects: Major
Acute
High
Injury
High
Likely
23. Psychosocial:
Time Limited,
Minor
Acute
Low
Psychosocial
High
Unlikely
24. Psychosocial:
Recurrent or
Chronic, Stable
Recurrent or
Chronic
Low
Psychosocial
High
Likely: mental
health
25. Psychosocial:
Recurrent or
Persistent,
Unstable
Recurrent or
Chronic
High
Psychosocial
High
Likely: mental
health
26.
Signs/Symptoms:
Minor
Uncertain
Low
Mixed
High
Unlikely
© 2014 The Johns Hopkins University. All rights reserved.
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The Johns Hopkins ACG® System Version 11.0 Technical Reference Guide
Chapter 2: Diagnosis-based Markers
Expected Need
for Specialty
Care
ADG
Duration
Severity
Etiology
Diagnostic
Certainty
27.
Signs/Symptoms:
Uncertain
Uncertain
Uncertain
Mixed
High
Uncertain
28.
Signs/Symptoms:
Major
Uncertain
High
Mixed
Low
Likely
29. Discretionary
Acute
Low to High
Anatomic
High
Likely: surgical
specialties
30. See and
Reassure
Acute
Low
Anatomic
High
Unlikely
31.
Prevention/Admi
nistrative
N/A
N/A
N/A
N/A
Unlikely
32. Malignancy
Chronic
High
Neoplastic
High
Likely: oncology
33. Pregnancy
Acute
Low
Pregnancy
High
Likely: obstetrics
34. Dental
Acute, Recurrent, Low to High
Chronic
Mixed
High
Likely: dental
Major ADGs
Some ADGs have very high expected resource use and are labeled as Major ADGs. The following table
presents major ADGs for adult and pediatric populations.
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
3 Time Limited: Major
9 Likely to Recur: Progressive
4 Time Limited: Major-Primary Infections
11 Chronic Medical: Unstable
9 Likely to Recur: Progressive
12 Chronic Specialty: Stable-Orthopedic
11 Chronic Medical: Unstable
13 Chronic Specialty: Stable-Ear, Nose, Throat
16 Chronic Specialty: Unstable-Orthopedic
18 Chronic Specialty: Unstable-Eye
22 Injuries/Adverse Effects: Major
25 Psychosocial: Recurrent or Persistent, Unstable
25 Psychosocial: Recurrent or Persistent, Unstable
32 Malignancy
32 Malignancy
While the primary use of ADGs is as a means for collapsing all diagnosis codes into clinically meaningful
morbidity types as a first step in the ACG assignment process, ADGs are useful as a risk assessment tool in
their own right. There are many examples in the literature of using ADG markers as generic case-mix
control variables. The most common application is the introduction of individual ADG-markers as binary
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flags in a regression model, but something as simple as a count of ADGs or Major ADGs can be a very
powerful indicator of need as well.
Relationship Between Number and Major Morbidities in Year 1 and Likelihood of Subsequent
High Cost
Positive Predictive Value
Number of Year 1 Major
Morbidities
Percent of Members
Percent High Cost in
Year 2
Percent High Cost in
Year 3
0 Major ADGs
77.1%
9.6%
11.0%
1 Major ADG
17.3%
20.9%
21.5%
2 Major ADGs
4.2%
34.7%
34.1%
3 Major ADGs
1.1%
43.6%
45.6%
4+ Major ADGs
0.4%
72.4%
70.1%
Patterns of Morbidity – Adjusted Clinical Groups (ACGs)
Adjusted Clinical Group actuarial cells, or ACGs, are the building blocks of The Johns Hopkins 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.
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.
The concept of ACGs 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.
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
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basis of the current ACG methodology and remains the fundamental concept that differentiates ACGs
from other case-mix adjustment methodologies.
There are four steps in the ACG assignment process:
1. Mapping Diagnosis Codes to a Parsimonious Set of Aggregated ADGs
2. Creating a Manageable Number of ADG Subgroups (CADGs)
3. Frequently Occurring Combinations of CADGs (MACs)
4. Forming the Terminal Groups (ACGs)
The first step is described in the preceding section while the remainder are summarized in the following
tables and figures depicting the ACG-decision-tree logic.
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
(presented in the following table). 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 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.
Collapsed ADG Clusters and the ADGs that Comprise Them
Collapsed ADG (CADG)
ADGs in Each
1. Acute Minor
1 Time Limited: Minor
2 Time Limited: Minor-Primary Infections
21 Injuries/Adverse Events: Minor
26 Signs/Symptoms: Minor
2. Acute Major
3 Time Limited: Major
4 Time Limited: Major-Primary Infections
22 Injuries/Adverse Events: Major
27 Signs/Symptoms: Uncertain
28 Signs/Symptoms: Major
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Collapsed ADG (CADG)
ADGs in Each
3. Likely to Recur
5 Allergies
7 Likely to Recur: Discrete
8 Likely to Recur: Discrete-Infections
20 Dermatologic
29 Discretionary
4. Asthma
6 Asthma
5. Chronic Medical: Unstable
9 Likely to Recur: Progressive
11 Chronic Medical: Unstable
32 Malignancy
6. Chronic Medical: Stable
10 Chronic Medical: Stable
30 See and Reassure
7. Chronic Specialty: Stable
12 Chronic Specialty: Stable-Orthopedic
13 Chronic Specialty: Stable-Ear, Nose, Throat
8. Eye/Dental
14 Chronic Specialty: Stable-Eye
34 Dental
9. Chronic Specialty: Unstable
16 Chronic Specialty: Unstable-Orthopedic
17 Chronic Specialty: Unstable-Ear, Nose, Throat
8 Chronic Specialty: Unstable-Eye
10. Psychosocial
23 Psycho-social: Time Limited, Minor
24 Psycho-social: Recurrent or Persistent, Stable
25 Psycho-social: Recurrent or Persistent, Unstable
11. Preventive/Administrative
31 Prevention/Administrative
12. Pregnancy
33 Pregnancy
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. The MACs and
the Collapsed ADGs Assigned to Them table 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.
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MACs and the Collapsed ADGs Assigned to Them
MACs
CADGs
1. Acute: Minor
1
2. Acute: Major
2
3. Likely to Recur
3
4. Asthma
4
5. Chronic Medical: Unstable
5
6. Chronic Medical: Stable
6
7. Chronic Specialty: Stable
7
8. Eye/Dental
8
9. Chronic Specialty: Unstable
9
10. Psychosocial
10
11. Prevention/Administrative
11
12. Pregnancy
All CADG combinations that include CADG 12
13. Acute: Minor and Acute: Major
1 and 2
14. Acute: Minor and Likely to Recur
1 and 3
15. Acute: Minor and Chronic Medical: Stable
1 and 6
16. Acute: Minor and Eye/Dental
1 and 8
17. Acute: Minor and Psychosocial
1 and 10
18. Acute: Major and Likely to Recur
2 and 3
19. Acute: Minor and Acute: Major and Likely to Recur
1, 2 and 3
20. Acute: Minor and Likely to Recur and Eye and
Dental
1, 3 and 8
21. Acute: Minor and Likely to Recur and Psychosocial
1, 3, and 10
22. Acute: Minor and Major and Likely to Recur and
Chronic Medical: Stable
1, 2, 3, and 6
23. Acute: Minor and Major and Likely to Recur and
Psychosocial
1, 2, 3, and 10
24. All Other Combinations Not Listed Above
All Other Combinations
25. No Diagnosis or Only Unclassified Diagnosis
No CADGs
26. Infants (age less than one year)
Any CADGs combinations and less than one year old
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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, ADG 31 is not included in the
count of total ADGs2.
See the Final ACG Categories, Reference ACG Concurrent Risks, and RUBs table on page 36 for a complete
listing and description of all ACGs.
ACG Decision Tree
The ACG Decision Tree figure 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).
2 Refer 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.
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Figure 4. ACG Decision Tree
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Decision Tree for MAC-12—Pregnant Women
The Decision Tree for MAC-12—Pregnant Women 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 optionally 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 a user-supplied delivery flag 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 Pregnant on page 58 and Delivered on page 58 for a more detailed discussion of appropriate
means of identifying pregnancy and delivery status using user-defined flags.
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Figure 5. Decision Tree for MAC-12—Pregnant Women
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Decision Tree for MAC-26—Infants
The Decision Tree for MAC-26—Infants 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 Low Birth Weight (Less than 2500 Grams) on page 59 for a more detailed discussion of
appropriate means of identifying low birth weight status using user-defined flags.
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Figure 6. Decision Tree for MAC-26—Infants
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Decision Tree for MAC-24—Multiple ADG Categories
The Decision Tree for MAC-24—Multiple ADG Categories illustrates the last branch of the ACG tree, MAC24, 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.
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Figure 7. Decision Tree for MAC-24—Multiple ADG Categories
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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
In the following examples, 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 for 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 the 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.
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Patient 1: Low Cost Patient with Hypertension
Input Data/Patient
Characteristics
Age/Sex: 51/Male
ACG Output
Resource Consumption Variables
ACG-0900: Chronic Medical,
Stable
Total Cost: $128
Ambulatory visits: 1
Emergency visits: 0
Hospitalizations: 0
Conditions: Hypertension, General ADGs: 10 and 31.
Medical Exam
Chronic Medical: Stable,
Prevention/Administrative
Patient 2: High Cost Patient with Hypertension
Input Data/Patient
Characteristics
ACG Output
Resource Consumption Variables
Age/Sex: 54/Male
ACG-3600: Acute Minor/Acute
Major/Likely Recur/Eye & Dental
Conditions: Hypertension,
Disorders of Lipid Metabolism,
Low Back Pain, Cervical Pain
Syndromes, Musculoskeletal Signs
and Symptoms
ADGS: 07, 10, 26, and 27
Likely to Recur: Discrete Chronic
Medical: Stable
Signs/Symptoms: Minor
Signs/Symptoms: Uncertain
Total Cost: $3,268
Ambulatory visits: 1
Emergency visits: 1
Hospitalizations: 0
Patient 3: Very High Cost Patient with Hypertension
Input Data/Patient
Characteristics
ACG Output
Resource Consumption Variables
Age/Sex: 52/Male
ACG - 5070: 10+Other ADG
Combinations, Age >17, 4+ Major
ADGs
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
ADGs: 02, 03*, 06, 07, 09*, 10,
11*, 16*, 24, 27, 28, and 31.
Time Limited: Minor-Primary
Infections
Time Limited: Major, Asthma
Likely to Recur: Discrete
Likely to Recur: Progressive
Chronic Medical: Stable Chronic
Medical: Unstable Chronic
Specialty: Unstable-Orthopedic
Psychosocial: Recurrent or
Persistent
Stable Signs/Symptoms: Uncertain
Signs/Symptoms: Major, and
Prevention/Administrative
Total Cost: $45,937
Ambulatory visits: 17
Emergency visits: 0
Hospitalizations: 1
*Major ADG
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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
Input Data/Patient
Characteristics
ACG Output
Resource Consumption Variables
Age/Sex: 49/Female
ACG-0900: Chronic Medical,
Stable
Conditions: Diabetes mellitus
ADGs: 1 0
Chronic Medical: Stable
Total Cost: $296
Ambulatory visits: 1
Emergency visits: 0
Hospitalizations: 0
Patient 2: High Cost Patient with Diabetes
Input Data/Patient
Characteristics
ACG Output
Age/Sex: 49/Female
ACG-3600: Acute Minor/Acute
Major/Likely Recur/Eye & Dental
Conditions: Diabetes mellitus,
Disorders of Lipid Metabolism,
Peripheral Neuropathy, Otitis
Media, Gastroesophageal Reflux,
Acute sprain, Joint disorder,
Bursitis, Arthropathy
Resource Consumption Variables
Total Cost: $1,698
Ambulatory visits: 6
ADGS: 01, 07, 08, 10, 22*, 26, and Emergency visits: 1
Hospitalizations: 0
27
Time Limited: Minor
Likely to Recur: Discrete
Likely to Recur: DiscreteInfections
Chronic Medical: Stable
Injuries/Adverse Effects: Major
Signs/Symptoms: Minor
Signs/Symptoms: Uncertain
*Major ADG
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Patient 3: Very High Cost Patient with Diabetes
Input Data/Patient
Characteristics
ACG Output
Resource Consumption Variables
Age/Sex: 51/Female
ACG - 5070: 10+Other ADG
Combinations, Age >17, 4+ Major
ADGs
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
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
Likely to Recur: Discrete
Likely to Recur: DiscreteInfections
Likely to Recur: Progressive
Chronic Medical: Stable
Chronic Medical: Unstable Chronic
Specialty: Stable-Orthopedic
Chronic Specialty: UnstableOrthopedic
Chronic Specialty: Unstable-Ear,
Nose, Throat
Injuries/Adverse Effects: Minor
Injuries/Adverse Effects: Major
Psychosocial: Time Limited, Minor
Signs/Symptoms: Minor
Signs/Symptoms: Uncertain
Major, Discretionary,
See/Reassure, and
Prevention/Administrative
Total Cost: $33,073
Ambulatory visits: 23
Emergency visits: 2
Hospitalizations: 1
*Major ADG
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
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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.
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
Input Data/Patient
Characteristics
ACG Output
Resource Consumption Variables
Age/Sex: 32/Female
ACG-1731: 2-3 ADGs, 1+ Major
ADGs, Delivered
Conditions: General medical
exam, Pregnancy and delivery uncomplicated and Pregnancy and
delivery - with complications.
ADGs: 01, 03*, 31, and 33.
Time Limited: Minor
Time Limited: Major
Prevention/Administrative, and
Pregnancy
Total Cost: $8,406
Ambulatory visits: 3
Emergency visits: 0
Hospitalizations: 1
*Major ADG
Patient 2: Pregnancy/Delivery with Complications, High Morbidity
Input Data/Patient
Characteristics
ACG Output
Resource Consumption Variables
Age/Sex: 36/Female
ACG-1771: 6+ ADGs, 1+ Major
ADGs, Delivered
Conditions: General medical exam
Hypertension, Low Back Pain,
Urinary tract infection, Renal
Calculi, Cervical Pain Syndromes,
Joint disorder, Pregnancy and
delivery-with complications.
ADGs: 03*, 07, 08, 10, 11*, 21,
22*, 28, 31, and 33.
Time Limited: Major
Likely to Recur: Discrete
Likely to Recur: Discreteinfections
Chronic Medical: Stable
Chronic Medical: Unstable
Injuries/Adverse Effects: Minor
Injuries/Adverse Effects: Major
Signs/Symptoms: Major
Prevention/Administrative, and
Pregnancy
Total Cost: $19,714
Ambulatory visits: 13
Emergency visits: 2
Hospitalizations: 1
*Major ADG
The Clinical Classification of Pregnancy/Delivery ACGs table 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
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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.
Clinical Classification of Pregnancy/Delivery ACGs
ACG Levels
Pregnancy Only
Delivered
Morbidity Level
Uncomplicated (No Complicated (1+
Major ADGs)
Major ADGs )
Uncomplicated (No Complicated (1+
Major ADGs)
Major ADGs)
Low
(1-3 ADGs)
1712, 1722
1732
1711, 1721
1731
Mid
(4-5 ADGs)
1742
1752
1741
1751
High
(6+ ADGs)
1762
1772
1761
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. Example 4: Infants compares an infant in the low morbidity/no complications
ACG (5310, the most frequently assigned infant ACG) to 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
Input Data/Patient
Characteristics
ACG Output
Resource Consumption Variables
Age/Sex: 0/Female
ACG 5312: 0-5 ADGs, No Major
ADGs, Normal Birthweight,
Conditions: General medical exam
Otitis media, Acute upper
respiratory tract infection, Fungal
infection and Gastroesophageal
Reflux
ADGs: 02, 08, 26, and 31
Time Limited: Minor
Likely to Recur: DiscreteInfections
Signs/Symptoms: Minor, and
Prevention/Administration
Total Cost: $3,208
Ambulatory visits: 17
Emergency visits: 0
Hospitalizations: 1
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Chapter 2: Diagnosis-based Markers
Patient 2: Infant with High Morbidity, Low Birthweight
Input Data/Patient
Characteristics
ACG Output
Resource Consumption Variables
Age/Sex: 0/Male
ACG 5341: 6+ ADGs, 1+ Major
ADGs, Low Birthweight
Conditions: General medical
exam, Respiratory symptoms
Congenital Heart Disease, Cardiac
Arrhythmia, Septicemia, Nausea,
vomiting, Gastroesophageal
Reflux, Neonatal Jaundice, Renal
Disorders, Endocrine disorders,
Vesicouretal reflux
ADGs: 03*, 04, 07 , 10, 11*, 22,
26, 27, 28, and 31
Time Limited: Major
Time Limited: Major-Primary
Infections
Likely to Recur: Discrete
Chronic Medical: Stable, Chronic
Medical: Unstable
Injuries/Adverse Effects: Major
Signs/Symptoms: Minor
Signs/Symptoms: Uncertain
Signs/Symptoms: Major,
Discretionary, and
Prevention/Administrative
Total Cost: $165,142
Ambulatory visits: 19
Emergency visits: 0
Hospitalizations: 1
*Major ADG
The Clinical Classification of Infant ACGs table provides a clinical classification of the infant ACGs.
