Copyright 2003, Johns Hopkins University, 10/19/2003 Medicare Risk Adjustment Development by Johns...
-
Upload
anissa-ramsey -
Category
Documents
-
view
220 -
download
0
Transcript of Copyright 2003, Johns Hopkins University, 10/19/2003 Medicare Risk Adjustment Development by Johns...
Copyright 2003, Johns Hopkins University, 10/19/2003
Medicare Risk Adjustment Development by Johns Hopkins
Chad Abrams, MA [email protected]
Johns Hopkins UniversitySchool of Hygiene and Public Health624 N Broadway #600Baltimore, Maryland 21205
June 6, 2004 San Diego CA
Copyright 2004, Johns Hopkins University, 5/20
2
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
Copyright 2004, Johns Hopkins University, 5/20
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
Copyright 2004, Johns Hopkins University, 5/20
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
Copyright 2004, Johns Hopkins University, 5/20
6
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.
Copyright 2004, Johns Hopkins University, 5/20
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
Copyright 2004, Johns Hopkins University, 5/20
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
Copyright 2004, Johns Hopkins University, 5/20
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)
Copyright 2004, Johns Hopkins University, 5/20
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%
Copyright 2004, Johns Hopkins University, 5/20
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
Copyright 2003, Johns Hopkins University, 10/19/2003
Results: What Have We Learned?
Copyright 2004, Johns Hopkins University, 5/20
13
1) The frailty variable increases explanatory power AND provides greater predictiveaccuracy
Data Source: 1996-97 Medicare 5 Percent Sample
Copyright 2004, Johns Hopkins University, 5/20
14
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
Copyright 2004, Johns Hopkins University, 5/20
15
3) Sometimes the kitchen-sink approach works
Data Source: 1996-97 Medicare 5 Percent Sample
Copyright 2004, Johns Hopkins University, 5/20
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
Copyright 2004, Johns Hopkins University, 5/20
17
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
Copyright 2004, Johns Hopkins University, 5/20
18
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
Copyright 2004, Johns Hopkins University, 5/20
19
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