Medicare Risk Adjustment
Development by Johns
Hopkins
Chad Abrams, MA
Cabrams@jhsph.edu
Johns Hopkins University
School of Hygiene and Public Health
624 N Broadway #600
Baltimore, Maryland 21205
June 6, 2004
San Diego CA
Copyright 2003, Johns Hopkins University, 10/19/2003
Objectives
• To provide an overview of JHU’s work on
Medicare risk adjustment
• To summarize what we have learned
• To discuss recent findings and how the ACGPredictive Model is being refined for the elderly
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Long History of Working
with Medicare Data
Final Reports Delivered to Center for Medicare & Medicaid
Services (formerly HCFA)
1996
Risk-Adjusted Medicare Capitation Rates Using
Ambulatory and Inpatient Diagnoses
2000
Updating & Calibrating the Johns Hopkins
University ACG/ADG Risk Adjustment Method
for Application to Medicare Risk Contracting
2003
Development and Evaluation of the Johns
Hopkins Univeristy Risk Adjustment Models for
Medicare+Choice Plan Payment
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Better Modeling or
Better Data Quality?
4
Project: Year-1 diagnoses
used to predict year-2
Medicare expenditures
Project 1: 1991-1992
Explanatory Power
Of JHU Model
Project 2: 1995-1996
8.4
Project 3: 1996-1997
9.1
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5.5
Components of the
Basic Model
Selected ADGs
13 ADGs demonstrated to have a significant
impact on future resource use
Hospital Dominant Marker
A marker indicating high probability of a future
admission
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The HOSDOM Marker
• Persons with a HOSDOM diagnosis have
a high probability (usually greater than
50%) of being hospitalized in the
subsequent time period.
• Based on two-years of Medicare claims
data and careful clinical review
• A single concise list of 266 “settingneutral” diagnosis codes.
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Examples of HOSDOM
Diagnoses
491.21: Obstructive Chronic Bronchitis with Acute
Exacerbation
518.81: Acute Respiratory Failure
584.9: Acute Renal Failure, Unspecified
198.5: Secondary Malignant Neoplasm, Bone
785.4: Gangrene
518.4: Acute Lung Edema, Unspecified
789.5: Ascites
571.5: Cirrhosis of Liver without mention of alcohol
403.91: Hypertensive Heart Disease with Renal Failure
284.8: Aplastic Anemia
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Impact of HOSDOM on
Resource Consumption
0 HOSDOMs
Percent
Of Pop.
90.7%
8
Year 2 Costs
(relative weight)
0.82
1 HOSDOM
7.4%
2.45
2 HOSDOM
1.5%
4.25
3 HOSDOMs
0.3%
5.74
4 HOSDOMs
0.05%
6.59
5+ HOSDOMs
0.01%
7.86
Data Source: 1996-97 Medicare 5 Percent Sample
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Other Variables
Considered
• Frailty Marker
–A list of 75 codes that appear to
clinically describe frail
beneficiaries.
–Divided into 11 “clusters” each
representing a discrete condition
consistent with frailty.
• Selected Disease Conditions
–Johns Hopkins Expanded Diagnosis
Clusters (EDCs)
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Percent of Beneficiaries
with Frail Clusters
Cluster
Description
1
2
3
4
5
6
Malnutrition
Dementia
Impaired Vision
Decubitus Ulcer
Incontinence of Urine
Loss of Weight
7
8
9
10
11
Incontinence of Feces
Obesity (morbid)
Poverty
Barriers to Access of Care
Difficulty in Walking
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Percent of Percent of
all Elderly
DualEligibles
0.08%
0.20%
0.82%
2.64%
0.25%
0.65%
1.08%
3.05%
0.04%
0.05%
2.51%
4.40%
0.15%
0.03%
0.00%
0.02%
2.88%
0.20%
0.07%
0.01%
0.05%
4.37%
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Impact of Frail on
Resource Consumption
Number of
Frail
Clusters
Percent
of all
Elderly
Year 2 Costs
(relative
weight)
0
93.8%
0.9
1
5.7%
1.9
2
0.5%
3.0
3
0.04%
4.0
4
0.005%
3.5
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Results: What Have
We Learned?
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1) The frailty variable increases explanatory
power AND provides greater predictive
accuracy
Data Source: 1996-97 Medicare 5 Percent Sample
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2) Be careful. Higher R2
and improved accuracy for top quintiles may result
in substantial overpayment for first quintile.
Data Source: 1996-97 Medicare 5 Percent Sample
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3) Sometimes the kitchen-sink
approach works
Data Source: 1996-97 Medicare 5 Percent Sample
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Comparison to CMS
61-Disease Model and HCC
Data Source: 1996-97 Medicare 5 Percent Sample
*61-Disease Model the then “current” model as of Nov. 2001.
** HCC model results from Pope et all Dec 2000
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The Goal-Ideally, payment models should pay
appropriately for sick individuals while at
the same time removing or reducing
traditional incentives for promoting
biased selection
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How are we doing?
• Current technologies probably not adequate
• Re-insurance and/or carve-outs are still
necessary to assure adequate payment for
treating high cost patients
• R-squared is probably NOT the correct criteria for
evaluating model performance
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Conclusions
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• The type of variables included matters
• In general, disease specific markers
–do not provide adequate payment for the sick, and
–possibly lead to substantial overpayment for healthy
individuals
• Markers such as “hospital dominant” (likely to lead
to a hospitalization) and “frail-symptoms” (a proxy
for ADLs) successfully target the sick without falsely
identifying healthy
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