Clinical Classification of Infant ACGs
Low Birthweight
Normal Birthweight
Morbidity Level
No Complications
(no Major ADGs)
Complication (1+
Major ADGs)
No Complications
(no Major ADGs)
Complication (1+
Major ADGs)
Low (0-5 ADGs)
5311
5321
5312
5322
Mid (6+ ADGs)
5331
5341
5332
5342
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
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Chapter 2: Diagnosis-based Markers
•
•
•
•
2 - Low
3 - Moderate
4 - High
5 - Very High
The relationship between ACG categories and RUBs is defined in the following table
Final ACG Categories, Reference ACG Concurrent Risks, and RUBs
Non-Elderly (0 to
64 Years)
Elderly (65 Years
and Older)
Acute Minor, Age 1
0.314
N/A
2
0200
Acute Minor, Age 2 to 5
0.135
N/A
1
0300
Acute Minor, Age > 5
0.131
0.081
1
0400
Acute Major
0.283
0.144
2
0500
Likely to Recur, w/o Allergies
0.193
0.116
2
0600
Likely to Recur, with Allergies
0.199
0.090
2
0700
Asthma
0.210
0.111
2
0800
Chronic Medical, Unstable
1.220
0.312
3
0900
Chronic Medical, Stable
0.298
0.127
2
1000
Chronic Specialty, Stable
0.185
0.141
2
1100
Eye/Dental
0.093
0.076
1
1200
Chronic Specialty, Unstable
0.188
0.100
2
1300
Psychosocial, w/o Psych Unstable
0.281
0.113
2
1400
Psychosocial, with Psych Unstable, w/o Psych
Stable
0.653
0.218
3
1500
Psychosocial, with Psych Unstable, w/ Psych
Stable
1.026
0.218
3
1600
Preventive/Administrative
0.095
0.074
1
1710*
Pregnancy: 0-1 ADGs
1.758
N/A
3
1711
Pregnancy: 0-1 ADGs, delivered
2.510
N/A
4
1712
Pregnancy: 0-1 ADGs, not delivered
0.358
N/A
2
1720*
Pregnancy: 2-3 ADGs, no Major ADGs
2.033
N/A
3
1721
Pregnancy: 2-3 ADGs, no Major ADGs, delivered
2.888
N/A
4
1722
Pregnancy: 2-3 ADGs, no Major ADGs, not
delivered
0.596
N/A
3
1730*
Pregnancy: 2-3 ADGs, 1+ Major ADGs
2.572
N/A
4
ACG
Description
0100
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Non-Elderly (0 to
64 Years)
Elderly (65 Years
and Older)
Pregnancy: 2-3 ADGs, 1+ Major ADGs, delivered
3.195
N/A
4
1732
Pregnancy: 2-3 ADGs, 1+ Major ADGs, not
delivered
0.914
N/A
3
1740*
Pregnancy: 4-5 ADGs, no Major ADGs
2.234
N/A
4
1741
Pregnancy: 4-5 ADGs, no Major ADGs, delivered
3.197
N/A
4
1742
Pregnancy: 4-5 ADGs, no Major ADGs, not
delivered
0.962
N/A
3
1750*
Pregnancy: 4-5 ADGs, 1+ Major ADGs
2.938
N/A
4
1751
Pregnancy: 4-5 ADGs, 1+ Major ADGs, delivered
3.722
N/A
4
1752
Pregnancy: 4-5 ADGs, 1+ Major ADGs, not
delivered
1.332
N/A
3
760*
Pregnancy: 6+ ADGs, no Major ADGs
2.553
N/A
4
1761
Pregnancy: 6+ ADGs, no Major ADGs, delivered
3.636
N/A
4
1762
Pregnancy: 6+ ADGs, no Major ADGs, not
delivered
1.537
N/A
3
1770*
Pregnancy: 6+ ADGs, 1+ Major ADGs
4.060
N/A
4
1771
Pregnancy: 6+ ADGs, 1+ Major ADGs, delivered
5.000
N/A
4
1772
Pregnancy: 6+ ADGs, 1+ Major ADGs, not
delivered
2.897
N/A
4
1800
Acute Minor and Acute Major
0.432
0.190
2
1900
Acute Minor and Likely to Recur, Age 1
0.456
N/A
2
2000
Acute Minor and Likely to Recur, Age 2 to 5
0.241
N/A
2
2100
Acute Minor and Likely to Recur, Age > 5, w/o
Allergy
0.262
0.118
2
2200
Acute Minor and Likely to Recur, Age > 5, with
Allergy
0.287
0.109
2
2300
Acute Minor and Chronic Medical: Stable
0.354
0.150
2
2400
Acute Minor and Eye/Dental
0.181
0.092
2
2500
Acute Minor and Psychosocial, w/o Psych
Unstable
0.341
0.142
2
2600
Acute Minor and Psychosocial, with Psych
Unstable, w/o Psych Stable
0.740
0.320
3
2700
Acute Minor and Psychosocial, with Psych
Unstable and Psych Stable
1.259
0.320
3
ACG
Description
1731
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Non-Elderly (0 to
64 Years)
Elderly (65 Years
and Older)
Acute Minor and Likely to Recur
0.499
0.213
3
2900
Acute Minor/Acute Major/Likely to Recur, Age 1
0.827
N/A
3
3000
Acute Minor/Acute Major/Likely to Recur, Age 2
to 5
0.508
N/A
3
3100
Acute Minor/Acute Major/Likely to Recur, Age 6
to 11
0.468
N/A
3
3200
Acute Minor/Acute Major/Likely to Recur, Age >
11, w/o Allergy
0.747
0.288
3
3300
Acute Minor/Acute Major/Likely to Recur, Age >
11, with Allergy
0.730
0.308
3
3400
Acute Minor/Likely to Recur/Eye & Dental
0.325
0.144
2
3500
Acute Minor/Likely to Recur/Psychosocial
0.558
0.207
3
3600
Acute Minor/Acute Major/Likely Recur/Eye &
Dental
1.311
0.457
3
3700
Acute Minor/Acute Major/Likely
Recur/Psychosocial
1.142
0.513
3
3800
2-3 Other ADG Combinations, Age < 18
0.415
N/A
2
3900
2-3 Other ADG Combinations, Males Age 18 to
34
0.541
N/A
3
4000
2-3 Other ADG Combinations, Females Age 18 to
34
0.476
N/A
3
4100
2-3 Other ADG Combinations, Age > 34
0.663
0.259
3
4210
4-5 Other ADG Combinations, Age < 18, no
Major ADGs
0.557
N/A
3
4220
4-5 Other ADG Combinations, Age < 18, 1+
Major ADGs
1.071
N/A
3
4310
4-5 Other ADG Combinations, Age 18 to 44, no
Major ADGs
0.638
N/A
3
4320
4-5 Other ADG Combinations, Age 18 to 44, 1+
Major ADGs
1.273
N/A
3
4330
4-5 Other ADG Combinations, Age 18 to 44, 2+
Major ADGs
2.307
N/A
4
4410
4-5 Other ADG Combinations, Age > 44, no
Major ADGs
0.816
0.275
3
4420
4-5 Other ADG Combinations, Age > 44, 1+
Major ADGs
1.525
0.467
3
ACG
Description
2800
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Chapter 2: Diagnosis-based Markers
Non-Elderly (0 to
64 Years)
Elderly (65 Years
and Older)
4-5 Other ADG Combinations, Age > 44, 2+
Major ADGs
2.810
0.812
4
4510
6-9 Other ADG Combinations, Age < 6, no Major
ADGs
0.972
N/A
3
4520
6-9 Other ADG Combinations, Age < 6, 1+ Major
ADGs
1.831
N/A
4
4610
6-9 Other ADG Combinations, Age 6 to 17, no
Major ADGs
0.948
N/A
3
4620
6-9 Other ADG Combinations, Age 6 to 17, 1+
Major ADGs
2.234
N/A
4
4710
6-9 Other ADG Combinations, Males, Age 18 to
34, no Major ADGs
0.965
N/A
3
4720
6-9 Other ADG Combinations, Males, Age 18 to
34, 1+ Major ADGs
1.802
N/A
3
4730
6-9 Other ADG Combinations, Males, Age 18 to
34, 2+ Major ADGs
3.648
N/A
4
4810
6-9 Other ADG Combinations, Females, Age 18
to 34, no Major ADGs
1.045
N/A
3
4820
6-9 Other ADG Combinations, Females, Age 18
to 34, 1+ Major ADGs
1.756
N/A
3
4830
6-9 Other ADG Combinations, Females, Age 18
to 34, 2+ Major ADGs
3.332
N/A
4
4910
6-9 Other ADG Combinations, Age > 34, 0-1
Major ADGs
1.816
0.598
3
4920
6-9 Other ADG Combinations, Age > 34, 2 Major
ADGs
3.616
1.088
4
4930
6-9 Other ADG Combinations, Age > 34, 3 Major
ADGs
6.451
1.776
5
4940
6-9 Other ADG Combinations, Age > 34, 4+
Major ADGs
12.218
3.015
5
5010
10+ Other ADG Combinations, Age 1 to 17, no
Major ADGs
1.806
N/A
3
5020
10+ Other ADG Combinations, Age 1 to 17, 1
Major ADGs
3.188
N/A
4
5030
10+ Other ADG Combinations, Age 1 to 17, 2
Major ADGs
12.171
N/A
5
5040
10+ Other ADG Combinations, Age > 17, 0-1
Major ADGs
2.790
0.889
4
ACG
Description
4430
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Chapter 2: Diagnosis-based Markers
Non-Elderly (0 to
64 Years)
Elderly (65 Years
and Older)
10+ Other ADG Combinations, Age > 17, 2
Major ADGs
4.572
1.422
4
5060
10+ Other ADG Combinations, Age > 17, 3
Major ADGs
7.536
2.213
5
5070
10+ Other ADG Combinations, Age > 17, 4+
Major ADGs
18.710
4.666
5
5110
No Diagnosis or Only Unclassified Diagnosis (2
input files)
0.129
0.204
1
5200
Non-Users (2 input files)
0.000
0.000
0
5310*
Infants: 0-5 ADGs, no Major ADGs
0.870
N/A
3
5311
Infants: 0-5 ADGs, no Major ADGs, low birth
weight
2.745
N/A
4
5312
Infants: 0-5 ADGs, no Major ADGs, normal birth
weight
0.846
N/A
3
5320*
Infants: 0-5 ADGs, 1+ Major ADGs
2.784
N/A
4
5321
Infants: 0-5 ADGs, 1+ Major ADGs, low birth
weight
10.955
N/A
5
5322
Infants: 0-5 ADGs, 1+ Major ADGs, normal birth
weight
1.943
N/A
4
5330*
Infants: 6+ ADGs, no Major ADGs
1.510
N/A
3
5331
Infants: 6+ ADGs, no Major ADGs, low birth
weight
3.999
N/A
4
5332
Infants: 6+ ADGs, no Major ADGs, normal birth
weight
1.436
N/A
3
5340*
Infants: 6+ ADGs, 1+ Major ADGs
10.538
N/A
5
5341
Infants: 6+ ADGs, 1+ Major ADGs, low birth
weight
31.997
N/A
5
5342
Infants: 6+ ADGs, 1+ Major ADGs, normal birth
weight
5.478
N/A
4
9900
Invalid Age or Date of Birth
0.000
0.000
0
ACG
Description
5050
RUB
Source: PharMetrics, Inc., a unit of IMS, Watertown, MA; national cross-section of managed care plans;
population of 3,310,540 commercially insured lives (less than 65 years old) and population of 501,987
Medicare beneficiaries (65 years and older), 2009-2011.
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.
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Chapter 2: Diagnosis-based Markers
Disease Markers - Expanded Diagnosis Clusters (EDCs)
The Johns Hopkins Expanded Diagnosis Clusters (EDCs) complement the unique person-oriented approach
that underpins the ACG System.
EDCs are a tool for easily identifying people with specific diseases or symptoms, without having to create
your own algorithms. The EDC methodology assigns diagnosis codes found in claims or encounter data to
one of 282 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.
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.
The Development of EDCs
EDCs build on the methodology developed by Ronald Schneeweiss and colleagues in 19833. In
Schneeweiss’ original work, diagnosis codes were classified according to clinical criteria. Schneeweiss’
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 Research4, another disease-oriented grouping methodology, to identify
additional conditions that were excluded from Schneeweiss’ original taxonomy. Other modifications made
to the original Schneeweiss clusters included deletion of some clusters, expansion of the content of other
clusters by 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.
For 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.
3 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.
4 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).
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Chapter 2: Diagnosis-based Markers
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 282 EDCs in the current version of the ACG System.
Understanding How EDCs Work
Each diagnosis code maps to one or more EDCs. Diagnosis codes within an EDC share similar clinical
characteristics and are expected to evoke similar types of diagnostic and therapeutic responses. Where a
single diagnosis code indicates more than one underlying condition, more than one EDC may be assigned.
For example, in ICD-10 the code E11.21 (Type 2 diabetes mellitus with diabetic nephropathy) would
trigger both END07 (Type 2 diabetes, w/ complication) and REN04 (Nephritis, nephrosis).
There are 65 ICD-9-CM and 41 ICD-10 codes (shown in the following tables) 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 your analyses related to this condition.
ICD-9-CM Codes Assigned to Otitis Media EDC (EAR01)
ICD-9-CM Codes
Description
055.2
Postmeasles otitis media
381
Nonsuppurative otitis media and Eustachian tube disorders
381.0
Acute nonsuppurative otitis media
381.00
Unspecified acute nonsuppurative 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 nonsuppurative otitis media
381.4
Nonsuppurative otitis media, not specified as acute or chronic
381.5
Eustachian salpingitis
381.50
Unspecified Eustachian salpingitis
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ICD-9-CM Codes
Description
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
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
382.00
Acute suppurative otitis media without spontaneous rupture of eardrum
382.01
Acute suppurative otitis media with spontaneous rupture of eardrum
382.02
Acute suppurative otitis media in diseases classified 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
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Chapter 2: Diagnosis-based Markers
ICD-9-CM Codes
Description
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 nonflaccid 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
ICD-10 Codes Assigned to Otitis Media EDC (EAR01)
ICD-10 Codes
Description
B05.3
Postmeasles otitis media
H65
Nonsuppurative otitis media and Eustachian tube disorders
H65.0
Acute serous otitis media
H65.1
Other acute nonsuppurative otitis media
H65.2
Chronic serous otitis media
H65.3
Chronic mucoid otitis media
H65.4
Other and unspecified chronic nonsuppurative otitis media
H65.9
Nonsuppurative 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
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ICD-10 Codes
Description
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
The EDCs related to Diabetes receive 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 the Examples of Diabetes Complications table),
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a patient with END06 will be assigned to END07 while a patient with END08 will be assigned to END09.
The implication of this method is that a patient cannot be assigned to a diabetes condition with and
without complications simultaneously.
Examples of Diabetes Complications
Patients will not be assigned to a Type 1 Diabetes EDC and a Type 2 Diabetes EDC concurrently. If the
patient has diagnoses indicating both Type 1 Diabetes and Type 2 Diabetes, the EDC assignment will
reflect Type 1 Diabetes only.
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
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ICD-10
Description
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
N19
Unspecified renal failure
N28
Other disorders of kidney
Z49
Care involving dialysis
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 user applies the lenient diagnostic certainty option, any single diagnosis qualifying for an EDC
marker will turn the marker on. However, the user may also apply a stringent diagnostic certainty option.
For a subset of chronic 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, but 2
diagnoses for Type 2 Diabetes will turn on EDC END06. For more information about the use of the
diagnostic certainty options, refer to Chapter 4 of the Installation and Usage Guide.
MEDC Types
To assist analysts, the 27 Major EDCs may be further aggregated into five MEDC Types. Specifically, the
categorization is presented in the following table.
MEDC Type
MEDCs
1. Administrative
Administrative
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MEDC Type
MEDCs
2. Medical
Allergy, Cardiovascular, Endocrine, Gastrointestinal/Hepatic, General Signs and
Symptoms, Genetic, Hematologic, Infections, Malignancies, Neonatal,
Neurologic, Nutrition, Renal, Respiratory, Rheumatologic, Skin, Toxic Effects
3. Surgical
Dental, ENT, Eye, General Surgery, Genito-urinary, Musculoskeletal,
Reconstructive
4. Obstetric/Gynecologic
Female reproductive
5. Psychosocial
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 286 groups.
High, Moderate, and Low Impact EDCs
Within the detailed EDC output from the system as well as in the Comprehensive Patient Clinical Profile
Report, the EDCs 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 diagnosis derived predictive model without prior cost predicting
total cost
• Moderate Impact EDC: Coefficient between 0.1 and 1.0 in the diagnosis derived predictive model
without prior cost predicting total cost
• Low Impact EDC: Coefficient less than 0.1 or not included in the diagnosis derived predictive model
without prior cost predicting total cost
Important Differences between EDCs and ADGs
Both EDCs and Aggregated Diagnosis Groups (ADGs) are aggregations of diagnosis codes. However, there
is a significant difference in the methodology underlying the grouping of diagnosis codes: ADGs are groups
of diagnoses with similar expected healthcare need, while EDCs are clinically similar clusters. The main
criterion used for the diagnosis-to-EDC assignment is diagnostic similarity, whereas the diagnosis-to-ADG
assignments are based on a unique set of clinical criteria that captures various dimensions of the range
and severity of an individual’s co-morbidity. This subject is discussed in more detail in Patterns of
Morbidity – Adjusted Clinical Groups (ACGs) on page 17.
In brief, the key dimensions of the diagnosis 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, diagnosis 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.
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Because of the marked differences between the conceptual underpinnings of EDCs and the ADG
framework, each ADG will usually contain diagnosis codes that map into more than one EDC. Likewise,
diagnosis codes contained within a single EDC may map into multiple ADGs.
Applications of EDCs
EDCs have many applications, particularly in areas of profiling and disease/case management. EDCs can be
used to:
1. Describe the prevalence of specific diseases within a single population;
2. Compare disease distributions across two or more populations; and,
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 Applications Guide chapters for Population Health
Monitoring and Clinical Screening.
Special Population Markers
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 ACG risk models.
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 the
following table). 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 the following table). The number of unique EDC flags found
for a patient represents the final chronic condition count.
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EDCs considered in the Chronic Condition Count Marker
EDC Description
EDC Flag
Acute hepatitis
GAS04
Acute leukemia
MAL16
Acute lower respiratory tract infection
RES02
Acute myocardial infarction
CAR12
Acute renal failure
REN03
Acute sprains and strains
MUS02
Adjustment disorder
PSY13
Administrative concerns and non-specific laboratory abnormalities
ADM05
Adverse events from medical/surgical procedures
TOX03
Age-related macular degeneration
EYE15
Anxiety, neuroses
PSY01
Aplastic anemia
HEM05
Arthropathy
RHU03
Asthma, w/o status asthmaticus
ASTH
Asthma, with status asthmaticus
ASTH
Attention deficit disorder
PSY05
Autism Spectrum Disorder
NUR26
Autoimmune and connective tissue diseases
RHU01
Benign and unspecified neoplasm
GSU03
Bipolar disorder
PSY12
Blindness
EYE02
Cardiac arrhythmia
CAR09
Cardiac valve disorders
CAR06
Cardiomyopathy
CAR07
Cardiovascular disorders, other
CAR16
Cardiovascular signs and symptoms
CAR01
Cataract, aphakia
EYE06
Central nervous system infections
NUR20
Cerebral palsy
NUR18
Cerebrovascular disease
NUR05
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EDC Description
EDC Flag
Chromosomal anomalies
GTC01
Chronic cystic disease of the breast
GSU06
Chronic liver disease
GAS05
Chronic pancreatitis
GAS12
Chronic renal failure
REN01
Chronic respiratory failure
RES13
Chronic ulcer of the skin
REC03
Cleft lip and palate
REC01
Congenital anomalies of limbs, hands, and feet
MUS11
Congenital heart disease
CAR04
Congestive heart failure
CAR05
Cystic fibrosis
RES03
Deafness, hearing loss
EAR08
Deep vein thrombosis
HEM06
Degenerative joint disease
MUS03
Dementia
NUR24
Delirium
NUR25
Depression
PSY09
Developmental disorder
NUR19
Diabetic retinopathy
EYE13
Disorders of lipid metabolism
CAR11
Disorders of Newborn Period
NEW05
Disorders of the immune system
ALL06
Eating disorder
PSY15
Emphysema, chronic bronchitis, COPD
RES04
Endometriosis
FRE03
ESRD
REN06
Eye, other disorders
EYE14
Failure to thrive
NUT01
Fluid/electrolyte disturbances
REN02
Gastrointestinal signs and symptoms
GAS01
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EDC Description
EDC Flag
Gastrointestinal/Hepatic disorders, other
GAS14
Generalized atherosclerosis
CAR10
Genito-urinary disorders, other
GUR12
Glaucoma
EYE08
Gout
RHU02
Hematologic disorders, other
HEM08
Hemophilia, coagulation disorder
HEM07
High impact malignant neoplasms
MAL03
HIV, AIDS
INF04
Hypertension, w/o major complications
HYPT
Hypertension, with major complications
HYPT
Hypothyroidism
END04
Impulse control
PSY16
Inflammatory bowel disease
GAS02
Inherited metabolic disorders
GTC02
Irritable bowel syndrome
GAS09
Ischemic heart disease (excluding acute myocardial infarction)
CAR03
Kyphoscoliosis
MUS06
Lactose intolerance
GAS13
Low back pain
MUS14
Low impact malignant neoplasms
MAL02
Malignant neoplasms of the skin
MAL01
Malignant neoplasms, bladder
MAL18
Malignant neoplasms, breast
MAL04
Malignant neoplasms, cervix, uterus
MAL05
Malignant neoplasms, colorectal
MAL12
Malignant neoplasms, esophagus
MAL07
Malignant neoplasms, kidney
MAL08
Malignant neoplasms, liver and biliary tract
MAL09
Malignant neoplasms, lung
MAL10
Malignant neoplasms, lymphomas
MAL11
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EDC Description
EDC Flag
Malignant neoplasms, ovary
MAL06
Malignant neoplasms, pancreas
MAL13
Malignant neoplasms, prostate
MAL14
Malignant neoplasms, stomach
MAL15
Migraines
NUR22
Multiple sclerosis
NUR08
Muscular dystrophy
NUR09
Musculoskeletal disorders, other
MUS17
Nephritis, nephrosis
REN04
Neurologic disorders, other
NUR21
Neurologic signs and symptoms
NUR01
Newborn Status, Complicated
NEW02
Obesity
NUT03
Organic brain syndrome
NUR23
Osteoporosis
END02
Other endocrine disorders
END05
Other hemolytic anemias
HEM01
Other skin disorders
SKN17
Paralytic syndromes, other
NUR17
Parkinson's disease
NUR06
Peripheral neuropathy, neuritis
NUR03
Peripheral vascular disease
GSU11
Personality disorders
PSY08
Prostatic hypertrophy
GUR04
Psychological disorders of childhood
PSY14
Psychosexual
PSY18
Psych-physiologic and somatoform disorders
PSY17
Pulmonary embolism
RES08
Quadriplegia and paraplegia
NUR12
Renal disorders, other
REN05
Respiratory disorders, other
RES11
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EDC Description
EDC Flag
Retinal disorders (excluding diabetic retinopathy)
EYE03
Rheumatoid arthritis
RHU05
Schizophrenia and affective psychosis
PSY07
Seizure disorder
NUR07
Short stature
END03
Sleep apnea
RES06
Sickle cell disease
HEM09
Skin keratoses
SKN10
Spinal cord injury/disorders
NUR16
Strabismus, amblyopia
EYE11
Substance use
PSY02
Thrombophlebitis
HEM03
Tracheostomy
RES09
Transplant status
ADM03
Type 1 diabetes
DIAB
Type 2 diabetes
DIAB
Vesicoureteral reflux
GUR01
Distribution of the Chronic Condition Count Marker
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. The following
table provides a representative distribution of the chronic condition count for elderly and non-elderly
populations.
Chronic Condition Count
Non-Elderly (%)
Elderly (%)
0
52.3
17.6
1
22.3
8.9
2
11.3
12.3
3
6.4
14.3
4
3.6
13.1
5
1.9
10.3
6
1.0
7.5
7
0.5
5.4
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Chronic Condition Count
Non-Elderly (%)
Elderly (%)
8
0.3
3.7
9
0.2
2.5
10+
0.2
4.4
Source: PharMetrics, Inc., a unit of IMS, Watertown, MA; national cross-section of managed care plans;
population of 3,310,540 commercially insured lives (less than 65 years old) and population of 501,987
Medicare beneficiaries (65 years and older), 2009-2011.
Hospital Dominant Morbidity Types
Hospital dominant morbidity types are based on diagnoses that, when present, are associated with a
markedly greater probability of hospitalization among affected patients 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. The following table provides examples of diagnostic codes that have been identified as hospital
dominant.
Examples of Diagnostic Codes Included in the Hospital Dominant Morbidity Types
ICD-9-CM Code
162.3
261
2638
ICD-10 Code Description
C34.1 Malignant Neoplasm, Upper Lobe, Bronchus or Lung
E41 Nutritional Marasmus
E43 Other Protein Calorie Malnutrition Nec
284.8
D61.9 Other specified Aplastic Anemia
2894
D73.1 Hypersplenism
29181
F10.3 Alcohol Withdrawal
29643
F31.6 Bipolar affective disorder, mixed episode
0380
A40 Streptococcal Septicemia
4150
I26.0 Acute cor pulmonale
4821
J15.1 Pseudomonal Pneumonia
491.21
518.81
J44.1 Obstructive Chronic Bronchitis with acute exacerbation
J96 Acute Respiratory Failure
5722
K72.9 Hepatic coma
584.9
N17.9 Acute Renal Failure, unspecified
785.4
A48.0 Gangrene
7895
R18 Ascites
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The risk of hospitalization, total healthcare costs, and medication costs rise dramatically in association with
the number of hospital dominant morbidity types (see the following table). 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 10fold increase for individuals in the group with two or more hospital dominant morbidity types.
