Predictive modeling to identify potential participants of a disease management program hypertension
ABSTRACT 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.
Expert review of pharmacoeconomics outcomes research