Predictive modeling to identify potential participants of a disease management program hypertension. (4th March 2021)

• Record Type: Journal Article
• Authors: Lenti, Pamela; Kottmair, Stefan; Stock, Stephanie; Shukri, Arim; Müller, Dirk
• Is Part Of: Expert review of pharmacoeconomics & outcomes research. Volume 21:Number 2(2021)
• Page Start: 307
• Page End: 314
• Publication Date: 2021-03-04
• Subjects: Predictive modeling -- hypertension -- disease management programs -- risk assessment -- hospitalization; Pharmacology -- Economic aspects -- Periodicals; 338.47615105
• DOI: 10.1080/14737167.2020.1780919
• Languages: English
• ISSNs: 1473-7167

Abstract

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.