Equitable Allocation of Healthcare Resources with Fair Cox Models
This work addresses fairness in healthcare resource allocation for vulnerable populations, representing an incremental improvement by adapting existing survival models with fairness constraints.
The paper tackles the problem of ensuring fairness in healthcare resource allocation by developing fairness definitions and fair Cox proportional hazards models to prioritize waiting lists, demonstrating their utility on two survival datasets with improved fairness and predictive accuracy.
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.