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
Next Year’s Outcomes
Baseline Year Risk Factors
% 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
3+
2.5
13.2
29.8
46.4
$3,401
$17,634
$40,457
$79,483
$651
$4,127
$5,316
$6,459
Medicare Beneficiaries (65 years and older)
Number of hospital
dominant morbidity types
None
1
2
3+
11.4
27.9
40.0
52.1
$10,152
$25,901
$42,964
$67,102
$1,812
$3,968
$4,714
$5,186
Source: PharMetrics, Inc., a unit of IMS, Watertown, MA; national cross-section of managed care plans;
population of 3,310,540 commercially insured lives (less than 65 years old) and population of 501,987
Medicare beneficiaries (65 years and older), 2009-2011.
Frailty Conditions
The Frailty Flag is a dichotomous (on/off) variable that indicates whether an enrollee over the age of 18
has a diagnosis falling within any one of 10 clusters that represent medical problems associated with
frailty. Further, assignments associated with each frailty concept are also available in the output. 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 the following
table. Presence of any one of these diagnoses turns on the FRAILTY indicator (yes/no) variable. Among
commercially insured populations, less than one percent of individuals have at least one of these frailty
diagnoses, whereas among Medicare beneficiaries the proportion is greater than seven percent.
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Medically Frail Condition Marker – Frailty Concepts and Diagnoses
Frailty Concept
Diagnoses (Examples)
Malnutrition and/or Catabolic
Illness (MAL)
Nutritional Marasmus
Other severe protein-calorie malnutrition
Dementia (DEM)
Senile dementia with delusional or depressive features
Senile dementia with delirium
Severe Vision Impairment (VIS) Profound impairment, both eyes
Moderate or severe impairment, better eye/lesser eye: profound
Decubitus Ulcer (DEC)
Decubitus Ulcer
Major Problems of Urine
Retention or Control (URC)
Incontinence without sensory awareness
Continuous leakage
Loss of Weight (WEI)
Abnormal loss of weight and underweight
Feeding difficulties and mismanagement
Absence of Fecal Control (AFC) Incontinence of feces
Social Support Needs (SSN)
Lack Of Housing
Inadequate Housing
Inadequate material resources
Difficulty in Walking (WLK)
Difficulty in walking
Abnormality of gait
Fall (FAL)
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 the following table).
Effects of Frailty Conditions During a Baseline Year on Next Year’s Hospitalization Risk, Total
Healthcare Costs, and Pharmacy Costs
FRAILTY CONDITIONS
Next Year’s Outcomes
Baseline Year Risk Factors
% with 1+ hospitalization
Mean total healthcare
costs
Mean pharmacy costs
Medicare Beneficiaries (65 years and older)
Number of frailty
conditions:
None
1+
11.9
26.0
$11,054
$21,260
Commercially insured lives (less than 65 years old)
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FRAILTY CONDITIONS
Next Year’s Outcomes
Baseline Year Risk Factors
% with 1+ hospitalization
Mean total healthcare
costs
Mean pharmacy costs
2.7
12.3
$3,724
$16,200
$717
$2,812
Number of frailty
conditions:
None
1+
Source: PharMetrics, Inc., a unit of IMS, Watertown, MA; national cross-section of managed care plans;
population of 3,310,540 commercially insured lives (less than 65 years old) and population of 501,987
Medicare beneficiaries (65 years and older), 2009-2011.
Compassionate Care Allowances (CAL-SSA)
The intent of the Compassionate Care Allowances is to identify persons who have an increased risk of
hospitalization. This marker complements other ACG System markers that identify vulnerable groups such
as Frailty and Hospital Dominant Morbidity Types.
The Compassionate Care Allowances marker was created by the U.S. Social Security Administration to
identify types of impairment that invariably meet disability standards5 and are, thereby, sufficient to allow
an individual to become immediately eligible for benefits. The conditions that qualify for Compassionate
Allowances include many cancers, amyotrophic lateral sclerosis (ALS), some types of muscular dystrophy
and muscular atrophy, early-onset Alzheimer's disease, and a few other illnesses. The presence of an
eligible diagnostic code triggers this dichotomous (on/off) marker.
Pregnant
Pregnancy status is a key differentiator for the ACG decision tree. The ACG Software will use the
diagnoses provided in the medical services file to identify pregnancy as follows:
• ICD-9-CM: 640.xx-677.xx, V22.xx, V23.xx, V24.xx, V27.xx, V28.xx, V91.xx
• ICD-10: O00.x-O99.x, Z33.x-Z37.x, Z39.x
It is possible for analysts to provide the software with a flag indicating that a woman is pregnant. The
rationale for including this option is that, in some plans, it is not uncommon for the charges associated
with a woman’s pregnancy and subsequent delivery to be reimbursed as a global or fixed payment at the
time of delivery. In this reimbursement scenario, a woman’s claims history may not include a pregnancy
diagnosis until she actually delivers. However, given the importance of this information, the plan often
does know that the woman is pregnant, despite this lack of related diagnosis codes during the prenatal
care period. In cases where the plan wishes to supplement the standard claims data (i.e., if a pregnancy
registry is believed to be more accurate than standard claims data), the user may submit a special
pregnancy flag that can supplement the standard diagnosis stream. Refer to Chapter 3 of the Installation
and Usage Guide for a discussion on implementing this approach.
Delivered
Delivery status is an important branch for pregnancy in the ACG Decision tree.
5 http://www.ssa.gov/compassionateallowances/
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Each ACG from 1710 through 1770 is split into two categories (1711, 1712 through 1771, 1772) based on
whether or not the women within these categories have delivered during the period of analysis. After
extensive testing, the ACG System development team at Johns Hopkins is confident the standard diagnosis
codes used by the software for identifying deliveries are effective with positive predictive accuracy (that is,
the women did actually deliver) averaging greater than 96% among all plans tested. However, for a
variety of reasons diagnosis codes for delivery may not appear in a woman’s claim history even though
she did in fact deliver. For example, the delivery may have occurred in an outpatient birthing center or
other non-traditional venue, and claims were never submitted containing any delivery codes. Also, if an
analyst is using only ambulatory data (not generally recommended) where the delivery diagnosis codes are
not available, it is suggested that the user provide a delivered flag in the input data stream. Refer to
Chapter 3 of the Installation and Usage Guide for a discussion on implementing this approach.
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
the ACG assignment 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.
Low Birth Weight (Less than 2500 Grams)
Because low birth weight is a significant predictor of resource use, diagnosis can be used to identify low
birth weight newborns when available. The ACG Software uses the following list of codes to identify low
birthweight newborns:
• ICD-9-CM: 764.0*; 764.1*; 764.2*; 764.9*; 765.0*; 765.1, V21.3* (where * = 1-8)
• ICD-10: P05.0, P05.1, P07.0, P07.1
In a manner similar to the way pregnant women are subdivided by delivery status, infants can be
subdivided into subcategories based on their birth weight. Diagnosis codes allow for identification of low
or normal birth weights among neonates. Historically, validation analysis across a variety of health care
organizations indicated that within most plans 2% to 5% of infants were identified as low birth weight.
Based on vital records and other sources, the actual percentage should be somewhere between 6% and
9%. If diagnoses do not seem to be a reliable source of the recording of birth weight, analysts may wish
to take advantage of this feature to appropriately categorize low birth weight infants. Analysts can flag
such infants before passing the data to the ACG Software. Refer to Chapter 3 of the Installation and
Usage Guide for a discussion on implementing this approach.
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Chapter 3: Pharmacy-based Markers
Chapter 3:
Pharmacy-based Markers
This chapter is intended to describe the pharmacy based morbidity markers, Rx-MGs, and the clinical
criteria used to assign medications to morbidity groups. The Rx-MGs provide a medication-based approach
for describing the morbidity profile of an individual or population. Rx-MGs also form the basis of the
pharmacy based predictive models. Specifics of the predictive models are discussed in greater detail in
later chapters discussing Risk Modeling and Predictive Models for Hospitalization.
Rx-MGs are groupings of medication codes. The system is compatible with NDC or ATC codes and may be
further augmented with HCPCS6 codes. Please contact the account representative to understand other
localizations available.
Rx-Defined Morbidity Groups (Rx-MGs)
Rx-Defined Morbidity Groups (Rx-MGs) are a medication classification system that was developed
according to the following principles:
1. Rx-MGs should be both comprehensive (includes all medications) and parsimonious (groups all
medications into a manageable set of categories).
2. Rx-MGs should be useful for clinical and population management applications.
3. Rx-MGs should be adaptable for domestic and international uses, and should accommodate domestic
and international medication codes (NDC and ATC, respectively).
4. Rx-MG assignment methodology should use a medication’s mechanism of action, clinical indications,
and route of administration information to make classification decisions. These principles were used to
develop 67 Rx-MG categories. Each unique active ingredient/route of administration combination is
assigned to a single Rx-MG. By extracting the key morbidity-related information from each active
ingredient/route of administration combination, drugs within conventional therapeutic classes were
sometimes logically assigned to different Rx-MGs. This observation reinforced the need to make
assignments at the individual drug level, rather than at the level of a therapeutic class.
Clinical Basis of Rx-MGs
Medications can be classified according to their intended therapeutic use (i.e., their clinical indications) or
their mechanisms of action. The mechanism of action of a medication refers to the molecular interactions
that lead to its effect on health. Molecular interactions may include such actions as inhibition of enzyme
activity, binding to a receptor site, or direct effects on the body.
Rx-MGs use both the intended therapeutic uses, including off-label usage, and mechanism of action to
categorize medications along four clinical dimensions of medication use:
• Primary organ system affected by the medication.
• Morbidity differentiation, which refers to whether the medication is used to relieve symptoms or treat
a specific disease.
• Expected duration of targeted diseases as time-limited or chronic.
6 Healthcare Common Procedure Coding System, maintained by the Centers for Medicare and Medicaid Services.
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Chapter 3: Pharmacy-based Markers
• Severity of illness, which refers to the long-term stability of the targeted diseases.
These four clinical dimensions of medication use are also relevant for predictive modeling. For example,
higher levels of differentiation, chronicity, and greater severity would all be expected to require higher
levels of resource use. Each of these criteria is discussed in the following sections.
Primary Organ System Affected
By combining information on a medication’s clinical indications and mechanism of action, we can identify
the primary organ system that it influences. Medications generally are most useful for treatment of
conditions related to a particular organ system. However, this is not a universal attribute of medications,
as some can be used for treatment of conditions that affect one or more organ systems. In this case, we
determined the most common, or primary, organ system that is targeted by the medication. Using this
approach, we derived 19 categories, which categorized all unique medication/route of administration
combinations: 16 represent organ systems, and the other three signs and symptoms, toxic effects/adverse
events group, and non-specific categories. These groupings are called Major Rx-MGs.
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.
Morbidity Differentiation
Another dimension of medication usage is the differentiation of the health conditions that the drug is
intended to treat. Three general categories of health condition differentiation are possible:
1. symptoms, which are poorly differentiated because a given symptom like pain can occur as a result of
many health conditions;
2. a general class of health conditions within an organ system, an intermediate level of differentiation; or
3. a highly differentiated indication with a single health condition target.
The Rx-MGs therefore 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 anticipated period of time that a medication will be used to manage a health
condition(s):
• time-limited for acute conditions expected to last a few days to a few weeks.
• repeated episodes of treatment for recurrent conditions.
• long-term treatment for chronic conditions, which typically last several months to years.
Severity
This dimension refers to the expected impact of the health condition(s) on the physiological stability of the
patient or the patient’s functional status without the use of appropriate medication therapy. Acute
conditions can be separated into low and high impact, the latter has significant effects on the ability of an
individual to perform daily activities. For chronic conditions, morbidity severity is related to the stability of
the problem over long periods of time. Without adequate treatment, unstable chronic conditions are
expected to progressively worsen, whereas stable conditions are expected to change less rapidly.
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Chapter 3: Pharmacy-based Markers
Clinical Characteristics of Medications Assigned to Each Rx-MG
The final set of 67 Rx-MG categories, along with their distinguishing clinical characteristics, is shown in the
following table.
Severity
Rx-Defined
Morbidity Group
Chronic and
Recurrent
Conditions: Stability
Differentiation
Duration
Acute Conditions:
Impact
Acute Minor
Symptoms/General
Acute
Low Impact
Chronic
Inflammatory
General
Chronic
Unstable
Immune Disorders
General
Chronic
Unstable
Transplant
General
Chronic
Unstable
Chronic Medical
General
Chronic
Stable
Congestive Heart
Failure
Disease
Chronic
Unstable
High Blood Pressure
Disease
Chronic
Stable
Disorders of Lipid
Metabolism
Disease
Chronic
Stable
Vascular Disorders
General
Chronic
Stable
Symptoms/General
Acute
Bone Disorders
General
Chronic
Stable
Chronic Medical
General
Chronic
Stable
Diabetes With Insulin
Disease
Chronic
Unstable
Diabetes Without
Insulin
Disease
Chronic
Stable
Thyroid Disorders
General
Chronic
Stable
Growth Problems
General
Chronic
Stable
Weight Control
General
Chronic
Stable
Allergy/Immunology
Cardiovascular
Ears-Nose-Throat
Acute Minor
Low Impact
Endocrine
Eye
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Chapter 3: Pharmacy-based Markers
Severity
Rx-Defined
Morbidity Group
Chronic and
Recurrent
Conditions: Stability
Differentiation
Duration
Acute Conditions:
Impact
Acute Minor:
Curative
General
Acute
Low Impact
Acute Minor:
Palliative
Symptoms
Acute
Low Impact
Disease
Chronic
Stable
Hormone Regulation
General
Chronic
Stable
Infertility
General
Acute
High Impact
Pregnancy and
Delivery
General
Acute
High Impact
Symptoms/General
Acute
Low Impact
Chronic Liver Disease
General
Chronic
Unstable
Chronic Stable
General
Chronic
Stable
Inflammatory Bowel
Disease
Disease
Chronic
Stable
Pancreatic Disorder
General
Chronic
Unstable
Peptic Disease
Disease
Recurrent
Stable
Nausea and
Vomiting
Symptoms
Acute
Low Impact
Pain
Symptoms
Acute
Low Impact
Pain and
Inflammation
Symptoms
Acute
Low Impact
Severe Pain
Symptoms
Acute
High Impact
Symptoms/General
Acute
Low Impact
Disease
Chronic
Unstable
General
Chronic
Unstable
Glaucoma
Female Reproductive
Gastrointestinal/Hepatic
Acute Minor
General Signs and Symptoms
Genito-Urinary
Acute Minor
Chronic Renal Failure
Hematologic
Coagulation
Disorders
Infections
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Chapter 3: Pharmacy-based Markers
Severity
Rx-Defined
Morbidity Group
Chronic and
Recurrent
Conditions: Stability
Differentiation
Duration
Acute Conditions:
Impact
Acute Major
General
Acute
High Impact
Acute Minor
General
Acute
Low Impact
HIV/AIDS
Disease
Chronic
Tuberculosis
Disease
Acute
Low Impact
Severe Acute Major
General
Acute
High Impact
General
Chronic
Unstable
Gout
Disease
Recurrent
Stable
Inflammatory
Conditions
General
Chronic
Unstable
Alzheimers Disease
Disease
Chronic
Unstable
Chronic Medical
General
Chronic
Stable
Migraine Headache
Disease
Recurrent
Stable
Parkinsons Disease
Disease
Chronic
Unstable
Seizure Disorder
General
Chronic
Stable
Attention Deficit
Disorder
Disease
Chronic
Stable
Addiction
General
Chronic
Unstable
Symptoms/General
Recurrent
Stable
Depression
Disease
Chronic
Stable
Acute Minor
Symptoms/General
Acute
General
Chronic
Sleep disorders
Symptoms/General
Acute and Recurrent
Tobacco Cessation
Symptoms/General
Acute
Disease
Chronic
Symptoms/General
Acute
Unstable
Malignancies
Malignancies
Musculoskeletal
Neurologic
Psychosocial
Anxiety
Chronic Unstable
Bipolar disorder
Low Impact
Unstable
Unstable
Respiratory
Acute Minor
Low Impact
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Chapter 3: Pharmacy-based Markers
Severity
Rx-Defined
Morbidity Group
Acute Conditions:
Impact
Chronic and
Recurrent
Conditions: Stability
Differentiation
Duration
Airway Hyperreactivity
Disease
Chronic
Stable
Chronic Medical
General
Chronic
Stable
Cystic Fibrosis
Disease
Chronic
Unstable
Disease
Chronic
Stable
Symptoms/General
Acute and Recurrent
General
Chronic
General
Acute
High Impact
N/A
N/A
N/A
Skin
Acne
Acute and Recurrent
Chronic Medical
Low Impact
Stable
Toxic Effects/Adverse Effects
Acute Major
Other and NonSpecific Medications
N/A
Rx-MG Medication Assignment Methodology
The Rx-MG assignment methodology evaluates each unique active ingredient / route of administration
combination along with the four clinical dimensions of medication use described above. This review
encompasses U.S. Food and Drug Administration (FDA)-approved and common off-label uses of a drug.
Each unique active ingredient/route of administration combination was assigned to a single Rx-MG
according to this clinical review.
The ACG team recognized that medications may be used differently, particularly for off label indications, in
international settings. The biomedical literature was extensively reviewed for evidence of alternate and
prevalent international use of medications.
A team of Johns Hopkins physicians and pharmacist health professionals made the initial Rx-MG
assignments. Disagreements were reconciled during consensus meetings. The face validity of the
assignments was tested by having a second team of physicians and pharmacist health professionals
examine the preliminary assignments. This second review led to some modifications, but generally
confirmed the validity of the initial assignments.
Assignment with NDC codes involved collapsing the 100,000 codes to 2,700 unique active
ingredient/route of administration combinations which then were assigned to an Rx-MG. Assignment of
ATC codes involved assigning close to 5,100 ATC codes (900 4th Level and nearly 4,200 5th Level ATC
codes) to an Rx-MG.
Most NDC codes representing drugs are assigned to a single Rx-MG, and just 9.8% are mapped to the
Non-Specific category. NDC codes representing medical supplies, exclusively over-the counter medications,
and other non-pharmaceutical items are not assigned to a Rx-MG. A single NDC may be mapped to two
Rx-MGs if the drug is a combination therapy. For example, amlodipine besylate/atorvastatin calcium is a
combined calcium channel blocker and statin therapy to treat patients with hypertension and disorders of
lipid metabolism. This single oral tablet will be assigned to both CARx030 (high blood pressure) and
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Chapter 3: Pharmacy-based Markers
CARx040 (disorders of lipid metabolism). Drugs that combine over the counter medications with
prescription medications will only be assigned to the Rx-MG for the prescription component (e.g.,
codeine/pseudoephedrine). Combination therapies that are treating a single underlying condition are
assigned to only one Rx-MG (e.g., HAART made up of emtricitabine/nelfinavir/tenofovir for treating HIV).
If HCPCS codes are available in the Medical Services input file, the Rx-MG assignments will also consider
medications administered (typically as injections) in the physician's office.
The ACG team updates the assignment of new NDC codes to Rx-MG regularly.
High, Moderate, and Low Impact Rx-MGs
Within the detailed Rx-MG output from the system as well as in the Comprehensive Patient Clinical Profile
Report, the 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 Rx-MG: Coefficient >1 in the pharmacy derived predictive model without prior cost
predicting total cost
• Moderate Impact Rx-MG: Coefficient between 0.1 and 1.0 in the pharmacy derived predictive model
without prior cost predicting total cost
• Low Impact Rx-MG: Coefficient less than 0.1 in the pharmacy derived predictive model without prior
cost predicting total cost
Active Ingredient Count
An active ingredient count is calculated as the count of unique active ingredient/route of administration
combinations encountered in the patient’s drug claims. If HCPCS codes are available in the Medical
Services input file, the active ingredient count will consider medications administered (typically as
injections) in the physician's office in addition to drugs identified in the pharmacy file. 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 pharmacy-based predictive models. Members with
an active ingredient count of 14 or greater get additional weight in the predictive models.
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Chapter 4: Diagnosis+Pharmacy-based Markers
Chapter 4:
Diagnosis+Pharmacy-based Markers
The ACG System outputs a number of markers derived from a combination of the patient's diagnosis code
history and medication fills from all encounters during a 12-month observation period. This chapter
provides definitions for the ACG System markers derived from diagnosis and pharmacy information.
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 this chapter.
The specific criteria used to define each condition marker are listed in the following table. 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 the following table)
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 the following table. 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 17 of the conditions, a separate column indicates if the condition is untreated. The specific criteria
for an untreated condition are listed in the following table. 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.
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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.
• Blank—There is insufficient evidence to confirm the presence of the condition.
