Who Goes Next? Optimizing the Allocation of Adherence-Improving Interventions

TL;DR

This paper presents a combined optimization and prediction approach to allocate adherence interventions effectively, aiming to reduce cardiovascular disease events by identifying at-risk patients using a dynamic logistic regression model and an integer programming framework.

Contribution

It introduces a novel integration of a predictive model with an optimization framework to improve resource allocation for adherence interventions in healthcare.

Findings

Including past adherence improves prediction accuracy.

The combined model can effectively prioritize patients for interventions.

Potential to decrease cardiovascular events through targeted resource allocation.

Abstract

Long-term adherence to medication is a critical factor in preventing chronic diseases, such as cardiovascular disease. To address poor adherence, physicians may recommend adherence-improving interventions; however, such interventions are costly and limited in their availability. Knowing which patients will stop adhering helps distribute the available resources more effectively. We developed a binary integer program (BIP) model to select patients for adherence-improving intervention under budget constraints. We further studied a long-term adherence prediction model using dynamic logistic regression (DLR) model that uses patients' claim data, medical health factors, demographics, and monitoring frequencies to predict the risk of future non-adherence. We trained and tested our predictive model to longitudinal data for cardiovascular disease in a large cohort of patients taking medication for cholesterol control seen in the national Veterans Affairs health system. Our study shows the importance of including past adherence to increase prediction accuracy. Finally, we assess the potential benefits of using the prediction model by proposing an algorithm that combines the DLR and BIP models to decrease the number of CVD events in a population.