The development and testing of risk adjusters using Medicare inpatient and ambulatory data

Published: June 6, 1996
Category: Reports
Authors: Anderson G, Coleman K, Dobson A, Maxwell S, Starfield B, Weiner J
Country: United States
Language: null
Type: Finance/Budgeting
Setting: Health Plan

Final report for Health Care Financing Administration contract 93-026/EE. Falls Church, VA, USA: Lewin Group.

Lewin Group, Falls Church, VA, USA

The Health Care Financing Administration (HCFA) is continuing to support research to develop risk adjuster models that combine demographic and clinical data to predict the expenditures of Medicare enrollees in order that payments to capitated plans might better reflect underlying enrollee disease burden. This project is one of the two that HCFA has supported that developed and then evaluated new risk adjuster models for the Medicare population using newly available ambulatory diagnosis codes found within Medicare administrative claims data. For this project, researchers at Johns Hopkins University (JHU) developed two different risk adjuster models that their colleagues at the Lewin Group then evaluated. A team of researchers from Boston University (BU) and the Center for Health Economics Research (CHER) conducted a second, parallel project to design and evaluate other risk adjuster models for the Medicare population.
The organization of this Executive Summary and the accompanying report echoes the organizational structure of the Lewin/JHU project. The two primary activities of this project – model development and model evaluation – were divided between The Lewin Group and JHU. JHU designed the two risk adjuster models for this project, while The Lewin Group conducted the evaluation of these models. This organization of tasks minimized interaction between model development and model evaluation. The Executive Summary opens with a brief Background and Overview section. Next, the development of the two new JHU risk adjuster models designed for this project is discussed. These two models are then evaluated from two different perspectives – their predictive accuracy; and the feasibility of using these models as the foundation of a new capitated payment system for Medicare enrollees. The Executive Summary ends with a conclusions section.

Risk Bearing Entities,Population Markers,Financial,Capitation,United States
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