Definitions of Condition Markers
Condition
Diagnostic Criteria
Pharmacy Criteria
Treatment Criteria
Untreated Criteria
Bipolar disorder
PSY12
N/A
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
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
Congestive heart
failure
CAR05
CARx020
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:
ACEI/ARB
Aldosterone receptor
blockers
Beta-blockers
Diuretics
Inotropic agents
Vasodilators
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
Aldosterone receptor
blockers
Beta-blockers
Diuretics
Inotropic agents
Vasodilators
Depression
PSY09, PSY20
PSYx040
1 inpatient or ER
diagnosis or 2
outpatient diagnoses
plus 2 prescription
fills (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
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Chapter 4: Diagnosis+Pharmacy-based Markers
Condition
Diagnostic Criteria
Pharmacy Criteria
Treatment Criteria
Untreated Criteria
Diabetes
END06, END07,
END08, END09
Meglitinides
Non-sulfonylureas
Sulfonylureas
Thiazolidinediones
Other
antihyperglycemic
agents
Long and shortacting Insulins
2 prescription fills
(within 120 days of
each other) in at
least one of the drug
classes:
Meglitinides
Non-sulfonylureas
Sulfonylureas
Thiazolidinediones
Other
antihyperglycemic
agents
Long and shortacting Insulins
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:
Meglitinides
Non-sulfonylureas
Sulfonylureas
Thiazolidinediones
Other
antihyperglycemic
agents
Long and shortacting Insulins
Disorders of Lipid
Metabolism
CAR11
Bile acid
sequestrants
Cholesterol
absorption inhibitors
Fibric acid
derivatives
HMG-CoA reductase
inhibitors
Miscellaneous
antihyperlipidemic
agents
2 prescription fills
(within 120 days of
each other) in at
least one of the drug
classes:
Bile acid
sequestrants
Cholesterol
absorption inhibitors
Fibric acid
derivatives
HMG-CoA reductase
inhibitors
Miscellaneous
antihyperlipidemic
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:
Bile acid
sequestrants
Cholesterol
absorption inhibitors
Fibric acid
derivatives
HMG-CoA reductase
inhibitors
Miscellaneous
antihyperlipidemic
agents
Glaucoma
EYE08
EYEx030
1 inpatient or ER
diagnosis or 2
outpatient diagnoses
plus 2 prescription
fills (within 120 days
of each other) in the
drug class:
Ophthalmic
glaucoma agents
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:
Ophthalmic
glaucoma agents
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Chapter 4: Diagnosis+Pharmacy-based Markers
Condition
Diagnostic Criteria
Pharmacy Criteria
Treatment Criteria
Untreated Criteria
Human
Immunodeficiency
Virus
INF04
HAART*
2 prescription fills
(within 120 days of
each other) in the
drug class:
HAART*
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:
HAART*
Hypertension
CAR14, CAR15
CARx030
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:
ACEI/ARB
Aldosterone receptor
blockers
Anti-adrenergic
agents
Beta-blockers
Calcium channel
blockers
Diuretics
Vasodilators
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
Aldosterone receptor
blockers
Anti-adrenergic
agents
Beta-blockers
Calcium channel
blockers
Diuretics
Vasodilators
Hypothyroidism
END04
Thyroid replacement
drugs
2 prescription fills
(within 120 days of
each other) in the
drug class:
Thyroid drugs
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:
Thyroid drugs
Immunosuppression
/Transplant
ADM03
ALLx050
1 inpatient or ER
diagnosis or 2
outpatient diagnoses
plus 2 prescription
fills (within 120 days
of each other) in the
drug class:
Immunologic agents
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:
Immunologic agents
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Chapter 4: Diagnosis+Pharmacy-based Markers
Condition
Diagnostic Criteria
Pharmacy Criteria
Treatment Criteria
Untreated Criteria
Ischemic heart
disease
CAR03
N/A
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:
Antianginal agents
Beta-blockers
Calcium channel
blockers
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:
Antianginal agents
Beta-blockers
Calcium channel
blockers
Osteoporosis
END02
ENDx010
1 inpatient or ER
diagnosis or 2
outpatient diagnoses
plus 2 prescription
fills (within 120 days
of each other) in the
drug class:
Osteoporosis
Hormones
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:
Osteoporosis
Hormones
Parkinson’s disease
NUR06
Anticholinergic
antiparkinson agents
Dopaminergic
antiparkinson agents
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:
Anticholinergic
antiparkinson agents
Dopaminergic
antiparkinson 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:
Anticholinergic
antiparkinson agents
Dopaminergic
antiparkinsonism
agents
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Condition
Diagnostic Criteria
Pharmacy Criteria
Treatment Criteria
Untreated Criteria
Persistent asthma
ALL04, ALL05
RESx040
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
(within 120 days of
each other) in one of
the drug classes
other than
Leukotriene
modifiers:
Long and shortacting Adrenergic
bronchodilators
Immunosuppressive
monoclonal
antibodies
Inhaled
corticosteroids
Leukotriene
modifiers
Mast cell stabilizers
Methylxanthines
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
Rheumatoid arthritis
RHU05
Disease-modifying
anti-rheumatic drugs
(DMARDs)
Immunologic agents
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:
Disease-modifying
anti-rheumatic drugs
(DMARDs)
Immunologic 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:
Disease-modifying
anti-rheumatic drugs
(DMARDs)
Immunologic agents
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Condition
Diagnostic Criteria
Pharmacy Criteria
Treatment Criteria
Untreated Criteria
Schizophrenia
PSY07
N/A
1 inpatient or ER
diagnosis or 2
outpatient diagnoses
plus 2 prescription
fills (within 120 days
of each other) in the
drug class:
Anti-psychotics
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-psychotics
Seizure disorders
NUR07
NURx050
1 inpatient or ER
diagnosis or 2
outpatient diagnoses
plus 2 prescription
fills (within 120 days
of each other) in the
drug class:
Anti-convulsants
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-convulsants
Age-related macular
degeneration
EYE15
Anti-angiogenic
ophthalmic agents
N/A
N/A
COPD
RES04
N/A
N/A
N/A
Chronic Renal Failure REN01, REN06
GURx020
N/A
N/A
Low back pain
N/A
N/A
N/A
MUS14
*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)
Pharmacy Adherence
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.)
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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 provide 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 detail 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.
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. The following figure depicts a gap event:
Figure 8. 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.
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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 the following figure.
Figure 9. 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 the following
figure.
Figure 10. 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
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measures adherence for 17 conditions where the chronic administration of medication is, in most
instances, appropriate.
These 17 Conditions that Require Chronic Administration of Medication are:
• Bipolar Disorder
• Congestive Heart Failure
• Depression
• Diabetes◄
• Disorders of Lipid Metabolism◄
• Glaucoma
• Human Immunodeficiency Virus◄
• 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 the Johns Hopkins
clinician advisors as a subset of drugs that once started, should be given continuously. The resultant
condition-drug class pairings are presented in the Condition-Drug Class Pairings table.
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.
Condition-Drug Class Pairings
Condition
Drug Category
Bipolar Disorder
Anti-convulsants
Bipolar Disorder
Anti-psychotics
Congestive Heart Failure
ACEI/ARB
Congestive Heart Failure
Aldosterone receptor blockers
Congestive Heart Failure
Beta-blockers
Congestive Heart Failure
Diuretics
Congestive Heart Failure
Inotropic agents
Congestive Heart Failure
Vasodilators
Depression
Anti-depressants
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Condition
Drug Category
Diabetes
Insulins
Diabetes
Meglitinides
Diabetes
Miscellaneous antidiabetic agents
Diabetes
Non-Sulfonylureas
Diabetes
Other Anti-Hyperglycemic Agents
Diabetes
Sulfonylureas
Diabetes
Thiazolidinediones
Glaucoma
Ophthalmic glaucoma agents
Human Immunodeficiency Virus
HAART* (see below)
Disorders of Lipid Metabolism
Bile acid sequestrants
Disorders of Lipid Metabolism
Cholesterol absorption inhibitors
Disorders of Lipid Metabolism
Fibric acid derivatives
Disorders of Lipid Metabolism
HMG-CoA reductase inhibitors
Disorders of Lipid Metabolism
Miscellaneous antihyperlipidemic agents
Hypertension
ACEI/ARB
Hypertension
Aldosterone receptor blockers
Hypertension
Anti-adrenergic agents
Hypertension
Beta-blockers
Hypertension
Calcium channel blockers
Hypertension
Diuretics
Hypertension
Vasodilators
Hypothyroidism
Thyroid drugs
Ischemic Heart Disease
Antianginal agents
Ischemic Heart Disease
Beta-blockers
Ischemic Heart Disease
Calcium channel blockers
Osteoporosis
Hormones
Parkinson's Disease
Anticholinergic antiparkinson agents
Parkinson's Disease
Dopaminergic antiparkinsonism agents
Persistent Asthma
Adrenergic bronchodilators
Persistent Asthma
Immunosuppressive monoclonal antibodies
Persistent Asthma
Inhaled corticosteroids
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Condition
Drug Category
Persistent Asthma
Leukotriene modifiers
Persistent Asthma
Mast cell stabilizers
Persistent Asthma
Methylxanthines
Rheumatoid Arthritis
Disease-modifying anti-rheumatic drugs (DMARDs)
Rheumatoid Arthritis
Immunologic agents
Schizophrenia
Anti-psychotics
Seizure Disorder
Anti-convulsants
Immunosuppression/Transplant
Immunologic agents
*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)
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 the
preceding Conditions that Require Chronic Administration of Medication list).
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 120-days 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.
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Detailed condition marker definitions are included in Definitions of Condition Markers on page 68.
Note: Pharmacy adherence calculations are ONLY performed for members with a TRT condition marker.
There are situations, however, 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., short-acting 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 possession calculations.
• The patient had two prescriptions within the same drug class, but not for the same active ingredient.
Five 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.
Pharmacy Adherence Module Markers
Marker
Definition
Application/Interpretation
Number of Gaps
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).
Medication Possession Ratio (MPR)
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.
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.
Continuous Single-Interval Measure
of Medication Availability (CSA)
Ratio of days supply to days until the The Continuous Single Interval
next prescription averaged across all Measure of Medication Acquisition
prescriptions.
(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.
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Marker
Definition
Application/Interpretation
Proportion of Days Covered (PDC)
Ratio of days supply divided by days
between first prescription fill and
end of observation period.
The Proportion of Days Covered
(PDC) averages possession over the
observation period. While MPR and
CSA measure the distance between
fills, PDC will consider the time
period from the first prescription
through the end of the observation
period, regardless of the timing of
the last prescription fill. This metric
has a maximum value of 1.0.
Untreated
No current evidence of treatment
with a designated class of
pharmaceuticals (see Definitions of
Condition Markers on page 68).
The untreated marker can have the
following values:
• Y – The patient has no
prescriptions in a targeted drug
class.
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
represents another potential care
management issue. It should be
noted, however, that there are
situations where prescribing can
• P - The patient is classified as
occur, but not be captured in the
potentially untreated based upon pharmacy claims. Additional research
one of the following criteria:
may be necessary before trying to
• The patient has only one
draw conclusions from this metric.
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.
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Marker
Definition
• 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.
Application/Interpretation
• N – The patient meets the
treatment criteria.
If none of the previous 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.
Pharmacy adherence calculations can be complicated by the multitude of ways that patients access
medications and by the patient's eligibility (or lack thereof) for prescription benefits. Examples:
• Pharmacy benefits outsourced or absent
• Enrollment gaps or discontinuation of eligibility
• Samples provided in the physician's office
• Discount drug programs
• Medications administered in the physician's office (e.g., injections to treat rheumatoid arthritis)
• Psychotherapy as a replacement or adjunct to medication therapy for behavioral health conditions
(Psychotherapy Service Marker on page 92)
Be mindful of these factors when drawing conclusions about pharmacy adherence.
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 the Condition-Drug Class Pairings table. 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. The MPR Example 1 table, MPR Example 2 table,
and MPR Example 3 table provide examples of MPR calculations.
MPR Example 1
Rx_fill_date
Days_supply
1/15/2014
30
Supply Available Upon
Refill
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Period
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Rx_fill_date
Days_supply
Supply Available Upon
Refill
Days Exceeding Grace
Period
2/10/2014
30
4
4/2/2014
30
2
5/19/2014
30
2
MPR=90 days supply/(5/19/2014 – 1/15/2014) = 0.73
MPR Example 2
Rx_fill_date
Days_supply
1/15/2014
30
2/10/2014
30
3/18/2014
30
4/19/2014
30
Supply Available Upon
Refill
Days Exceeding Grace
Period
4
MPR=90 days supply/(4/19/2014 – 1/15/2014) = 0.96
MPR Example 3
Supply Available Upon
Refill
Rx_fill_date
Days_supply
1/15/2014
30
2/10/2014
30
4
3/10/2014
30
6
4/06/2014
30
9
Days Exceeding Grace
Period
MPR=90 days supply/(4/06/2014 – 1/15/2014) = 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. Refer to Appendix A of the Installation and Usage Guide for more
information on this 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 the Condition-Drug Class Pairings
table. For each condition, there is a marker with the naming convention of the specific condition
appended with _CSA. The CSA Example 1 table, CSA Example 2 table, and CSA Example 3 table provide
examples of CSA calculations.
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CSA Example 1
Supply Available Upon
Refill
Days Exceeding Grace
Period
Rx_fill_date
Days_supply
1/15/2014
30
2/10/2014
30
4/2/2014
30
2
5/19/2014
30
2
4
CSA = ((30/(2/10/2014 – 1/15/2014)) + (30/(4/2/2014 – 2/10/2014)) + (30/(5/19/2014 – 4/2/2014)))/3 =
0.79
CSA Example 2
Rx_fill_date
Days_supply
1/15/2014
30
2/10/2014
30
3/18/2014
30
4/19/2014
30
Supply Available Upon
Refill
Days Exceeding Grace
Period
4
CSA = ((30/(2/10/2014 – 1/15/2014)) + (30/(3/18/2014 – 2/10/2014)) + (30/(4/19/2014 – 3/18/2014)))/3 =
0.97
CSA Example 3
Supply Available Upon
Refill
Rx_fill_date
Days_supply
1/15/2014
30
2/10/2014
30
4
3/10/2014
30
6
4/06/2014
30
9
Days Exceeding Grace
Period
CSA = ((30/(2/10/2014 – 1/15/2014)) + (30/(3/10/2014 – 2/10/2014)) + (30/(4/6/2014 – 3/10/2014)))/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. Refer to Appendix A of the Installation and Usage
Guide for more information on this file.
Proportion of Days Covered (PDC)
The proportion of days covered, available at the drug class level only, indicates the rate of possession
from the first dispensing event in the period through the observation period end date. The ACG System
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does not consider length of eligibility, so exercise caution when interpreting PDC. PDC will not exceed 1.0.
The PDC Example 1 table, PDC Example 2 table, and PDC Example 3 table provide examples of PDC
calculations.
PDC Example 1
rx_fill_date
rx_refill_date
rx_days_supply
rx_supply_available_upon
_refill
1/21/2014
3/19/2014
30
0
3/19/2014
5/3/2014
30
0
5/3/2014
5/24/2014
30
9
5/24/2014
5/25/2014
30
37
5/25/2014
7/5/2014
30
26
7/5/2014
7/20/2014
30
41
7/20/2014
8/16/2014
30
44
8/16/2014
9/13/2014
30
46
9/13/2014
10/14/2014
30
45
10/14/2014
11/17/2014
30
41
11/17/2014
12/18/2014
30
40
rx_final_supply_end_date
12/31/2014
PDC=11 fills x 30 days supply plus 0 days supply used from final prescription due to prior oversupply /
(Observation End Date 12/31/2014 – First Fill date 1/21/2014) = 330 / 344 = 0.96
PDC Example 2
rx_fill_date
rx_refill_date
rx_days_supply
2/9/2014
4/17/2014
90
4/17/2014
6/28/2014
90
rx_final_supply_end_date
rx_final_supply_used
9/6/2014
71
PDC=2 fills x 90 days supply plus 1 fill x 71 days supply used / (Observation End Date 12/31/2014 – First
Fill date 2/9/2014) = 251 / 325 = 0.77
PDC Example 3
rx_fill_date
rx_refill_date
rx_days_supply
rx_final_supply_end_date
7/20/2014
8/17/2014
30
8/18/2014
8/17/2014
9/14/2014
30
9/17/2014
9/14/2014
10/12/2014
30
10/17/2014
10/12/2014
11/9/2014
30
11/16/2014
11/9/2014
12/7/2014
30
12/16/2014
12/7/2014
12/24/2014
30
1/15/2014
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PDC=6 fills x 30 days supply plus 0 days supply used from final prescription due to prior oversupply /
(Observation End Date 12/31/2014 – First Fill date 7/20/2014) = 180 /164 = 1.10 capped to 1.0
Reporting of Possession
With the exception of PDC, reporting of possession is summarized at the condition level within the
Patients and ACG Results 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 Installation and Usage Guide.
In addition, reporting of possession (including PDC) 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. The maximum gap (largest
gap, in days, for a medication within the specific drug class) is 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 Installation and Usage Guide.
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.
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Chapter 4: Diagnosis+Pharmacy-based Markers
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 has strong face validity among clinicians. If patients fail to comply with therapy then
there are likely to be consequences downstream. For some conditions these will be felt immediately while
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).
The following table 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).
Validation Analyses
Maximum Gap Duration
DEPENDENT EFFECTS
No Gap
<7 days
8-29 Days
30+ Days
Number of Cases
2,844
496
1,035
1,167
Median Total Cost
Year One
$7,789
$7,656
$8,640
$8,094
Median Pharmacy
Cost Year One
$2,787
$2,671
$2,653
$2,426
Median Total Cost
Year Two
$7,178
$6,863
$7,319
$7,907
Median Pharmacy
Cost Year Two
$2,842
$2,724
$2,625
$2,645
0.99
0.90
0.83
0.59
Median MPR
Number of Gaps
DEPENDENT EFFECTS
No Gap
One Gap
Two Gaps
3+ Gaps
Number of Cases
2,844
1,710
590
398
Median Total Cost
Year One
$7,789
$8,154
$8,255
$8,416
Median Pharmacy
Cost Year One
$2,787
$2,529
$2,514
$2,729
Median Total Cost
Year Two
$7,178
$7,345
$7,322
$8,517
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Number of Gaps
DEPENDENT EFFECTS
Median Pharmacy
Cost Year Two
Median MPR
No Gap
One Gap
Two Gaps
3+ Gaps
$2,842
$2,637
$2,622
$2,910
0.99
0.84
0.69
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.
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. The
previous table 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 condition-drug class pairs are targeted
for potential intervention.
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Chapter 5: Utilization and Resource Use Markers
Chapter 5:
Utilization and Resource Use Markers
Utilization markers identify unique markers of risk and are specifically used in the hospitalization risk
models. There are a variety of utilization measures that are usually captured from dates of service, place
of service, type of bill, diagnosis codes, revenue codes and procedure codes. As there are variances in
coding systems, the following are intended to fully explain how the software calculates the associated
utilization variables. Optionally, these variables may be manually calculated and provided on the patient
file (see "Patient Data Format" in the Installation and Usage Guide).
Business Rules for Utilization Markers
All Cause Inpatient Hospitalization Count
The intent is to count acute care inpatient hospitalization stays, regardless of cause.
To count inpatient visits, count as one event each inpatient confinement with either: (a) IP in place of
service, or (b) room and board revenue codes on contiguous dates of service and either the number 21 in
the place of service or 11x, 12x or 18x in the type of bill. The sum of these events represents the number
of inpatient visits.
The criteria for identifying an inpatient confinement are:
• Inpatient hospital place of service (IP); or Inpatient hospital place of service code (21) or type of bill
11x, 12x or 18x, along with a revenue code (three-digit version) for room and board: 100 to 219.
• If interim claims are submitted, identify a single inpatient confinement for claims with contiguous begin
and end dates of service.
• If a patient is transferred within or between facilities without a gap in service dates, count as a single
inpatient visit.
Inpatient Hospitalization Count
The intent is to count unanticipated acute care inpatient hospitalization stays.
To count inpatient visits, count as one event each inpatient confinement with either: (a) IP in place of
service, or (b) room and board revenue codes on contiguous dates of service and either the number 21 in
the place of service or 11x, 12x or 18x in the type of bill. The sum of these events represents the number
of inpatient visits.
The criteria for identifying an inpatient confinement are:
• Inpatient hospital place of service (IP); or Inpatient hospital place of service code (21) or type of bill
11x, 12x or 18x, along with a revenue code (three-digit version) for room and board: 100 to 219.
• If interim claims are submitted, identify a single inpatient confinement for claims with contiguous begin
and end dates of service.
• If a patient is transferred within or between facilities without a gap in service dates, count as a single
inpatient visit.
Exclude from the All Cause Inpatient Hospitalization count, inpatient admissions with a primary discharge
diagnosis of:
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ICD-9-CM
Pregnancy and delivery: 630-676
Newborns: V30-V39
Injury: 800-995
E000-E999 (except E870-E876 and E878-E879)
ICD-10
Pregnancy and delivery: O00-O99, Z37
Newborns: Z38
Injury: S00-T98 (except T80-T88)
V01-Y98 (except Y40-Y84)
Inpatient Hospitalization Days
Inpatient hospitalization days count the days between the minimum service begin date and the maximum
service end date associated with each inpatient confinement in the inpatient hospitalization count.
For confinements with the same service begin and end date, count a minimum of one inpatient day.
Unplanned Inpatient Hospitalization Count
The unplanned inpatient hospitalization count is a subset of the inpatient hospitalization count with
exclusions for planned admissions. A planned admission is defined as either:
• A definitively planned procedure, identified by either ICD procedure code or diagnosis code.
Definitively planned procedures includes items such as rehabilitation services, chemotherapy and
transplants.
• A potentially planned procedure, identified by ICD procedure code. Potentially planned procedures are
more common surgical procedures, such as hip replacements, cardiovascular procedures and other
inpatient surgical treatments without evidence of acute complications such as infections, burns or
injuries.
Any inpatient hospitalization that cannot be identified as definitively or potentially planned is considered
unplanned.
