Identifying Inequity in Treatment Allocation

Disparities in resource allocation, efficacy of care, and patient outcomes along demographic lines have been documented throughout the healthcare system. In order to reduce such health disparities, it is crucial to quantify uncertainty and biases in the medical decision-making process. In this work, we propose a novel setup to audit inequity in treatment allocation. We develop multiple bounds on the treatment allocation rate, under different strengths of assumptions, which leverage risk estimates via standard classification models. We demonstrate the effectiveness of our approach in assessing racial and ethnic inequity of COVID-19 outpatient Paxlovid allocation. We provably show that for all groups, patients who would die without treatment receive Paxlovid at most 53% of the time, highlighting substantial under-allocation of resources. Furthermore, we illuminate discrepancies between racial subgroups, showing that patients who would die without treatment receive Paxlovid at most 6% and 27 % lower for Blacks than Whites and Asians, respectively.