Analysis of predictive value of four risk models in medicaid recipients with chronic obstructive pulmonary disease in Texas

Abstract

Objective: The aim of this study was to assess the predictive value of 4 risk models when applied to Medicaid recipients with chronic obstructive pulmonary disease (COPD) in Texas. Methods: Four risk models were used to predict future medical costs: Diagnostic Cost Group (DCG) Inpatient + RxGroups Commercial, DCG All-Encounter + RxGroups Commercial, DCG All-Encounter + RxCost Medicaid, and prior cost. Diagnostic codes, National Drug Code categories, and 2001 total health care costs were input into the risk models. The study included Texas Medicaid recipients aged 40 to <65 years who had an inpatient diagnosis of COPD in 2000 and were continuously enrolled in Medicaid in 2000–2002. The risk models were used to predict total health care costs for 2002. Predicted costs were compared with actual 2002 costs using R 2, and predictions of high-cost cases were analyzed using the positive predictive value (PPV) of the top 5% of high-cost patients. Results: Information from 7967 patients was used in this analysis. The R 2 values were 0.11, 0.08, 0.05, and 0.04 with the DCG Inpatient + RxGroups Commercial, DCG All-Encounter + RxGroups Commercial, DCG All-Encounter + RxCost Medicaid, and prior cost models, respectively. In the top 5% of high-cost patients, the corresponding PPVs were 26% (105/399), 23% (90/399), 18% (72/399), and 28% (112/399). Conclusions: The DCG Inpatient + RxGroups Commercial model was the best predictor of future costs as measured using R 2. However, diagnosis-based risk-adjustment models may not be as useful as prior cost data when attempting to identify future high-cost cases among Texas Medicaid recipients with COPD. A combination of diagnosis-based risk adjustment models and prior cost data may prove to be the most effective way to use claims data to identify COPD candidates for case management.