Readmission 30 Day Count
The readmission 30 day count is a subset of the all cause inpatient hospitalization count. The presence of
this count indicates, regardless of cause, the identified admission occurred within 30 days of a previous
inpatient hospitalization.
Unplanned Readmission 30 Day Count
The unplanned readmission 30 day count is a subset of the inpatient hospitalization count. The presence
indicates the identified admission meets the unplanned inpatient hospitalization criteria and occurred
within 30 days of a previous inpatient hospitalization.
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Emergency Visit Count
The intent is to count emergency room visits that are not precursors to subsequent inpatient hospital
stays in the same time period. Those emergency room visits that are immediately followed by an
inpatient hospital stay are ‘absorbed’ by a hospitalization.
To count emergency room visits, count as one event each patient/day in which any record exists for
emergency room. The sum of these events represents the count of emergency room visits.
The criteria for identifying an emergency room record are any of the following:
Revenue code (three-digit version) for emergency room 450, 452, 459
CPT code for evaluation and management in emergency room7 99281, 99282, 99283, 99284, 99285,
99288
Emergency room place of service code (23) with a CPT code for surgery 10040 to 66679
Emergency room place of service (ED)
Outpatient Visit Count
The intent is to count instances where patients receive ambulatory care in outpatient settings.
To count outpatient visits, count as one event each unique record of a patient id, provider id and date of
service. The sum of these events represents the number of outpatient visits.
The criteria for identifying an outpatient service record are the following:
Physician office (11), outpatient hospital (22), and other (24, 25, 26, 50, 53, 60, 62, 65, 71, 72) place of
service codes; type of bill 13x or 14x; or outpatient (OP) place of service code.
Dialysis Service Marker
The dialysis service marker identifies the presence of (outpatient) dialysis services using procedure codes
for patients who have chronic renal failure.
The default for this marker is 0. Flag with a 1, any patients with a diagnosis for chronic renal failure
recorded at any time during the observation period that also have at least one ICD, CPT, or HCPCS
procedure code indicating dialysis.
Diagnosis Codes for Chronic Renal Failure
ICD-9-CM: 585, 586, V451, V56, V560, V561, V562, V568, 587, 7925, V5631, V5632, 45821, 5851, 5852,
5853, 5854, 5855, 5856, 5859
ICD-10: N18.x, N19, Z49.x, Z99.2
ICD Procedure Codes for Dialysis
ICD-9-CM: 38.95, 39.27, 39.42, 39.95, 54.98
ICD-10-PCS: 3E1M39Z, 5A1D00Z, 5A1D60Z
CPT Codes for Dialysis
90918, 90919, 90920, 90921, 90922, 90923, 90924, 90925, 90935, 90937, 90939, 90940, 90945, 90947,
90989, 90993, 90997, 90999, 93990, 99512
7 CPT codes copyright 2014 American Medical Association. All rights reserved. CPT is a registered trademark of the AMA.
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Chapter 5: Utilization and Resource Use Markers
HCPCS Codes for Dialysis
G0308, G0309, G0310, G0311, G0312, G0313, G0314, G0315, G0316, G0317, G0318, G0319, G0320,
G0321, G0322, G0323, G0324, G0325, G0326, G0327, G0365
Nursing Service Marker
The nursing service marker identifies the presence of nursing home services through procedure codes. The
default for this marker is 0. Flag with a 1 any patients with an instance of a procedure code indicating
nursing services.
CPT Codes for Nursing Services
94004 - 94005, 99304 - 99337
Major Procedure Marker
The intent is to identify patients with major procedures that occur in inpatient hospital settings.
The major procedure marker identifies the presence of major procedure codes. The default for this
marker is 0. Flag with a 1 any patient with a procedure code for a major procedure on a claim indicating
an inpatient place of service code (21 or IP). There are approximately 3,000 procedure codes on the major
procedure list. Due to the number of major procedure codes, this list is not included within the body of
the Technical Reference Guide. Please contact your account representative for more details.
Cancer Treatment Marker
The goal of the dichotomous marker for cancer treatment is to differentiate the active treatment phase
from the diagnostic and remission phases since the use of resources is different.
To be identified as some with active cancer treatment a patient must have at least one EDC in the
malignancy series (MAL) and any of the procedure (CPT or ICD-9-CM) or revenue codes.
CPT
• 38230-38242
• 79000-79999
• 77261-77799
• 96401-96549
ICD Procedure
ICD-9-CM:
• 00.10
• 00.15
• 41.0
• 41.91
• 92.2
• 99.25
• 99.28
• 99.85
ICD-10-PCS: Given the large number of qualifying codes, this list in not included within the body of the
Technical Reference Guide. Please contact your account representative for more details.
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Chapter 5: Utilization and Resource Use Markers
Revenue Code
• 028x
• 0973
• 0344
Mechanical Ventilation Marker
The dichotomous mechanical ventilation marker identifies the presence of mechanical ventilation through
tracheostomy or ventilator support. Mechanical ventilation carries many potential complications including
lung injury, infection and respiratory distress syndrome.
Mechanical ventilation is triggered by the presence of selected procedure or diagnosis codes.
ICD-9-CM
• V44.0
• V46.11
• V46.12
ICD-10
• Z93.0
• Z99.11
• Z99.12
CPT/HCPCS
• A4629
• C5281-C5282
• S8189
• A7520-A7527
• C5284
• 99504
• A4481
• D7990
• 94656-94657
• A4605
• K0165
• 94660
• A4608-A4610
• K0534
• 94662
• A4621-A4626
• L8501
• 94002-94005
• A7501
• L8507
• 31500
• A7504-A7506
• L8509
• 31502
• C1039
• L8511-L8515
• 31600-31603
• C1146
• S8180-S8181
• 31605
ICD Procedure
ICD-9-CM: 96.70-96.72
ICD-10-PCS: 5A1935Z, 5A1945Z, 5A1955Z
Psychotherapy Service Marker
The psychotherapy marker identifies the presence of ambulatory therapeutic interactions or treatments to
address problems that are psychological in nature. The focus is on ambulatory treatment as a proxy for
ongoing management and not crisis-related interventions.
This marker is a complement to the pharmacy adherence module where psychotherapy may supplement
or replace medication use in some patients.
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Chapter 5: Utilization and Resource Use Markers
There must be at least two instances of a qualifying service on two different dates of service within 120
days of each other to set the marker to 1.
A qualifying service is an individual medical services record with either a procedure code or a revenue
code in the range below excluding inpatient, emergency department and urgent care services with either
a place of service codes 20, 21, 23, 31, 51, IP, UC, or ED or a type of bill 11x, 12x, or 18x or revenue code
45x.
CPT-4 Codes
• 90801-90822
• 90853
• 90832-90838
• 90857
• 90845-90849
Revenue Codes
• 0513
• 0909-0919
• 0900-0904
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 costs
• 1 – 1-10 percentile
• 2 – 11-25 percentile
• 3 – 26-50 percentile
• 4 – 51-75 percentile
• 5 – 76-90 percentile
• 6 – 91-93 percentile
• 7 – 94-95 percentile
• 8 – 96-97 percentile
• 9 – 98-99 percentile
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Chapter 6: Coordination
Chapter 6:
Coordination
The Johns Hopkins ACG System introduced ACG Coordination Markers in order to identify populations that
are at risk for poorly coordinated care. Used by themselves or in conjunction with other risk markers, 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
Care coordination has traditionally been assessed using instruments that have been unable to use
administrative claims information. These coordination assessment approaches have been survey based,
and have targeted the patient's, family member’s, or provider's 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. Five patient markers make up ACG Coordination
Markers:
• Management Visit Count: The number of face-to-face physician visits with an eligible specialty.
• Majority Source of Care (MSOC): An assessment of the level of participation of those providers that
provided care to each patient.
• Unique Provider Count: A count of the number of unique providers that provided care to the patient.
• Specialty Count: A count of the number of specialty types (not the same as number of specialists seen)
that provided care to the patient.
• Generalist Seen: A marker indicating a generalist’s participation in an individual’s care.
A coordination risk measure combines these markers to determine whether a person has a "likely,"
"possible," or "unlikely" coordination issue.
A care density ratio quantifies patient sharing based on outpatient face-to-face visits with eligible
physicians.
Each of the ACG Coordination Markers limit their evaluation to outpatient face-to-face physician visits as
defined by procedure code.
CPT Codes defining Face to Face Physician Visit8
• 59400
• 99217
• 99375
• 59410
• 99218
• 99377
• 59425
• 99219
• 99378
8 CPT Codes copyright 2014 American Medical Association. All rights reserved. CPT is a registered trademark of the AMA.
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Chapter 6: Coordination
• 59426
• 99220
• 99379
• 59430
• 99241
• 99380
• 59510
• 99242
• 99381
• 59515
• 99243
• 99382
• 59610
• 99244
• 99383
• 59614
• 99245
• 99384
• 59618
• 99324
• 99385
• 90792
• 99325
• 99386
• 90805
• 99326
• 99387
• 90807
• 99327
• 99391
• 90809
• 99328
• 99392
• 90811
• 99334
• 99393
• 90813
• 99335
• 99394
• 90815
• 99336
• 99395
• 90862
• 99337
• 99396
• 92002
• 99339
• 99397
• 92004
• 99340
• 99401
• 92012
• 99341
• 99402
• 92014
• 99342
• 99403
• 99201
• 99343
• 99404
• 99202
• 99344
• 99411
• 99203
• 99345
• 99412
• 99204
• 99347
• 99420
• 99205
• 99348
• 99429
• 99211
• 99349
• 99455
• 99212
• 99350
• 99456
• 99213
• 99354
• 99499
• 99214
• 99355
• 99215
• 99374
The Management Visit Count, Majority Source of Care (MSOC), Unique Provider Count, and
Specialty Count further consider only those visits provided by eligible specialties (i.e.,
specialties that could reasonably manage the overall care for a patient). Examples of eligible
specialties are provided in the following list of Eligible Specialties:
•
•
•
•
•
Allergy & Immunology
Colon & Rectal Surgery
Family Medicine
Internal Medicine
Neurological Surgery
• Neuro-musculoskeletal
Medicine
• Nuclear Medicine
• Obstetrics &
Gynecology
• Ophthalmology
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• Oral & Maxillofacial
Surgery
• Orthopedic Surgery
• Otolaryngology
• Pain Medicine
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Chapter 6: Coordination
• 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
Face-to-face visits with provider specialties that are not eligible are not considered as part
of the Management Visit Count, Majority Source of Care (MSOC), Unique Provider Count,
and Specialty Count markers. Examples of specialties that are not considered eligible are
provided in the following list of 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
with no outpatient face-to-face visits to a generalist will have the Generalist Seen marker
set to "N". The following list of Generalist Specialties provides the list of specialties
considered generalists for this marker:
• Family Medicine
• Internal Medicine
• Geriatricians
• Pediatricians
• Preventive Medicine
• Nurse Practitioners
• Obstetrics and
Gynecology
Management Visit Count
The Management Visit Count represents the total number of face-to-face visits with an eligible specialty.
Majority Source of Care (MSOC)
The Majority Source of Care marker will determine the percent of the outpatient face-to-face visits
provided by eligible physicians that saw the member most over the measurement period. For each
patient, the ACG System determines the following:
• The percentage of visits provided from the largest source. The Management Visit Count is the
denominator for this percentage.
• The provider ID(s) that is responsible for the majority source of care. In the event that more than one
provider is assigned the same MSOC percentage, the ACG System will identify all the providers as
being the majority source of care.
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The following figure shows two patients. The patient on the left saw three eligible providers and received
eight of his ten outpatient face-to-face visits 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 11. Majority Source of Care
The following figure 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 problems.
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Figure 12. How Majority Source of Care is Distributed in a Large Commercial Managed Care Population
Unique Provider Count
The Unique Provider Count marker determines the number of unique eligible providers that imparted
outpatient care over the measurement period for any condition.
In the following figure the patient on the left saw three eligible providers giving him a unique provider
count of 3. The patient on the right saw four eligible providers and received a unique provider count of 4.
Figure 13. Unique Provider Count
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Chapter 6: Coordination
The prevalence of different levels of unique providers seen is shown in the following figure. Consistent
with Figure 12: 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 large the number of unique
providers involved the greater the likelihood of coordination issues.
Figure 14. Distribution of Unique Provider Count Within A Large Commercial Managed Care Population
Specialty Count
The Specialty Count determines the number of eligible specialty types that provided outpatient care over
the measurement period for any condition.
In the following figure, the patient on the left saw two distinct specialty types. Geriatricians and Internists
are both considered Generalists and, thus, are only counted once. The patient on the right saw four
eligible providers. This patient received a specialty count of 4.
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Figure 15. Specialty Count
The distribution of Specialty Count in a large commercial managed care population is shown in the
following figure. The results are consistent with what was presented in Figure 14: Distribution of Unique
Provider Count Within A Large Commercial Managed Care Population. Nearly two percent saw six or more
specialties and represent the population segment most likely to experience coordination issues.
Figure 16. Distribution of Specialty Count in a Large Commercial Managed Care Population
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Chapter 6: Coordination
Generalist Seen
The Generalist Seen marker determines whether or not a patient has been provided outpatient care by a
generalist in the measurement period.
In the following figure, 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 17. Generalist Seen
Risk of Poor 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 the following table.
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Coordination Risk
Likely
coordination
Issue
Possible
Coordination
Issue
Unlikely
Coordination
Issue
Coordination
Risk
Unique Provider
Count
Specialty Count
Majority Source
of Care
Generalist Seen
LCI
>=7
>=5
<=28
N
LCI
>=7
>=5
<=28
Y
LCI
>=7
>=5
>28
N
LCI
>=7
<5
<=28
N
LCI
>=7
<5
>28
N
LCI
>=7
<5
>28
Y
LCI
>=7
>=5
>28
Y
PCI
2-6
>=5
<=28
N
PCI
>=7
<5
<=28
Y
PCI
2-6
>=5
<=28
Y
PCI
2-6
>=5
>28
N
PCI
2-6
<5
<=28
N
PCI
2-6
>=5
>28
Y
PCI
2-6
<5
>28
N
UCI
2-6
<5
<=28
Y
UCI
2-6
<5
>28
Y
UCI
<=1
-
-
-
A high unique provider count is the major risk factor for being a potential coordination issue with one
exception.
The following figure 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.
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Chapter 6: Coordination
Figure 18. Impact of Poor Coordination on High Morbidity Users
Coordination is also expected to influence aspects of the quality of care provided. Considering members of
a large commercial population with asthma, the following figure 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 19. Impact of Poor Coordination on ED Use by Asthma Patients
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Chapter 6: Coordination
Care Density
Myriad formal and informal referral relationships among clinicians lead to varying levels of patient sharing.
Higher levels of patient sharing are often credited with higher intensity of care, better care coordination,
and greater potential cost opportunities.
The care density ratio is a patient-level measure of the extent of patient sharing among the physicians
that a patient saw during face-to-face encounters over a period of time. The numerator is the total
number of instances of patient sharing among a patient’s eligible physicians. Every relevant instance of
patient sharing contributes to the numerator, whereby the contributions of instance range between 1 and
10 maximally. The denominator is the total number of unique pairs of the patient’s physicians. A patient
who visited at least two physicians receives a care density ratio.
Figure 20. Care Density Ratios
The distribution of the care density ratio typically varies by the unique provider count, whether a patient
saw a generalist, and a patient’s resource utilization band (RUB). Among patients with complex morbidity
who seek care from multiple physicians one often finds a higher frequency of low care density ratios,
compared to healthier patients who seek care from a small number of physicians. Hence the lower and
upper quartile care density cutoff values vary among patients.
The care density ratio distributions are characterized by three “care density quantiles,” the bottom
quartile (LOW), the middle half (MID), and the top quartile (TOP). The ACG System outputs the care
density bottom and top quartile cutoff values.
A finding from research is that greater patient sharing and higher care density are associated with lower
resource utilization. As a consequence one may form expectations about cost savings relative to the top
quartile of care density that is achieved by members. The expected savings associated with high care
density are greater for members in the bottom quartile of care density compared to patients in the
middle half. The care density cost saving ratio considers the same factors that have an impact on the
care density ratio and additionally the patient’s current care density quartile.
The ACG System applies the care density cost saving ratio to the patient’s total cost. The result of this
calculation is presented as a care density cost saving range. This range represents the cost opportunity for
a similar patient with an assumed high care density compared to the actual lower level of care density.
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Chapter 6: Coordination
Conclusion
The ACG Coordination Markers provide a meaningful way to identify members at risk for poor
coordination. The coordination risk marker provides a 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.
The care density ratio provides a means for quantifying the strength of possible relationships among a
member's network of physicians. A network with greater density is expected to have greater potential
cost savings. These markers complement the ACG System risk scores and additional system markers in
further defining the “at risk” population.
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Chapter 7: Risk Modeling
Chapter 7:
Risk Modeling
The innovation that the ACG risk models present is their unique clinical logic coupled with their excellent
statistical performance. Each model employs risk factors that were created using clinical frameworks that
make sense to health professionals and medical managers.
This chapter describes the application of the diagnosis and pharmacy based markers in the determination
of patient risk. The chapter includes an overview of the methods used and validation of the various scores
produced by the ACG System.
Elements of a Risk Model: Five Key Dimensions
The structure of a risk model can be characterized in five dimensions: conceptual basis; reference
population; risk factors; statistical approach; and, modeled outcomes. Each of these dimensions is
discussed in more detail below in the context of the ACG System.
Conceptual Basis
The ACG System’s risk models are anchored in the same fundamental insight about morbidity that led to
the creation of ACGs. This insight, conceived by Dr. Barbara Starfield in the 1970s, is that morbidity
clusters and multi-morbidity are the best indicators of health needs. Clinicians continue to shape the ACG
System to ensure that the models retain clinical cogency as improvements are made to explanatory
power. On very large datasets, it is possible to make improvements in predictive power by just adding
new variables, but there is an art to developing well-constructed risk factors to support a parsimonious
model.
In the application of risk models, it is important to define models that are less prone to strategic
manipulation. Again, statistical improvements may be gained by increasing the number of variables;
however, this makes the model susceptible to potential up-coding. The ACG System is robust and less
likely to appraise large shifts in risk assessment on the basis of a single diagnosis code.
Modeled Outcomes
The ACG System risk models can be used to predict or explain costs (annual total healthcare or annual
pharmacy costs) and to predict the likelihood of certain sentinel events (the likelihood of being high cost,
likelihood of inpatient hospitalization, etc). Optimal performance of ACG System risk models occurs when
the model is specifically calibrated to the outcome. A specialized model will be a better fit than
attempting to use a model focused on a different outcome, e.g., using a model built to explain total cost
to explain hospitalization, and vice versa. Some users of the ACG System have built well-performing
models on additional model outcomes; e.g. mortality9; however, one of the criteria for models presented
in the ACG System is availability of reference data discussed in the following.
9 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 49:932939. http://www.ncbi.nlm.nih.gov/pubmed/21478773.
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Chapter 7: Risk Modeling
Reference Population
In order to provide a reliable standard model, the ACG System derives its risk models from a large,
representative reference population. The reference population is used as a control for a range of factors
known to affect modeled health risks such as age, gender, and morbidity burden.
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. Multiple populations were used for modeling: (1) commercial (all ages to 65 years old)
and (2) Medicare Advantage (United States federal program that pays for certain health care expenses for
people aged 65 or older through commercial managed care organizations). The ACG System also contains
an all-age model that selectively assigns risk scores based on the model appropriate to each patient:
children (0-17 years of age), adults (18-64 years of age), and the elderly (65 and over years of age). There
are also reference weights for specific population subgroups such as Medicaid TANF (a health care
support program for children and families in the U.S.). The selection of reference population is made
when processing the ACG data file with the selection of Risk Assessment Variables.
Various subsets of data for the period 2009-2011 were used for development and validation, depending
on the model requirements. The population was restricted to include enrollees that had both pharmacy
and medical eligibility. Additional eligibility requirements vary by model, but no less than six months of
eligibility is required for defining risk factors.
Risk Factors
As described in the previous chapters, the ACG System derives a wide range of risk factors depending on
available data.
The ACG System models make use of the comprehensive array of metrics available within the entire ACG
risk adjustment system. The risk factors that constitute the ACG System models are shown in the
following figure, and are discussed in detail following. The full list of variables used in the models are
available in Appendix A: Variables Necessary to Locally Calibrate the ACG System Risk Models on page
129.
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Chapter 7: Risk Modeling
Figure 21. Risk Factors in the ACG System Models
Age and gender are included to assess age-related and gender-based health needs.
Overall disease burden is measured using the ACG categorization of morbidity burden. For more
information, see Patterns of Morbidity – Adjusted Clinical Groups (ACGs) on page 17. For predictive
models, 27 individual ACGs that represent the most resource intensive morbidity groups are included; for
concurrent models, 30 individual ACGs are utilized. All other ACGs are incorporated into the concurrent
and prospective models as one of two groups. Individual ACG Categories Included in the Risk Models on
page 129 and ACGs Included in Three Resource Groups on page 130 are derived from ACG category
alone.
Disease markers. 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 predictive model. A sub-set of the ACG System’s Expanded
Diagnostic Clusters (EDCs) is used to identify the high impact conditions in the model. The Concurrent Risk
models also include EDC conditions that are acute in nature. For more information, see Disease Markers Expanded Diagnosis Clusters (EDCs) on page 41.
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Pregnancy without delivery is included in predictive models because of the high use of resources
associated with delivery that will occur in the subsequent assessment period. For more information, see
Pregnancy without Delivery on page 59.
This marker is not used in concurrent models because the delivery is a future event. Concurrent models
incorporate pregnancy using individual ACG categories.
