# Learning Patient Engagement in Care Management: Performance vs. Interpretability

## TL;DR

This paper introduces a data-driven method for scoring patient engagement in care management, balancing predictive accuracy with interpretability to assist care managers in improving patient health outcomes.

## Contribution

The paper presents a novel behavioral engagement scoring pipeline that predicts patient response propensity while providing interpretable insights for care managers.

## Key findings

- The scoring method accurately predicts patient engagement levels.
- Interpretable insights are provided without sacrificing prediction performance.
- The approach is validated on real-world care management data.

## Abstract

The health outcomes of high-need patients can be substantially influenced by the degree of patient engagement in their own care. The role of care managers includes that of enrolling patients into care programs and keeping them sufficiently engaged in the program, so that patients can attain various goals. The attainment of these goals is expected to improve the patients' health outcomes. In this paper, we present a real world data-driven method and the behavioral engagement scoring pipeline for scoring the engagement level of a patient in two regards: (1) Their interest in enrolling into a relevant care program, and (2) their interest and commitment to program goals. We use this score to predict a patient's propensity to respond (i.e., to a call for enrollment into a program, or to an assigned program goal). Using real-world care management data, we show that our scoring method successfully predicts patient engagement. We also show that we are able to provide interpretable insights to care managers, using prototypical patients as a point of reference, without sacrificing prediction performance.