Equitable Allocation of Healthcare Resources with Fair Survival Models
01/01/2021
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 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, and does not reinforce harmful systemic bias. In this study, we develop multiple fairness definitions and corresponding fair learning algorithms for survival models to ensure equitable allocation of healthcare resources. We demonstrate the utility of our methods in terms of fairness and predictive accuracy on three publicly available survival data sets.
Senior Director of Research and Analytics/Chief Data Scientist Ian Stockwell, PhD co-authored this article published in proceedings of the 2021 Society for Industrial and Applied Mathematics (SIAM) International Conference on Data Mining.