Hospital dominant morbidity types are based on diagnoses that, based on expert clinical review, would be
associated with a high likelihood 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. For more information, see Hospital Dominant Morbidity Types on page 55.
Frailty is a dichotomous (on/off) variable that indicates whether an patient has a diagnosis falling within
any clusters that represent medical problems associated with frailty. For more information, see Frailty
Conditions on page 56.
Rx-defined morbidity groups. All Rx-MGs are included in the predictive model, either independently or as a
complement to the diagnosis-based markers. For more information, see Rx-Defined Morbidity Groups (RxMGs) on page 60.
The Concurrent Risk models may optionally consider pharmacy data when available. Due to the circularity
in defining morbidity based on prescribing, it is strongly recommended against using pharmacy data to
contribute to risk for any profiling or payment applications. The option is available to assess potential
biases in the diagnosis data that might be reflected in the pharmacy data.
Active Ingredient Count is considered in the predictive model when an individual has had medications
representing 14 or greater individual active ingredients. For more information, see Active Ingredient Count
on page 66.
The Concurrent Risk models may optionally consider pharmacy data when available. Due to the circularity
in defining morbidity based on prescribing, it is strongly recommended against using pharmacy data to
contribute to risk for any profiling or payment applications. The option is available to assess potential
biases in the diagnosis data that might be reflected in the pharmacy data.
Costs Percentiles can be added to the predictive model as a measure of demand and need for services
not captured by the treated morbidity information available in diagnostic codes. The model uses either
prior pharmacy costs or total health care costs as presented in the pharmacy cost band or total cost band
(see Resource Bands on page 93) depending on the outcome of interest. Cost percentiles are not utilized
in the concurrent model.
Utilization Markers are considered in the hospitalization and readmission models only. The complement of
utilization markers includes inpatient hospitalization, emergency visits, outpatient visits, dialysis service,
nursing service, major procedure, psychotherapy service, active cancer treatment, mechanical ventilation
and CAL-SSA. The readmission model also includes inpatient days and unplanned readmissions within 30
days. For more information, see Predictive Models for Hospitalization on page 123.
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 most complete clinical picture of the patient’s health needs and demand for care that
is possible using administrative data.
While a model calibrated to look at diagnosis, pharmacy and prior cost information may yield the highest
statistical performance in theory, missing input data is a practical challenge that health care organizations
face. Rather than enforce the use of all risk factors, a number of model variants have been developed to
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adapt to available data. The ACG System will apply logic to determine the model that will make use of the
most available data. This model will be identified in the summary statistics.
Statistical Approach
Three types of models are commonly used depending on the outcome variable:
• Actuarial cells are categorical variables that can directly explain variation in an outcome variable (e.g.,
Age-gender or ACG categories are examples of actuarial cells used for explaining total cost).
• Linear regression models are used for continuous outcome variables (e.g., total cost or pharmacy cost).
• Logistic regression models are used for binary/dichotomous outcome variables (e.g., probability of high
cost, probability of inpatient hospitalization or probability of readmission).
Actuarial cells provide an easily calibrated model for explaining the relative risk of an outcome. Deriving a
risk score from an actuarial cell requires only basic arithmetic. In use in the ACG System, the actuarial cell
determination of relative risk is calculated as the mean cost of all patients in an actuarial cell divided by
the mean cost of all patients in the population. This method is also referred to as indirect standardization
because comparisons are made by extrapolating a comparison of each person to the average. This
method is employed for age-gender concurrent risk, local ACG concurrent risk and reference ACG
concurrent risk. For more information, see Patterns of Morbidity – Adjusted Clinical Groups (ACGs) on
page 17.
Linear Regression using ordinary least squares parameter estimation is widely used and, when calculated
against a substantially large population, provides fairly stable estimates. The individual predictions have an
intuitive appeal, because they can readily be translated into monetary values. While 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), the models are sufficiently robust that some departures from
the assumptions can be tolerated. 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 System uses 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 applied to dichotomous/binary 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). The output from logistic regression estimates the probability that the modeled event will
occur. The probability is expressed as a decimal number from 0 to 1. Logistic regression assumes a specific
functional form between predictors and the outcome (a logit relationship) that differs from typical linear
models of health care resource use.
Concurrent Cost Models
A concurrent risk model, also commonly referred to as a retrospective model, is an explanatory model
where the risk factors are derived from the same time period as the outcome variable. In the ACG
System, concurrent models are used to explain historical costs. For example, a concurrent risk model may
use patient experience from 2014 to derive risk factors (e.g., age-gender or ACG) in order to explain
variation in 2014 total expenditures. Concurrent risk models are often applied in the context of
performance assessment in order to derive the expected level of resource utilization. Learn more about
the application of concurrent cost models in the Applications Guide.
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Local Age-Gender Concurrent Risk
There is an occasional need to compare a risk score with some reference standard. Scores based solely on
age and gender are often used for this purpose. The software computes the reference standard using a
locally-derived, concurrently calibrated measure of risk using the following age groups:
• 0-4 years
• 5-11 years
• 12-17 years
• 18-34 years
• 35-44 years
• 45-54 years
• 55-64 years
• 65-69 years
• 70-74 years
• 75-79 years
• 80-84 years
• 85+ years
The computational method uses an actuarial cell approach; refer to Statistical Approach on page 110.
Patients are distributed into different risk “cells” based on their age and gender. The local age-gender
concurrent risk is calculated as the mean cost of all patients in the age-gender cell divided by the mean
cost of all patients in the population. Risk scores greater than 1.0 indicate that, on average, patients in
that age-gender cell cost more than average while risk scores less than 1.0 indicate that patients in the
age-gender cell cost less than average.
ACG Concurrent Risk
An ACG concurrent risk is an assessment of the relative resource use for individuals in the ACG applying
an actuarial cell approach. The ACG concurrent risk 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 ACG concurrent risk weights
derived from external reference data is available in the software output file as Reference ACG Concurrent
Risk.
Separate sets of weights exist for under 65 working age populations, over age 65 Medicare eligible
populations (United States federal program that pays for certain health care expenses for people aged 65
or older), and all age populations. The weights used are 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 and all individuals assigned the same ACG category will have the same ACG Concurrent Risk. 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. If the all age reference is
selected, different weights will be applied to children (0-17), adults (0-64) and elderly (65+). The softwaresupplied weights may be considered a national reference or benchmark for comparisons with locally
calibrated ACG-weights. In some 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.
The software-supplied Reference ACG Concurrent Risk is supplied in two forms: unscaled and rescaled.
Unscaled Reference ACG Concurrent Risk represents the values of the Reference ACG Concurrent Risk
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applied to a population of interest. If the mean of the Unscaled ACG Concurrent Risk 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 Reference ACG Concurrent Risk that mathematically
manipulates the Unscaled Reference ACG Concurrent Risk to have a mean of 1.0 in the local population.
The steps for performing this manually are discussed in the following "Rescale and Assign Monetary
Values Process" section.
Our experience indicates that ACG concurrent risk weights, especially when expressed as relative values,
have remarkable stability. Where differences in ACG concurrent risk are present across healthcare
organizations, it is almost universally attributable to differences in covered services reflected by different
benefit levels and cost structures.
If local cost data are available, the ACG Software also calculates Local ACG Concurrent Risk. 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.
Rescale and Assign Monetary Values Process
The rescaling process consists of the following steps:
Step 1: Compute population mean risk Compute a separate grand mean for each of the weights (either
concurrent or predicted risk) generated for the client’s population (the observations represent individuals).
The mean for this example is shown at the bottom of Column B in the table following.
Step 2: Apply weighting factor Divide each individual’s risk by the rescaling factor (i.e., the mean)
computed in Step 1. The result is the rescaled relative risk (Column C).
Step 3: Compute population mean cost For the same population on which the weights were based,
compute the mean cost for the current data year. For this example, the mean cost was 1,265.11.
Step 4: Compute cost Multiply the rescaled relative weights generated for each member of the
population (Column C) by the average population cost generated from Step 3 to calculate an estimated
individual cost (Column D).
A Member
B Relative Weight
C Rescaled Weight
D Estimated Cost
1
0.185
0.171
216.36
2
0.291
0.268
339.61
3
0.387
0.357
451.64
4
0.457
0.422
533.33
5
0.541
0.499
631.33
6
0.609
0.562
711.58
7
0.696
0.642
812.58
8
0.842
0.777
982.84
9
1.025
0.946
1,196.68
10
1.293
1.194
1,510.19
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A Member
B Relative Weight
C Rescaled Weight
D Estimated Cost
11
1.892
1.746
2,209.38
12
4.783
4.415
5,585.78
Mean
1.083
1.000
1,265.11
The rescaling factor functions as a summary case-mix index for understanding how the rating population
(e.g., local population) compares to the reference population. The interpretation of this factor is analogous
to how one interprets both relative risks and profiling indicators. If the rescaling factor is greater than 1.0
(as it was in the example), then the client’s population is sicker. If the factor is less than 1.0, then the
client’s population is healthier than the reference population.
Concurrent Risk (Regression-based)
Virtually since the inception of the ACG System, the ACG Concurrent Risk approach has been the sole
means for concurrent adjustment of costs. Because of the more granular refinement offered by using
linear regression, a regression-based concurrent risk score based on numerous risk factors with concurrent
cost as the outcome, is now also provided. While the ACG Concurrent Risk is the same for all members
within an ACG, there is actually a continuum of risk within that cell that can be detected by linear
regression. The regression-based concurrent risk model uses the same family of risk factors used in the
diagnosis-based predictive model plus a complement of acute conditions to account for potentially costly
conditions of limited duration.
The model uses standard coefficient weights derived from the selected reference population. These
weights are then applied to each patient based on the specific risk factors identified by the ACG System.
The weights are additive at the patient level as in the following example.
Predictive Modeling Factor
Coefficient
Demographic
Age: 60
Gender: F
0.0371
0.0031
Diagnosis-based Markers
3 Hospital Dominant Morbidity
Types
ACG 5060 – 10+ ADGs, 3 Major
END09 - Type 1 Diabetes
w/complication
CAR14 - Hypertension w/o
complications
9.4970
1.4967
0.6096
0.0834
Concurrent Risk (regression-based) = sum of coefficients
11.7269
As with the Reference ACG Concurrent Risk, the Concurrent Risk is supplied in two forms: unscaled and
rescaled. Unscaled Concurrent Risk represents the risk weights of the Concurrent Risk regression model
applied to a population of interest. If the mean of the Unscaled Concurrent Risk 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 Concurrent Risk that mathematically manipulates the
Unscaled Concurrent Risk to have a mean of 1.0 in the local population. The steps for performing this
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manually are discussed in Rescale and Assign Monetary Values Process; refer to Rescale and Assign
Monetary Values Process on page 112.
Concurrent Model Performance Assessment and Validation
In creating regression models it is customary to divide the data randomly into two parts, a test dataset
and a validation dataset. The models are designed using the test dataset, and then validated on the other
dataset to avoid statistical over-fitting of the model. The typical statistics used to validate concurrent cost
models are Adjusted R-Squared as a measure of individual variation explained and Expected to Actual
ratios as a measure of population variation explained.
R-Squared
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. The R-squared value,
expressed as a decimal number between 0 and 1, is interpreted as how much variance around the mean
is explained by the model. A perfect model would explain all the variation and the R-squared would be 1.
This, of course, is a rare occurrence and, in fact, suggests that there might be some design defect in the
model. Outlier values can diminish the R-squared performance and are sometimes truncated. Model
performance can also be improved by focusing only on those persons who have high values of the
modeled outcome where variation is also high. A good fit will occur although the model may actually not
work as well for persons who are not in the highest group.
The following table provides the R-squared values for various concurrent model options presented in the
ACG System using the all age reference population.
R-Squared Performance for ACG System Concurrent Risk Models
R-Squared Modeling Total Cost
without Truncation
R-Squared Modeling Total Cost
Truncated at $250,000
Local Age-Gender Risk
0.035
0.056
Local ACG Concurrent Risk
0.229
0.332
Reference ACG Concurrent Risk
0.231
0.333
Concurrent Risk (regression-based)
0.405
0.510
Source: PharMetrics, Inc., a unit of IMS, Watertown, MA; national cross-section of managed care plans;
population of 3,310,540 commercially insured lives (less than 65 years old) and population of 501,987
Medicare beneficiaries (65 years and older). Data for 2010 were used for model development, and data
for 2009 were used for model validation.
Expected to Actual Cost Ratios
The credibility of models does not solely rest on regression fit, which is a measure of performance at the
individual level. To determine how well models perform at estimating cost across the full range of risk, we
also recommend looking at persons in different cost cohorts (we use quintiles).
Expected to Actual Cost Ratios are another means for assessing risk models. The ratio is modeled
outcome to what is actually observed. In the case of costs, one might array the population into different
cost quintiles and consider the expected to actual cost ratios for each of these quintiles. The goal is to
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approach 1.0 as closely as possible, especially in the high cost quintile. Ratios less than 1.0 mean that the
model under-estimates costs while ratios over 1.0 suggest that the model is over-estimating costs.
The following table shows these ratios for the various concurrent model options presented in the ACG
System using the all age reference population.
Expected to Actual Cost Ratios by Cost Quintile for ACG System Concurrent Risk Models
Local ACG
Reference ACG
Local Age-Gender Concurrent Risk Cost Concurrent Risk Cost
Risk Cost Ratio
Ratio
Ratio
Concurrent Risk
(regression-based)
Cost Ratio
Top 1%
0.05
0.32
0.31
0.54
Top 5%
0.13
0.49
0.48
0.66
Top 20%
0.31
0.71
0.71
0.82
Mid-High
1.78
1.80
1.80
1.49
Mid
4.05
2.72
2.71
2.02
11.06
4.42
4.38
2.99
Low-Mid
Bottom 20%
480.85
Source: PharMetrics, Inc., a unit of IMS, Watertown, MA; national cross-section of managed care plans;
population of 3,310,540 commercially insured lives (less than 65 years old) and population of 501,987
Medicare beneficiaries (65 years and older). Data for 2010 were used for model development, and data
for 2009 were used for model validation.
There is a tendency for all of the models to somewhat overestimate costs among the lowest cost quintile
and somewhat underestimate costs in the highest cost quintile. This effect is most pronounced in the age
and gender model. The various morbidity-based ACG System concurrent models all substantially
outperform age and gender.
Deciding between ACG Concurrent Risk or Regression-based
Concurrent Risk
The ACG method of determining concurrent risk using the mean costs within an ACG category remains a
mainstay of risk adjustment. The method is conceptually simple and can allow for further calibration on
local cost data or other outcomes, such as visits. The ACG method can even be used for relatively small
populations by aggregating similar ACG categories into Resource Utilization Bands (RUBs). This method has
been validated in numerous settings and remains both stable and resistant to strategic manipulation.
The ACG method is not without challenges. In particular, using ACG categories as a basis of risk
adjustment does not account for within-cell variation. When a biased population is evaluated, this
variation can be distributed disproportionately. For example, if a primary care practice based at a tertiary
care center receives referrals for complex cases, the caseload is likely to look quite different (i.e., the
patients are likely to have higher health service needs) than a community-based primary care practice,
even after adjusting for differences in ACG category. A regression-based approach to concurrent risk may
improve the risk assessment for biased populations by creating a more refined prediction for each
individual member. This improves statistical performance, specifically for higher risk patients. The
improvement in statistical performance for high risk patients is offset by a tendency for regression-based
models to over-predict low risk patients.
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While statistical performance is improved, there are trade-offs to the regression-based approach as well.
Regression-based models can be more difficult to explain and with unique scores for individual patients
may be interpreted with a false sense of precision. The model is built on an external population and may
not reflect the local practice patterns or cost structures of the population being evaluated. Likewise, a
regression model explaining total cost does not readily translate to assessing other outcomes, such as
visits. Because each new risk factor discovered will increase the risk score for the patient, regression-based
approaches are potentially more susceptible to strategic manipulation.
Ultimately, the decision to use an ACG categorical approach to risk adjustment or a regression-based
approach to risk adjustment must consider the audience and intended use. If statistical performance for
high risk members is critically important, then the regression-based approach may be most appropriate.
However, for many applications, especially with the introduction of incentives, the ACG categorical
approach may be equally effective without creating opportunities for strategic manipulation. Many of the
perceived weaknesses of the ACG categorical approach may be overcome with thoughtful application of
the risk adjustment process. In the context of performance assessment, please see Chapter 4 of the
Applications Guide for more specific considerations when profiling. One of the unique strengths of the
ACG model is the ability to derive local calibrations. For more information, see Local Calibration of ACG
Risk Models on page 122.
Prospective Cost Models
A prospective risk model is a predictive model where the risk factors are derived from the period prior to
the assessment of outcome variable. In the ACG System, prospective models are used to predict both the
anticipated cost level and to predict those members that are likely to experience high costs. For example,
a prospective risk model may use patient experience from 2014 to derive risk factors in order to explain
anticipated 2015 total expenditures. Prospective risk models are often applied in the context of population
stratification and high risk case identification or in the setting of premium rates as part of the
underwriting process. Learn more about the application of prospective cost models in the Applications
Guide.
Predicted Total/Pharmacy Cost Risk
The predicted risk outputs from the ACG System represent the relative risk for a specific outcome, either
total cost in the 12 months subsequent to the observation period or pharmacy cost in the 12 months
subsequent to the observation period. The predicted risk is the result of a linear regression utilizing risk
factors derived from either diagnosis data, pharmacy data, or both, and either exclusive or inclusive of
prior cost.
The model uses standard coefficient weights derived from the selected reference population. These
weights are then applied to each patient based on the specific risk factors identified by the ACG System.
The weights are additive at the patient level as in the following example.
Predictive Modeling Factor
Demographic
Coefficient
Age: 60
Gender: F
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0.4568
0.0109
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Predictive Modeling Factor
Coefficient
Diagnosis-based Markers
2 Hospital Dominant Morbidity
Types
ACG 5060 – 10+ ADGs, 3 Major
END09 - Type 1 Diabetes
w/complication
CAR15 - Hypertension w/
complications
REN01 – Chronic Renal Failure
1.7124
0.1811
0.5534
0.1241
0.8321
Pharmacy-based Markers
CARx030 – Cardiovascular/High
Blood Pressure
INFx010 – Infections/Acute Major
0.1393
0.2015
Cost Percentile Markers
Total Cost 51-75th percentile
0.2172
Unscaled Predicted Risk = sum of coefficients
4.4288
As with the concurrent models, the Predicted Risk (total cost or pharmacy cost) is supplied in two forms:
unscaled and rescaled. Unscaled Predicted Risk represents the risk weights of the Predicted Risk
regression model applied to a population of interest. If the mean of the Unscaled Predicted Risk 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 underestimated, the ACG System also makes available a Rescaled Predicted Risk that mathematically
manipulates the Unscaled Predicted Risk to have a mean of 1.0 in the local population. The steps for
performing this manually are discussed in Rescale and Assign Monetary Values Process (see Rescale and
Assign Monetary Values Process on page 112).
Rank Probability High Total/Pharmacy Cost
While predicted risk is useful in providing a cost estimate, differences in the mean across populations can
change the shape of the predicted risk distribution. This creates a challenge when trying to establish a
threshold for predicting high cost members. The rank probability of high total cost and rank probability of
high pharmacy cost represent transformations of the predicted risk score into a standardized distribution.
The predicted risk is sorted and arrayed into 1000 quantile groups. The rank probability is an estimate of
the likelihood of the individual having costs in the top 5% of the population in the subsequent period
given the quantile group the predicted risk falls into. Because the methodology is looking at 1000
quantiles, it is possible for many individuals to have the same rank probability score. The output is
suppressed for populations less than 100.
The result will always yield a uniform distribution, regardless of the underlying morbidity of the
population. This can be useful when trying to manage the top 1-5% of members.
Reference Probability High Total/Pharmacy Cost
The reference probability high total cost and reference probability high pharmacy cost are derived from a
logistic regression where the dependent variable is patients in the top 5% of costs in the subsequent
period. The model takes the form of a logistic regression, meaning model coefficients from the selected
reference population are added and the final sum transformed to a probability as in the example
following.
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Predictive Modeling Factor
Coefficient
Demographic
Age: 60
Gender: F
-3.1258
-0.1141
Diagnosis-based Markers
2 Hospital Dominant Morbidity
Types
ACG 5060 – 10+ ADGs, 3 Major
END09 - Type 1 Diabetes
w/complication
CAR15 - Hypertension w/
complications
REN01 – Chronic Renal Failure
-0.1368
0.2489
0.2403
0.0194
0.2053
Pharmacy-based Markers
CARx030 – Cardiovascular/High
Blood Pressure
INFx010 – Infections/Acute Major
0.0501
0.0153
Cost Percentile Markers
Total Cost 51-75 percentile
-0.3218
Sum of coefficients
2.9192
0.0512
Probability of high total cost =
As noted above, the output from logistic regression estimates the probability that the modeled event will
occur. The probability is expressed as a decimal number from 0 to 1.
Although a cost model that predicts a continuous outcome and one that predicts a dichotomous outcome
may use the same set of predictor variables, it is possible for there to be slight discrepancies between
who gets identified as the highest risk because of the differences in how the outcomes are defined. The
logistic model is predicting those at high risk while the linear model is predicting cost broadly. These
discrepancies have been noted in the past and, as a consequence, the rank probability score is based on
the distribution of linear risk scores indicating the likelihood that the individual will be high risk relative to
the study population while the reference probability score indicates the likelihood that the individual will
be high risk relative to the reference population.
