Case selection for a Medicaid chronic care management program

Published: September 1, 2008
Category: Bibliography > Papers
Authors: Aweh G, Clark RE, Weir S
Countries: United States
Language: null
Types: Care Management
Settings: Academic

Health Care Financ Rev 30:61-74.

Center for Health Policy and Research, University of Massachusetts Medical School, Shrewsbury, MA, USA

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.

PMID: 19040174

Targeted Program,High-Impact Chronic Conditions,Predictive Risk Models,United States,Cost Control,Forecasting,Models,Theoretical,Quality of Health Care,Vermont,Vulnerable Population

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