Equitable Allocation of Healthcare Resources with Fair Cox Models

TL;DR

This paper introduces fairness-aware Cox models to improve equitable healthcare resource allocation, ensuring predictions are unbiased by demographic stereotypes while maintaining predictive accuracy.

Contribution

It develops new fairness definitions and fair Cox models for survival analysis, addressing bias in healthcare prioritization.

Findings

Fair Cox models improve fairness metrics without sacrificing accuracy

Methods tested on two public survival datasets demonstrate effectiveness

Proposed models reduce demographic bias in resource allocation

Abstract

Healthcare programs such as Medicaid provide crucial services to vulnerable populations, but due to limited resources, many of the individuals who need these services the most languish on waiting lists. Survival models, e.g. the Cox proportional hazards model, can potentially improve this situation by predicting individuals' levels of need, which can then be used to prioritize the waiting lists. Providing care to those in need can prevent institutionalization for those individuals, which both improves quality of life and reduces overall costs. While the benefits of such an approach are clear, care must be taken to ensure that the prioritization process is fair or independent of demographic information-based harmful stereotypes. In this work, we develop multiple fairness definitions for survival models and corresponding fair Cox proportional hazards models to ensure equitable allocation of healthcare resources. We demonstrate the utility of our methods in terms of fairness and predictive accuracy on two publicly available survival datasets.