Machine Learning for Deferral of Care Prediction

TL;DR

This paper develops a machine learning model to predict patient care deferral, emphasizing the role of social determinants and assessing model fairness to improve proactive healthcare outreach and reduce long-term health costs.

Contribution

It introduces a predictive model for care deferral that incorporates social determinants of health and evaluates fairness across demographic groups.

Findings

Social determinants significantly improve prediction accuracy.

Models demonstrate fairness across demographics and socioeconomic factors.

Proactive outreach can potentially reduce care deferrals.

Abstract

Care deferral is the phenomenon where patients defer or are unable to receive healthcare services, such as seeing doctors, medications or planned surgery. Care deferral can be the result of patient decisions, service availability, service limitations, or restrictions due to cost. Continual care deferral in populations may lead to a decline in population health and compound health issues leading to higher social and financial costs in the long term. Consequently, identification of patients who may be at risk of deferring care is important towards improving population health and reducing care total costs. Additionally, minority and vulnerable populations are at a greater risk of care deferral due to socioeconomic factors. In this paper, we (a) address the problem of predicting care deferral for well-care visits; (b) observe that social determinants of health are relevant explanatory factors towards predicting care deferral, and (c) compute how fair the models are with respect to demographics, socioeconomic factors and selected comorbidities. Many health systems currently use rules-based techniques to retroactively identify patients who previously deferred care. The objective of this model is to identify patients at risk of deferring care and allow the health system to prevent care deferrals through direct outreach or social determinant mediation.