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

TL;DR

This paper presents a scalable, data-driven decision model that leverages electronic health records and machine learning to optimize preventive care allocation for prediabetic patients, significantly reducing healthcare costs.

Contribution

It introduces a novel combination of counterfactual inference, machine learning, and optimization for scalable, high-dimensional preventive care decision-making.

Findings

Potential to save $1.1 billion annually in the U.S.

Effective allocation of preventive treatments improves cost-efficiency.

Model applicable to other preventable diseases.

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.