Predictive modeling to identify potential participants of a disease management program hypertension.

BACKGROUND: Based on the premise of limited health-care resources, decision-makers pursue to allocate disease management programs (DMP) more targeted. METHODS: Based on routine data from a private health insurance company, a prediction model was developed to estimate the individual risk for future in-patient stays of patients eligible for a DMP Hypertension. The database included anonymous claims data of 38,284 policyholders with a diagnosis in the year 2013. A cutoff point of ≥70% was used for selecting candidates with a risk for future hospitalization. Using a logistic regression model, we estimated the model's prognostic power, the occurrence of clinical events, and the resource use. RESULTS: Overall, the final model shows acceptable prognostic power (detection rate = 64.3%; sensitivity = 68.7%; positive predictive value (PPV) = 64.1%, area under the curve (AUC) = 0.72). The comparison between the selected hypothetical DMP-group with a predicted (LOH) ≥70% showed additional costs of about 69% for the DMP-group compared to insure with a LOH <70%. CONCLUSION: The predictive analytical approach may identify potential DMP participants with a high risk of increased health services utilization and in-patient stays.