Prospective Model Performance Assessment and Validation
R-Squared
The R-squared value is a measure of model performance, expressed as a decimal number between 0 and
1, and interpreted as how much variance around the mean is explained by the model. For more
information, see Concurrent Model Performance Assessment and Validation on page 114.
The following table provides the R-squared values for the total cost and pharmacy cost predictive model
options presented in the ACG System using the all age reference population and diagnosis, pharmacy and
prior cost as model inputs.
R-Squared Total
R-Squared Total Cost truncated to
Cost
250K
0.197
0.246
R-Squared Total Cost
truncated to 100K
0.280
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R-Squared Rx
Cost truncated to
R-Squared Rx Cost
50K
0.445
0.610
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Source: PharMetrics, Inc., a unit of IMS, Watertown, MA; national cross-section of managed care plans;
population of 3,310,540 commercially insured lives (less than 65 years old) and population of 501,987
Medicare beneficiaries (65 years and older). Data for 2010-11 were used for model development, and
data for 2009-10 were used for model validation.
Predictive Ratios
Predictive Ratios, the ratio of predicted to actual cost, are another means for assessing predictive risk
models. In the case of costs, one might array the population into different cost quintiles and consider the
predicted to actual cost ratios for each of these quintiles. 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-estimates
costs while ratios over 1.0 suggest that the model is over-estimating costs.
The following table shows these ratios for the predictive model presented in the ACG System using the all
age reference population and diagnosis, pharmacy and prior cost as model inputs.
Expected to Actual Cost Ratios by Cost Quintile for ACG System Predictive Risk Models
Quantile
Predictive Ratio
Top 1%
0.24
Top 5%
0.38
Top 20%
0.60
Mid-High
1.78
Mid
2.90
Low-Mid
5.70
Bottom 20%
76.33
Source: PharMetrics, Inc., a unit of IMS, Watertown, MA; national cross-section of managed care plans;
population of 3,310,540 commercially insured lives (less than 65 years old) and population of 501,987
Medicare beneficiaries (65 years and older). Data for 2010-11 were used for model development, and
data for 2009-10 were used for model validation.
Sensitivity and Positive Predictive Value
For logistic regression, performance is assessed based upon how well the occurrence of the modeled
event matches the actual data. Some measures include:
• Sensitivity
• Specificity
• Positive Predictive Value (PPV)
• Negative Predictive Value (NPV)
The sensitivity is the number of predictions with a true outcome identified by the model at a specific cutpoint (true positives) as a percent of all positive outcomes (true positives plus false negatives). Conversely,
the specificity represents the number of negative predictions identified by the model (true negatives) as a
percent of all negative outcomes (false positives plus true negatives). The PPV represents the ratio of
positive predictions identified by the model (true positives) to all positive outcomes at a specific cut-point
(true positives plus false positives). Finally, NPV is the proportion of negative predictions (true negatives) as
a percent of all negative outcomes (false positives plus true negatives).
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A classification accuracy measure can also be calculated as the proportion of true predictions (true
positives + true negatives) to all predictions.
Outcome
Prediction
Positive
Negative
Positive
True Positive
False Positive
Negative
False Negative
True Negative
Sensitivity is very salient when you are concerned about the prevalence of an event in the target
population. PPV is useful when you want to intervene on someone with the identified event (e.g, a
disease). It expresses the likelihood that this person actually has the targeted event.
The following table shows these ratios for the rank probability high total cost and reference probability
high total cost models presented in the ACG System using the all age reference population and diagnosis,
pharmacy and prior cost as model inputs. Patients with year 2 costs in the top 5% of the population were
considered a positive outcome. Patients with a prediction in the top 5% of the population were
considered a positive prediction.
Classification Accuracy of the ACG System Probability High Total Cost Models
Rank Probability High Total Cost
Reference Probability High Total
Cost
Sensitivity
39.8%
40.1%
Specificity
96.8%
96.8%
Positive Predictive Value
39.8%
40.1%
Negative Predictive Value
96.8%
96.8%
Classification Accuracy
94.0%
94.0%
Source: PharMetrics, Inc., a unit of IMS, Watertown, MA; national cross-section of managed care plans;
population of 3,310,540 commercially insured lives (less than 65 years old) and population of 501,987
Medicare beneficiaries (65 years and older). Data for 2010-11 were used for model development, and
data for 2009-10 were used for model validation.
Additional Probability Models
Probability of Unexpected Pharmacy Cost/High Risk Unexpected Pharmacy
Cost
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
Risk for Unexpected Pharmacy Cost 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 diagnosis-based morbidity burden.
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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 markers in the model are
the same as the predictive model using diagnosis and pharmacy inputs without prior cost (see Appendix
A: Variables Necessary to Locally Calibrate the ACG System Risk Models on page 129). 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 diagnosis information.
High Risk for Unexpected Pharmacy Cost is a binary flag that indicates individuals with a Probability of
Unexpected Pharmacy Cost greater than 0.4.
Objectives for success for the High Risk for Unexpected Pharmacy Cost Model included validation statistics
similar to those detailed for the traditional predictive models; however, to warrant inclusion of an
additional model in the ACG System, the High Risk for Unexpected Pharmacy Cost Model needed to
identify a different group of individuals than traditional predictive modeling methods. The following table
summarizes the overlap of the High Risk for Unexpected Pharmacy Cost Model and Predicted Risk from
diagnosis inputs to identify future high pharmacy users with moderate morbidity burden or higher. This
table indicates that the overlap between the two models was only 16%. In short, the High Risk for
Unexpected Pharmacy Cost Model is identifying a new group of patients not identified by prior methods.
Overlap of the High Risk for Unexpected Pharmacy Cost Model and Diagnosis-Based Predicted
Risk (Top 1.5% of Scores)
Total
Identified by the High
Risk for Unexpected
Pharmacy Cost Model
only
Identified by Diagnosis-Based
Predicted Risk only
Identified by both the High
Risk for Unexpected
Pharmacy Cost Model and
Diagnosis-based Predicted
Risk
Total
20,722
20,709
7,822
49,253
42.07%
42.05%
15.88%
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 diagnosis 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 case managers to:
• Better identify those at risk for future high pharmacy utilization, and
• Provide insights that might allow for providing better coordination or refining the patient’s drug
regimen.
Probability of Persistent High User
A common phenomenon associated with predictive modeling is that someone who is high cost one year
will have lower costs the next year (i.e., regression to the mean). Relatively few studies have
characterized and/or developed predictive models to identify persistent high cost users, people with high
medical costs consistently across multiple consecutive time periods; these prior studies suggest that
people with chronic diseases (such as diabetes) and psycho-social conditions (such as mental illness) are
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more likely to be a persistent high cost user. These patients do not experience regression to the mean
and do represent an ongoing cost burden to organizations responsible for their care.
The persistent high user model uses a logistic regression to predict individuals likely to be in the top 20%
of users in the population for four consecutive half year periods following the assessment of risk factors.
Model performance was measured using sensitivity and positive predictive value (PPV) (see Sensitivity and
Positive Predictive Value on page 119). The specialized persistent high user model outperforms the more
generic high cost model in identifying this unique set of members that do not regress to the mean over
multiple years.
Sensitivity and Positive Predictive Value for the Persistent High User Model compared to Rank
Probability High Total Cost
True Persistent High Users
(top 20% in four
consecutive 6 month
periods of years 2 and 3)
True High Users (top
6.14% in Year 2 to keep
same proportion
identified)
% of Total Sample
6.14%
6.14%
Top 5% of Probability
Persistent High User
Sensitivity
49.7%
37.8%
PPV
61.1%
37.3%
Top 5% of Rank
Probability High Total Cost
Sensitivity
39.0%
36.7%
PPV
47.9%
36.6%
Threshold
Indicators
Local Calibration of ACG Risk Models
The standard model outputs provided in the ACG System are based on a large reference database based
on commercial managed care plans in the U.S. We recommend that users attempt local calibration if they
feel that their population is likely to differ significantly from the reference population and outcome data is
available. To develop a locally-based score would involve fitting a regression to local data using the
variables included within the ACG Models. A listing of the predictor variables (the independent variables) is
provided in Appendix A: Variables Necessary to Locally Calibrate the ACG Predictive Models on page 129.
Using these variables and local cost data, an experienced analyst could develop a new set of scores that
are customized for the local population. Custom models should be based on populations of no fewer than
100,000 individuals.
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Chapter 8: Predictive Models for Hospitalization
Chapter 8:
Predictive Models for Hospitalization
Inpatient care is part of the healthcare system in every country. The ability to predict hospitalizations with
accuracy improves the allocation of healthcare resources in budgetary systems, and in systems that link
financial incentives to efficiency and quality of care. The value of a model-based screening strategy is to
identify high risk individuals for population health interventions.
Typology of Acute Care Hospitalizations
Acute care hospitalizations are clinical events that are potentially predictable. For example, admissions that
are attributed to congestive heart failure can have a medical history leading up to the event. Other
hospitalizations are potentially predictable based on prior medical history and a set of utilization markers.
Unplanned hospitalizations may also have an external cause such as poisonings and injuries suffered in
accidents. These hospitalizations are all a focus of the ACG predictive modeling.
Admissions for acute illness or for complications of care are unplanned. Planned admissions are those in
which a planned inpatient procedure occurs. A set of surgical and medical inpatient procedures that are
considered to be planned is used by the U.S. Centers for Medicare and Medicaid Services (CMS) for
quality assessment purposes10. We have adapted the CMS definition of planned admission to include
childbirth.
The following figure gives an overview over a guiding typology of hospitalizations.
Figure 22. Typology of Acute Care Inpatient Hospitalization
How the ACG System Predicts Hospitalization
The ACG System uses a tiered approach to making predictions about hospitalizations. The predictions
about future hospitalization consider whether a person had a hospitalization in the prior time period.
Persons who have had a prior hospitalization empirically 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
10 Hospital-wide All-cause Unplanned Readmission (HWR) Measure, see http://www.qualitynet.org.
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increased likelihood of hospitalization compared to younger individuals. The ACG System uses age 55 as
the threshold for separating two hospitalization risk groups.
The hospitalization prediction model differs from the Hospital Dominant Morbidity Types marker. Hospital
Dominant Morbidity Types represent a small subset of diagnoses associated with high rates of admission
in the following 12 months. The hospital prediction model identifies a larger pool of patients at risk for
hospitalization.
Predictive Models for Hospitalization include Utilization
Measures
The ACG predictive model framework has been amended to improve the prediction of hospitalization
events. The framework for predicting hospitalization includes several utilization markers which represent a
plausible set of valid predictors. For example, emergency care and inpatient hospitalization can be
precursors to future inpatient hospitalization.
Several utilization measures support the calculation of hospitalization risk. These measures use diagnosis
codes, dates of service, place of service, procedures, and revenue codes in the Medical Services Input File.
Alternatively, users may provide these markers in the Patient Input File.
Dialysis Service
The dialysis service marker indicates that a patient with chronic renal failure has received a dialysis service
during the observation period. For more information, see Dialysis Service Marker on page 90.
Nursing Service
The nursing service marker indicates that a patient has used services in a nursing home, domiciliary, rest
home, assisted living, or custodial care facility during the observation period. For more information, see
Nursing Service Marker on page 91.
Active Cancer Treatment
The active cancer treatment marker indicates that a malignancy EDC was present and chemotherapy
and/or radiation therapy was performed during the observation period. This marker is useful for
measuring hospitalization risk, because hospitalization rates for cancer patients are high during the active
treatment phase. For more information, see Cancer Treatment Marker on page 91.
Psychotherapy
The psychotherapy marker identifies the presence of outpatient therapeutic treatments of mental health
conditions. The focus is on ambulatory treatment as a proxy for ongoing management. This marker is
useful for measuring hospitalization risk, because hospitalization rates for mental health patients are high
during active psychotherapy. For more information, see Psychotherapy Service Marker on page 92.
Mechanical Ventilation
The mechanical ventilation marker identifies the presence of mechanical ventilation through tracheostomy
or ventilator support. Mechanical ventilation carries many potential complications that lead to
hospitalization, including lung injury, infection and respiratory distress syndromes. For more information,
see Mechanical Ventilation Marker on page 92.
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Compassionate Care Allowances (CAL-SSA)
The CAL-SSA marker identifies the presence of a compassionate allowance condition defined by the U.S.
Social Security Administration as an impairment that invariably meets disability standards. This marker is
useful for measuring hospitalization risk, because hospitalization rates for patients with disabilities are high.
For more information, see Compassionate Care Allowances (CAL-SSA) on page 58.
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. For more information, see Emergency Visit Count
on page 90.
Inpatient Hospitalization Count
The inpatient hospitalization count is a 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. For more information, see Inpatient Hospitalization Count on page 88.
Inpatient Hospitalization Days Count
The inpatient hospitalization days count is a count of inpatient days associated with each inpatient
confinement in the inpatient hospitalization count. This count is a proxy for the intensity of inpatient care,
and useful in the context of assessing readmission risk. For more information, see Inpatient Hospitalization
Days on page 89.
Unplanned Readmission 30 Day Count
The unplanned 30 day readmission count is the number of unplanned acute care inpatient
hospitalizations within 30 days of a previous inpatient hospitalization. For more information, see
Unplanned Readmission 30 Day Count on page 89.
This count excludes any planned readmission based on inpatient procedures that are likely to have been
planned in advance unless there is contradicting diagnosis of an acute condition or complication of care.
Outpatient Visit Count
The outpatient visit count is a count of outpatient encounters in the physician office and selected
outpatient facilities. Visits are counted a maximum of once per date of service and provider. Higher rates
of outpatient encounters are associated with higher rates of hospitalization. For more information, see
Outpatient Visit Count on page 90.
Likelihood of Hospitalization
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.
Probability scores indicating the likelihood of a future hospitalization event are generated. A discussion of
the model outputs follows.
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Probability IP Hospitalization Score
The 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 was used for the ACG model development and validation (see
Reference Population on page 107). The database includes commercial and Medicare managed care plans.
Data for the period 2010-2011 were used for model development, and data for the period 2009-2010
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 nearly half a million Medicare-eligible (a U.S. government program
that pays for healthcare expenses for people for people aged 65 and older) beneficiaries aged 65 or older.
Sensitivity and Positive Predictive Value
Model performance is measured by how well true cases of admission are prospectively identified and
false positives are avoided. The focus is on two key indicators, sensitivity and positive predictive value:
• Sensitivity = True Cases Identified/True Cases in Population
• Positive Predictive Value (PPV) = True Cases Identified/(True Cases Identified + False Positives)
Sensitivity measures how well the model predicts which individuals are hospitalized. Positive predictive
value measures the likelihood that a particular patient is hospitalized in the next time period.
The following table 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 identify cases.
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PPV and Sensitivity for Predictive Hospitalization Models
Predictive Model
Positive Predictive Value
Sensitivity
IP Hospitalization with prior cost and diagnosis
and pharmacy data input
33.3%
21.2%
IP Hospitalization with prior cost and diagnosis
data input
32.6%
20.8%
Prior cost only11
22.4%
14.2%
C-Statistic
The C-Statistic is a measure of model fit. A C-Statistic of 0.5 indicates that true cases are indistinguishable
from false positives. A C-Statistic of 0.7 is widely accepted as a threshold for good model performance.
C-Statistics for models that predict hospitalization are summarized in the following table. 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.
C-Statistics for Predictive Hospitalization Models
Predictive Model
Persons with Prior
Hospitalization
Persons Aged less than 55
without Prior
Hospitalization
Persons Aged 55 or older
without Prior
Hospitalization
IP Hospitalization
.751
.741
.718
IP Hospitalization Six
Months
.754
.747
.728
ICU Hospitalization
.805
.757
.754
Injury Hospitalization
.808
.668
.748
Extended Hospitalization
.842
.721
.793
The C-Statistics for predicting hospitalization are above 0.7 for most outcomes and groups. Plausible
patterns emerge from the empiric validation. First, ACG System models better predict hospitalization
events for persons who had a hospitalization within the previous 12 months, compared to persons who
did not. Second, the ACG System better predicts specialized hospitalization events, compared to general
hospitalization events. This finding validates the fact that optimal performance of an ACG System
predictive model occurs when the weighting of risk factors employs the outcome that is being predicted.
Likelihood of Unplanned Readmission within 30 Days
Hospital financing increasingly considers the efficiency and quality of inpatient care. One area of interest in
this context is the rate at which patients are readmitted within a short period after previously having been
discharged. Higher than expected 30 day readmission rates may be perceived as a signal of lower quality
of care.
11 The prior cost only model serves as a benchmark to the IP Hospitalization model.
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Chapter 8: Predictive Models for Hospitalization
A tool for identifying individuals at risk for readmission is valuable, because it enables a prioritization of
discharge interventions among patients who are leaving a hospital. Planned admissions may entail a
comprehensive discharge program that includes patient education, medication reconciliation, scheduled
outpatient visits, and healthcare worker outreach or monitoring. These interventions are intended to
reduce readmission risk.
The unplanned 30 day readmission model is calibrated so that scores are available at the time of
admission to the hospital. Patients are not required to have a prior hospitalization in order to receive a
risk score for unplanned readmission. This enables healthcare delivery systems to anticipate discharge
planning needs for patients at risk of readmission in the event of an admission.
Probability Unplanned 30 Day Readmission Score
The probability unplanned 30 day readmission score is the probability score for an acute care inpatient
hospital readmission within that time frame in the event of an admission.
Empiric Validation of the Readmission Model
The C-Statistics for predicting 30 day unplanned readmission was .735 for the predictive model with
diagnosis and pharmacy data input.
The following table illustrates, using sensitivity and positive predictive value measures, the relationship
between identifying larger groups of patients at risk of readmission with high sensitivity, and smaller
groups of patients with improved positive predictive values.
Patients at Risk of Unplanned 30 Day Readmission
Sensitivity
Positive Predictive Value
Top 20% Scorers in Population
52%
16%
Top 10%
39%
24%
Top 5%
29%
36%
Top 2%
15%
49%
Top 1%
8%
53%
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Appendix A: Variables Necessary to Locally Calibrate the ACG® System Risk Models
Appendix A:
Variables Necessary to Locally Calibrate the
ACG® System Risk Models
Introduction
These tables indicate the variables used in the ACG System standard models. The ACG System standard
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 System standard 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, 2011, and 2014. All Rights Reserved.
Individual ACG Categories Included in the Risk Models
ACG
Description
0400*
Acute Major
0500*
Likely to Recur, w/o Allergies
0600*
Likely to Recur, w/ Allergies
4220
4-5 Other ADG Combinations, Age 1-17, 1+ Major ADGs
4330
4-5 Other ADG Combinations, Age 18-44, 2+ Major ADGs
4420
4-5 Other ADG Combinations, Age >44, 1 Major ADGs
4430
4-5 Other ADG Combinations, Age >44, 2+ Major ADGs
4510
6-9 Other ADG Combinations, Age 1-5, No Major ADGs
4520
6-9 Other ADG Combinations, Age 1-5, 1+ Major ADGs
4610
6-9 Other ADG Combinations, Age 6-17, No Major ADGs
4620
6-9 Other ADG Combinations, Age 6-17, 1+ Major ADGs
4730
6-9 Other ADG Combinations, Male, Age 18-34, 2+ Major ADGs
4830
6-9 Other ADG Combinations, Female, Age 18-34, 2+ Major ADGs
4910
6-9 Other ADG Combinations, Age >34, 0-1 Major ADGs
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Appendix A: Variables Necessary to Locally Calibrate the ACG® System Risk Models
ACG
Description
4920
6-9 Other ADG Combinations, Age >34, 2 Major ADGs
4930
6-9 Other ADG Combinations, Age >34, 3 Major ADGs
4940
6-9 Other ADG Combinations, Age >34, 4+ Major ADGs
5010
10+ Other ADG Combinations, Age 1-17 No Major ADGs
5020
10+ Other ADG Combinations, Age 1-17, 1 Major ADGs
5030
10+ Other ADG Combinations, Age 1-17, 2+ Major ADGs
5040
10+ Other ADG Combinations, Age 18+, 0-1 Major ADGs
5050
10+ Other ADG Combinations, Age 18+, 2 Major ADGs
5060
10+ Other ADG Combinations, Age 18+, 3 Major ADGs
5070
10+ Other ADG Combinations, Age 18+, 4+ Major ADGs
5320
Infants: 0-5 ADGs, 1+ Major ADGs
5321
Infants: 0-5 ADGs, 1+ Major ADGs, low birth weight
5322
Infants: 0-5 ADGs, 1+ Major ADGs, normal birth weight
5330
Infants: 6+ ADGs, No Major ADGs
5331
Infants: 6+ ADGs, No Major ADGs, low birth weight
5332
Infants: 6+ ADGs, No Major ADGs, normal birth weight
5340
Infants: 6+ ADGs, 1+ Major ADG
5341
Infants: 6+ ADGs, 1+ Major ADG, low birth weight
5342
Infants: 6+ ADGs, 1+ Major ADG, normal birth weight
* - This subset of ACG categories is used in the concurrent cost models only.
