Copyright 2003, Johns Hopkins University, 10/19/2003 Medicare Risk Adjustment Development by Johns Hopkins Chad Abrams, MA Cabrams@jhsph.edu Johns Hopkins.

Copyright 2003, Johns Hopkins University, 10/19/2003

Medicare Risk Adjustment Development by Johns Hopkins

Chad Abrams, MA Cabrams@jhsph.edu

Johns Hopkins UniversitySchool of Hygiene and Public Health624 N Broadway #600Baltimore, Maryland 21205

June 6, 2004 San Diego CA

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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 ACG-Predictive Model is being refined for the elderly

Copyright 2004, Johns Hopkins University, 5/20

3Long 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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4Better Modeling or Better Data Quality?

Project: Year-1 diagnoses used to predict year-2 Medicare expenditures

Explanatory Power

Of JHU Model

Project 1: 1991-1992 5.5

Project 2: 1995-1996 8.4

Project 3: 1996-1997 9.1

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5Components of the Basic Model

Selected ADGs13 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 “setting-neutral” diagnosis codes.

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7Examples 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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8Impact of HOSDOM on Resource Consumption

Percent

Of Pop.

Year 2 Costs (relative weight)

0 HOSDOMs 90.7% 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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9Other 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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10Percent of Beneficiaries with Frail Clusters

Cluster Description

Percent of all Elderly

Percent of Dual-

Eligibles1 Malnutrition 0.08% 0.20%2 Dementia 0.82% 2.64%3 Impaired Vision 0.25% 0.65%4 Decubitus Ulcer 1.08% 3.05%5 Incontinence of Urine 0.04% 0.05%6 Loss of Weight 2.51% 4.40%7 Incontinence of Feces 0.15% 0.20%8 Obesity (morbid) 0.03% 0.07%9 Poverty 0.00% 0.01%

10 Barriers to Access of Care 0.02% 0.05%11 Difficulty in Walking 2.88% 4.37%

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11Impact 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 predictiveaccuracy

Data Source: 1996-97 Medicare 5 Percent Sample

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2) Be careful. Higher R2and 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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16Comparison 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

•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