Data-Driven Allocation of Preventive Care with Application to Diabetes Mellitus Type II

Mathias Kraus (), Stefan Feuerriegel () and Maytal Saar-Tsechansky ()
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Mathias Kraus: Institute of Information Systems, Friedrich-Alexander-University Erlangen Nürnberg, 90403 Nürnberg, Germany
Stefan Feuerriegel: Institute of Artificial Intelligence in Management, Ludwig-Maximilian-University, 80539 Munich, Germany
Maytal Saar-Tsechansky: Department of Information, Risk and Operations Management, McCombs School of Business, University of Texas at Austin, Austin, Texas 78712

Manufacturing & Service Operations Management, 2024, vol. 26, issue 1, 137-153

Abstract: Problem definition : Increasing costs of healthcare highlight the importance of effective disease prevention. However, decision models for allocating preventive care are lacking. Methodology/results : In this paper, we develop a data-driven decision model for determining a cost-effective allocation of preventive treatments to patients at risk. Specifically, we combine counterfactual inference, machine learning, and optimization techniques to build a scalable decision model that can exploit high-dimensional medical data, such as the data found in modern electronic health records. Our decision model is evaluated based on electronic health records from 89,191 prediabetic patients. We compare the allocation of preventive treatments ( metformin ) prescribed by our data-driven decision model with that of current practice. We find that if our approach is applied to the U.S. population, it can yield annual savings of $1.1 billion. Finally, we analyze the cost-effectiveness under varying budget levels. Managerial implications : Our work supports decision making in health management, with the goal of achieving effective disease prevention at lower costs. Importantly, our decision model is generic and can thus be used for effective allocation of preventive care for other preventable diseases.

Keywords: preventive care; disease prevention; healthcare analytics; machine learning (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:inm:ormsom:v:26:y:2024:i:1:p:137-153

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