ACGs Included in Three Resource Groups
ACG Resource
Groups
Prospective Group
Concurrent Group
ACG RUB Level 1
(included in
Intercept)
ACG 0100 0200 0300 1100 1200 1600 5100
5110 5200 9900
ACG 0100 0200 0300 1100 1200 1600 5100
5110 5200 9900
ACG RUB Level 2
ACG 0400 0500 0600 0900 1000 1300 1711 ACG 0900 1000 1300 1711 1721 1731 1741
1721 1731 1741 1800 1900 2000 2100 2200 1800 1900 2000 2100 2200 2300 2400 2500
2300 2400 2500 2800 2900 3000 3100 3400 2800 2900 3000 3100 3400 3900 4000
3900 4000
ACG RUB Level 3
ACG 0700 0800 1400 1500 1751 1761 1771
2600 2700 3200 3300 3500 3600 3700 3800
4100 4210 4310 4320 4410 4710 4720 4810
4820 5310 5311 5312
ACG 0700 0800 1400 1500 1751 1761 1771
2600 2700 3200 3300 3500 3600 3700 3800
4100 4210 4310 4320 4410 4710 4720 4810
4820 5310, 5311, 5312
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Appendix A: Variables Necessary to Locally Calibrate the ACG® System Risk Models
Pregnancy Without Delivery
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 (EDC) FRE14 and to include pregnancies subsequent to a delivery.
Delivered
All delivered pregnancy related ACGs (1711, 1721, 1731, 1741, 1751, 1761 and 1771. This marker is used
in concurrent cost models only.
EDCs Included in the Risk Models
EDC
Description
ADM02*
Surgical Aftercare
ADM03
Transplant Status
ADM05*
Administrative concerns and non-specific laboratory abnormalities
ALL04
Asthma, w/o status asthmaticus
ALL05
Asthma, with status asthmaticus
ALL06
Disorders of the Immune System
CAR01*
Cardiovascular Signs and Symptoms
CAR03
Ischemic heart disease (excluding acute myocardial infarction)
CAR04
Congenital heart disease
CAR05
Congestive heart failure
CAR06
Cardiac valve disorders
CAR07
Cardiomyopathy
CAR08*
Heart Murmur
CAR09
Cardiac arrhythmia
CAR10
Generalized atherosclerosis
CAR12
Acute Myocardial Infarction
CAR13
Cardiac arrest, shock
CAR14
Hypertension, w/o major complications
CAR15
Hypertension, with major complications
DEN04*
Stomatitis
END02
Osteoporosis
END03*
Short Stature
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Appendix A: Variables Necessary to Locally Calibrate the ACG® System Risk Models
EDC
Description
END06
Type 2 Diabetes, w/o Complication
END07
Type 2 Diabetes, w/ Complication
END08
Type 1 Diabetes, w/o Complication
END09
Type 1 Diabetes, w/ Complication
EYE03
Retinal disorders (excluding diabetic retinopathy)
EYE08*
Glaucoma
EYE11*
Strabismus, amblyopia
EYE12*
Traumatic injuries of eye
EYE13
Diabetic Retinopathy
EYE15
Age-related Macular Degeneration
FRE03
Endometriosis
FRE05
Female Infertility
FRE12
Utero-vaginal prolapse
GAS02
Inflammatory bowel disease
GAS04
Acute hepatitis
GAS05
Chronic liver disease
GAS06
Peptic ulcer disease
GAS09*
Irritable Bowel Syndrome
GAS10
Diverticular disease of colon
GAS11
Acute pancreatitis
GAS12
Chronic pancreatitis
GSI02*
Chest Pain
GSI04*
Syncope
GSI06*
Debility and undue fatigue
GSI07*
Lymphadenopathy
GSI08
Edema
GSU02*
Appendicitis
GSU03*
Benign and unspecified neoplasm
GSU04*
Cholelithiasis, cholecystitis
GSU05*
External abdominal hernias, hydroceles
GSU08*
Varicose veins of lower extremities
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Appendix A: Variables Necessary to Locally Calibrate the ACG® System Risk Models
EDC
Description
GSU09*
Nonfungal infections of skin and subcutaneous tissue
GSU10*
Abdominal pain
GSU11
Peripheral vascular disease
GSU13
Aortic aneurysm
GSU14
Gastrointestinal Obstruction/Perforation
GSU15*
Alimentary or excretory surgical openings
GTC01
Chromosomal anomalies
GTC02*
Inherited metabolic disorders
GUR04
Prostatic hypertrophy
GUR09*
Renal calculi
HEM01
Other hemolytic anemias
HEM02*
Iron deficiency, other anemias
HEM03
Thrombophlebitis
HEM05
Aplastic anemia
HEM06
Deep vein thrombosis
HEM07
Hemophilia, coagulation Disorder
HEM09
Sickle Cell Disease
INF01*
Tuberculosis infection
INF04
HIV, AIDS
INF07*
Lyme disease
INF08
Septicemia
MAL01*
Malignant neoplasms of the skin
MAL02
Low impact malignant neoplasms
MAL03
High impact malignant neoplasms
MAL04
Malignant neoplasms, breast
MAL05*
Malignant neoplasms, cervix, uterus
MAL06
Malignant neoplasms, ovary
MAL07
Malignant neoplasms, esophagus
MAL08
Malignant neoplasms, kidney
MAL09
Malignant neoplasms, liver and biliary tract
MAL10
Malignant neoplasms, lung
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Appendix A: Variables Necessary to Locally Calibrate the ACG® System Risk Models
EDC
Description
MAL11
Malignant neoplasms, lymphomas
MAL12
Malignant neoplasms, colorectal
MAL13
Malignant neoplasms, pancreas
MAL14
Malignant neoplasms, prostate
MAL15
Malignant neoplasms, stomach
MAL16
Acute Leukemia
MAL18
Malignant neoplasms, bladder
MUS03
Degenerative joint disease
MUS06*
Kyphoscoliosis
MUS09*
Joint disorders, trauma related
MUS10
Fracture of neck of femur (hip)
MUS11*
Congenital anomalies of limbs, hands and feet
MUS13*
Cervical pain syndromes
MUS14
Low back pain
MUS16
Amputation Status
NUR03
Peripheral neuropathy, neuritis
NUR05
Cerebrovascular disease
NUR06
Parkinson's disease
NUR07
Seizure disorder
NUR08
Multiple sclerosis
NUR09
Muscular dystrophy
NUR11
Dementia and delirium
NUR12
Quadriplegia and Paraplegia
NUR15
Head Injury
NUR16
Spinal Cord Injury/Disorders
NUR17
Paralytic Syndromes, Other
NUR18
Cerebral Palsy
NUR19
Developmental disorder
NUR20*
Central nervous system infections
NUR22*
Migraines
NUR23
Organic Brain Syndrome
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Appendix A: Variables Necessary to Locally Calibrate the ACG® System Risk Models
EDC
Description
NUR24
Dementia
NUR25
Delirium
NUR26
Autism Spectrum Disorders
NUT02
Nutritional deficiencies
PSY01
Anxiety, neuroses
PSY02
Substance use
PSY03
Tobacco abuse
PSY05
Attention deficit disorder
PSY07
Schizophrenia and affective psychosis
PSY08
Personality disorders
PSY09
Depression
PSY10*
Psychologic signs and symptoms
PSY12
Bipolar Disorder
PSY13
Adjustment disorder
PSY14
Psychosocial disorders of childhood
PSY15
Eating disorder
PSY16
Impulse control
PSY17
Psycho-physiologic and somatoform disorders
PSY18
Psychosexual
PSY19
Sleep disorders of nonorganic origin
PSY20
Major depression
REC01
Cleft lip and palate
REC03
Chronic ulcer of the skin
REC04
Burns--2nd and 3rd degree
REN01
Chronic renal failure
REN02
Fluid/electrolyte disturbances
REN03
Acute renal failure
REN04
Nephritis/Nephrosis
REN06
End stage renal disease
RES02
Acute lower respiratory tract infection
RES03
Cystic fibrosis
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Appendix A: Variables Necessary to Locally Calibrate the ACG® System Risk Models
EDC
Description
RES04
Emphysema, chronic bronchitis, COPD
RES06*
Sleep apnea
RES08
Pulmonary embolism
RES09
Tracheostomy
RES12
Acute respiratory failure
RES13
Chronic respiratory failure
RES14
Aspiration and bacterial pneumonias
RHU01
Autoimmune and connective tissue diseases
RHU02*
Gout
RHU03*
Arthropathy
RHU05
Rheumatoid Arthritis
SKN18*
Benign neoplasm of skin and subcutaneous tissues
TOX02
Adverse effects of medicinal agents
TOX03*
Adverse effects from medical/surgical procedures
TOX04
Complications of Mechanical Devices
* - This subset of EDC categories is used in the concurrent cost models only.
Rx-Defined Morbidity Groups™ (Rx-MGs) Included in the
Risk 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
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Appendix A: Variables Necessary to Locally Calibrate the ACG® System Risk Models
Rx-MG
Description
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
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
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Appendix A: Variables Necessary to Locally Calibrate the ACG® System Risk Models
Rx-MG
Description
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
PSYx070
Psychosocial/Sleep Disorders
PSYx080
Psychosocial/Tobacco Cessation
PSYx090
Psychosocial/Bipolar Disorder
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
Special Population Markers
Special Population Marker
Use in the Predictive Model
HOSDOM0 (included in Intercept)
Boolean indicator for 0 HOSDOM
HOSDOM1
Boolean indicator for 1 HOSDOM indicator
HOSDOM2
Boolean indicator for 2 HOSDOM indicators
HOSDOM3+
Boolean indicator for 3 or more HOSDOM indicators
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Appendix A: Variables Necessary to Locally Calibrate the ACG® System Risk Models
Special Population Marker
Use in the Predictive Model
Frailty Marker
Boolean indicator
CAL-SSA*
Boolean indicator
Active Ingredient Count
Boolean indicator for 14 or more Active Ingredients
* - CAL-SSA is used in the Hospitalization Prediction Models only.
Demographic Markers
Demographic Marker
Use in the Predictive Model
Ten age groups
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+
Female
Boolean indicator
Optional Prior Cost Markers
Prior Cost Markers*
• Total Expense, Non-users (Included in the Intercept)
• Total Expense, 1-10th Percentile
• Total Expense, 11-25th Percentile
• Total Expense, 26-50th Percentile
• Total Expense, 51-75th Percentile
• Total Expense, 76-91st Percentile
• Total Expense, 91-93rd Percentile
• Total Expense, 94-95th Percentile
• Total Expense, 96-97th Percentile
• Total Expense, 98-99th Percentile
• Pharmacy Expense, Non-users (Included in the Intercept)
• Pharmacy Expense, 1-10th Percentile
• Pharmacy Expense, 11-25th Percentile
• Pharmacy Expense, 26-50th Percentile
• Pharmacy Expense, 51-75th Percentile
• Pharmacy Expense, 76-91st Percentile
• Pharmacy Expense, 91-93rd Percentile
• Pharmacy Expense, 94-95th Percentile
• Pharmacy Expense, 96-97th Percentile
• Pharmacy Expense, 98-99th 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.
© 2014 The Johns Hopkins University. All rights reserved.
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Appendix A: Variables Necessary to Locally Calibrate the ACG® System Risk Models
Utilization Markers
Note: These are used only in the Hospitalization and 30 Day Unplanned Readmission Prediction Models.
•
•
•
•
•
•
•
•
•
Dialysis Service
Nursing Service
Major Procedure
Psychotherapy
Active Cancer Treatment
Active Cancer Treatment
Inpatient Hospitalization Count
Emergency Visit Count
Outpatient Visit Count
Prior Hospitalization Markers
Note: These are used only in the 30 Day Unplanned Readmission Prediction Models.
• Inpatient Hospitalization Days
• Unplanned 30 day readmission count
© 2014 The Johns Hopkins University. All rights reserved.
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Appendix B: Acknowledgements
Appendix B:
Acknowledgements
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., and Klaus Lemke, Ph.D.
Additional production assistance and original content provided by Lisa Kabasakalian, Meg McGinn, Robert
Palmer, Amy Salls, and Joseph Steuer 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.
Support
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.
Such communication is encouraged.
Email: mailto:jhsph.askacg@jhu.edu
Website: http://acg.jhsph.edu
Third-Party Library Acknowledgements
This product includes software developed by the following companies:
This product includes software developed by Health + Technologies, Inc.
Copyright © 2003-2014 Health + Technologies, Inc. All rights reserved.
http://www.healthplustechnologies.com
This product includes software developed by SafeNet, Inc.
Copyright © 2010 SafeNet, Inc. All rights reserved.
http://www.safenet-inc.com
This product includes the Java Runtime Environment from Oracle
Copyright © 2014 Oracle America, Inc. All rights reserved.
http://www.oracle.com/java/
The license for the Java Runtime is contained in the LICENSE file in the jre directory of this
software.
REQUIRED NOTICE REGARDING COMMERCIAL FEATURES IN JAVA
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The Johns Hopkins ACG® System Version 11.0 Technical Reference Guide
Appendix B: Acknowledgements
Use of the Commercial Features for any commercial or production purpose requires a
separate license from Oracle. “Commercial Features” means those features identified
Table 1-1 (Commercial Features In Java SE Product Editions) of the Java SE documentation
accessible at http://www.oracle.com/technetwork/java/javase/documentation/index.html
This product includes the following open source:
• This product includes software developed by The Apache Software Foundation
Copyright © 2001-2014 The Apache Software Foundation
http://www.apache.org
• This product includes software developed by the JDOM Project
Copyright © 2000-2012 Jason Hunter & Brett McLaughlin.
http://www.jdom.org
• This product includes the open source iText library
Copyright 1999, 2000, 2001, 2002 by Bruno Lowagie.
http://www.lowagie.com/iText
with modifications made by JasperReports
Copyright © 2014 JasperSoft Corporation, Inc.
http://www.jasperforge.org
The license for these libraries is contained in the ITEXT_LICENSE.txt file distributed in the
lib directory of this software.
• This product includes the open source JasperReports library
Copyright © 2014 JasperSoft Corporation, Inc.
http://www.jasperforge.org
The license for these libraries is contained in the JASPERREPORTS_LICENSE.txt file
distributed in the lib directory of this software.
• This product includes the open source JFreeChart and JCommon libraries
Copyright © 2000-2014, by Object Refinery Limited and Contributors.
http://jfree.org
The license for these libraries is contained in the JFREECHART_LICENSE.txt file distributed in
the lib directory of this software.
• This product includes the open source JIDE Common Layer library
Copyright © 2002-2013 JIDE Software, Inc, all rights reserved.
http://www.jidesoft.com
• This product includes JGoodies open source libraries
Copyright © 2002-2006 JGoodies Karsten Lentzsch. All rights reserved.
http://www.jgoodies.com
• This product includes the open source DejaVu Fonts distributed by JasperReports Copyright © 2003 by
Bitstream, Inc. All Rights Reserved. Bitstream Vera is a trademark of Bitstream, Copyright © 2006 by
Tavmjong Bah. All Rights Reserved.
http://www.dejavu-fonts.org/
The license for these fonts are contained in the JASPER_DEJAVU_FONTS_LICENSE.txt file distributed in
the lib directory of this software.
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The Johns Hopkins ACG® System Version 11.0 Technical Reference Guide
Index
Index
A
ACG
categories 129
chronic illness examples 29
clinical classification of pregnancy/delivery 33
clinically-oriented examples 29, 32
decision tree 21
delivery 33
diabetes example 31
hospitalization prediction 123
hypertension example 29
infants clinical classification 35
infants examples 34
predictive model variables 129
pregnancy 33
pregnancy/delivery with complications example 33
RUB categories 36
RUB levels 130
terminal groups formation 21
active cancer treatment utilization marker 124
active ingredient count variable 66
ADG
clusters 18
collapsed 18, 20
diagnostic certainty 14
duration 14
EDC differences 48
etiology 14
expected need for specialty care 14
major 16
severity 14
subgroups 18
applications 17
B
birth weight 59
C
CADG
collapsed 18
combinations 19
MAC assignments 20
CAL-SSA 58
cancer 91
care coordination
assessing 94
generalist seen 101
majority source of care 96
management visit count 96
risk of poor coordination 101
specialty count 99
unique provider count 98
care density 104
categorical variables 6
chronic condition count marker
distribution 54
EDCs considered 50
overview 49
chronic illness
diabetes example 31
examples 29
hypertension example 29
clinical aspects
duration 13
etiology 13
expected need for specialiy care 14
severity 13
clinically-oriented pregnancy example 32
collapsed ADG 18
common ACG system applications 6
compassionate allowances condition utilization
marker 125
condition markers
criteria 67
definitions 68
continuous single-interval measure of medication
availability 82
coordination markers for population identification 94
cost models
ACG concurrent risk 111
outcomes 106
CSA 82
c-statistic 127
D
decision tree
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Index
ACG 21
MAC-12 pregnant women 23
MAC-24 multiple ADG categories 27
MAC-26 infants 25
delivered 58
delivered pregnancy variables 131
delivery ACGs 33
demographic markers 139
diagnostic certainty 14, 47
dialysis service utilization marker 124
duration 13, 14
E
EDC
ADG differences 48
applications 49
chronic condition count marker 50
development 41
diabetes complications examples 46
diagnostic certainty 47
MEDC types 47
otitis media ICD-9-CM codes 42
otitis media ICD-10 codes 44
use of 42
variables 131
eitology 14
emergency visit counts utilization marker 125
empiric validation
c-statistic 127
likelihood of hospitlization model 126
positive predictive value 126
readmission model 128
sensitivity 126
end-of-period possession 85
etiology 13
expanded diagnostic clusters 41
expected need for speciality care 14
expected need for specialty care 14
F
frailty conditions markers 56
G
generalist seen 101
GS 101
H
hospital dominant conditions markers 55
hospitalization 123
hospitalization likelihood
model outputs 125, 127
probability extended hospitalization score 126
probability ICU hospitalization score 126
probability injury hospitalization score 126
probability IP hospitalization score 126
probability IP hospitalization six months score 126
probability unplanned 30 day readmission
score 128
I
ICD-9-CM otitis media EDC 42
ICD-10 otitis media EDC 44
ICD mapping to ADG group 10
impact conditions 66
impact EDCs 48
infants
ACG 35
examples 34
infants decision tree 25
inpatient days count utilization marker 125
inpatient hospitalization count utilization marker 125
input files 6
L
local calibration of PM scores 122
low birth weight 59
M
MAC
assigned collapsed ADG 20
combinations 19
MAC-12 23
MAC-24 27
MAC-26 25
major ADG 16
majority source of care 96
management visit count 96
mapping ICD to ADG set 10
markers
demographic 139
optional prior cost 139
special population 138
utilization 140
mechanical ventilation 92
mechanical ventilation utilization marker 124
MEDC types 47
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Index
medication-defined morbidity
active ingredient count variable 66
NDC-to-Rx-MG assignment methodology 65
overview 60
Rx-MG morbidity taxonomy 62
medication possession ratio 81
morbidity groups
Rx-defined 60
MPR 81
MSCO 96
multiple ADG categories decision tree 27
N
NDC-to-Rx-MG assignment methodology 65
non-delivery pregnancy variables 131
nursing service utilization marker 124
O
optional prior cost markers 139
outpatient visit count utilization marker 125
P
PDC 83
pharmacy adherence
continuous single-interval measure of medication
availability 82
defined 75
effective care significance 73
end of period possession 85
medication possession ratio 81
possession/adherence marker development 74
proportion of days covered 83
reporting of possession 85
testing 86
validation 86
pharmacy cost 117
pharmacy cost risk 116
population markers
chronic condition count 49
condition 67
frailty conditions 56
hospital dominant conditions 55
pregnancy without delivery 59
populations
assign dollar value 112
rescaling 112
positive predictive value 126
predicting hospitalization
ACG use 123
empiric validation 126, 128
overview 123
utilization markers 124
predictive modeling
ACG categories 129
conceptual bassis 106
EDC variables 131
elements of 106
impact conditions 66
impact EDCs 48
positive predictive value 119
ratios 119
resource bands 93
r-squared 114, 118
Rx-defined morbidity groups 136
sensitivity 119
statistical approach 110
variables 129
pregnancy
ACGs 33
with complications examples 33
without delivery markers 59
pregnant 58
pregnant women decision tree 23
Probability
extended hospitalization score 126
ICU hospitalization score 126
injury hospitalization score 126
IP hospitalization score 126
IP hospitalization six months score 126
persistent high user 121
unexpected pharmacy cost 120
unplanned 30 day readmission score 128
proportion of days covered 83
prospective cost models 116
psychotherapy 92
psychotherapy utilization marker 124
R
rank probability 117
reference population 107
reference probability 117
reporting of possession 85
rescaling 112
resource bands 93
resource utilization bands 35
risk
concurrent 113
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The Johns Hopkins ACG® System Version 11.0 Technical Reference Guide
Index
risk adjustment pyramid 6
risk factors 107
risk of poor coordination 101
RUB
ACG categories 36
ACG variables 130
overview 35
Rx-defined morbidity groups 60
Rx-defined morbidity group variables 136
Rx-MG
clinical basis 60
morbidity taxonomy 62
S
SC 99
sensitivity 126
severity 13, 14
special population markers 138
specialty count 99
statistical performance
positive predictive value 119
predictive ratios 119
r-squared 114, 118
sensitivity 119
T
terminal groups 21
total risk 116
typology 123
U
unique provider count 98
unplanned readmissions within 30 days count
utilization marker 125
utilization markers
active cancer treatment 124
compassionate allowances condition 125
dialysis service 124
emergency visit count 125
inpatient days count 125
inpatient hospitalization count 125
list of 140
mechanical ventilation 124
nursing service 124
outpatient visit count 125
psychotherapy 124
unplanned readmissions within 30 days count 125
use of 124
V
variables
ACG categories 129
ACG RUB levels 130
delivered pregnancy 131
demographic markers 139
EDC 131
non-delivery pregnancy 131
optional prior cost markers 139
predictive model 129
Rx-defined morbidity groups 136
special population markers 138
utilization markers 140
W
weights 36
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