Title

Case selection for a Medicaid chronic care management program

UMMS Affiliation

Department of Family Medicine and Community Health; Center for Health Policy and Research; Clinical and Population Health Research

Date

12-2-2008

Document Type

Article

Medical Subject Headings

Chronic Disease; Cost Control; *Disease Management; Forecasting; Humans; *Medicaid; Models, Theoretical; Quality of Health Care; United States; Vermont; Vulnerable Populations

Disciplines

Health Services Administration | Health Services Research | Public Health

Abstract

Medicaid agencies are beginning to turn to care management to reduce costs and improve health care quality. One challenge is selecting members at risk of costly, preventable service utilization. Using claims data from the State of Vermont, we compare the ability of three pre-existing health risk predictive models to predict the top 10 percent of members with chronic conditions: Chronic Illness and Disability Payment System (CDPS), Diagnostic Cost Groups (DCG), and Adjusted Clinical Groups Predictive Model (ACG-PM). We find that the ACG-PM model performs best. However, for predicting the very highest-cost members (e.g, the 99th percentile), the DCG model is preferred.

Rights and Permissions

Citation: Health Care Financ Rev. 2008 Fall;30(1):61-74.

Related Resources

Link to Article in PubMed