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.

KEYWORDS:

• Predictive modeling
• hypertension
• disease management programs
• risk assessment
• hospitalization

Acknowledgments

I would like to thank Mrs. Alina Richter, Team Leader of the Health Management Department of the private health insurance Central, for providing and reviewing the routine data and the company Generali Health Solutions (GHS) for the valuable contributions in the implementation of this project.

Author contributions

AS and DM contributed to the conception or design of the work. PL and SK contributed to the acquisition, analysis, and interpretation of data for the work. PL drafted the manuscript. SS critically revised the manuscript. All gave final approval and agree to be accountable for all aspects of work ensuring integrity and accuracy.

Declaration of interest

The corresponding author had full access to all of the data in this study and takes complete responsibility for the integrity of the data and the accuracy of the data analysis. The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

Reviewers disclosure

Peer reviewers on this manuscript have no relevant financial relationships or otherwise to disclose.

Ethical approval statement

Ethical approval was not required for this study.

Supplementary material

Supplemental data for this article can be accessed here.

Additional information

Funding

This paper was not funded.