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, New York State 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 multiclass priority queueing model with abandonments to assess three priority schemes: (i) SOFA-P (Sequential Organ Failure Assessment based prioritization), which most closely mimics the existing practice by prioritizing patients with sufficiently low SOFA scores; (ii) ISP (incremental survival probability), which assigns priority based on patient-level survival predictions; and (iii) ISP-LU (incremental survival probability per length-of-use), 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 SOFA-P and ISP in terms of maximizing saved lives after mechanical ventilation while limiting racial disparity in access to the priority queue.

Introduction: 2021 Daniel H. Wagner Prize for Excellence in the Practice of Advanced Analytics and Operations Research

The judges for the 2021 Daniel H. Wagner Prize for Excellence in the Practice of Advanced Analytics and Operations Research selected the five finalist papers featured in this special issue of the INFORMS Journal on Applied Analytics. The prestigious Wagner Prize-awarded for achievement in implemented operations research, management science, and advanced analytics-emphasizes the quality and originality of mathematical models along with clarity of written and oral exposition. This year's winning application describes the design and deployment of Eva, the Greek COVID-19 testing system used as Greece was opening up for tourism in 2020. The remaining four papers describe the stochastic modeling and mixed-integer programming system used to optimize the Atlanta police patrol zones for better police balance and reduced response time to emergency calls;Lyft's new priority dispatch system, which solves the ride-sharing productivity paradox whereby increases in efficiency do not benefit the drivers;the application of advanced analytics to assist local and federal law enforcement organizations in their efforts to disrupt sex-trafficking networks;and the development of a new after-sales service concept, which increases chip availability for ASML's customers.