Rationing Scarce Healthcare Capacity: A Study Of The Ventilator Allocation Guidelines During The Covid-19 Pandemic

In the United States, even though national guidelines for allocating scarce healthcare resources are lacking, 26 states have specific ventilator allocation guidelines to be invoked in case of a shortage. While several states developed their guidelines in response to the recent Covid-19 pandemic, NYS developed these guidelines in 2015 as "pandemic influenza is a foreseeable threat, one that we cannot ignore." The primary objective of this study is to assess the existing procedures and priority rules in place for allocating/rationing scarce ventilator capacity and propose alternative (and improved) priority schemes. We first build machine learning models using inpatient records of COVID-19 patients admitted to New York-Presbyterian/Columbia University Irving Medical Center and an affiliated community health center to predict survival probabilities as well as ventilator length-of-use. Then, we use the resulting point estimators and their uncertainties as inputs for a multi-class priority queueing model with abandonments to assess three priority schemes: (i) SOFA-P, which most closely mimics the existing practice by prioritizing patients with sufficiently low Sequential Organ Failure Assessment (SOFA) scores, (ii) ISP, which assigns priority based on patient-level survival predictions, and (iii) ISP-LU, which takes into account survival predictions and resource use duration. Our findings highlight that our proposed priority scheme, ISP-LU, achieves a demonstrable improvement over the other two alternatives. Specifically, the expected number of survivals increases and death risk while waiting for ventilator use decreases. We also show that ISP-LU is a robust priority scheme whose implementation yields a Pareto-improvement over both ISP and SOFA-P in terms of maximizing saved lives after mechanical ventilation while limiting racial disparity in access to the priority queue.