Capacity Management in a Pandemic with Endogenous Patient Choices and Flows

TL;DR

This paper models healthcare capacity management during a pandemic, considering patient choices and flow dynamics across emergency and clinic facilities to optimize capacity allocation and reduce adverse outcomes.

Contribution

It introduces a fluid approximation model incorporating endogenous patient decisions and evolving severities, providing a novel analytical framework for capacity optimization in complex healthcare settings.

Findings

Endogeneity leads to non-intuitive capacity allocations.

Optimal strategies vary with demand profiles and patient severity dynamics.

The model achieves global optimality through computational decomposition.

Abstract

Motivated by the experiences of a healthcare service provider during the Covid-19 pandemic, we aim to study the decisions of a provider that operates both an Emergency Department (ED) and a medical Clinic. Patients contact the provider through a phone call or may present directly at the ED: patients can be COVID (suspected/confirmed) or non-COVID, and have different severities. Depending on the severity, patients who contact the provider may be directed to the ED (to be seen in a few hours), be offered an appointment at the Clinic (to be seen in a few days), or be treated via phone or telemedicine, avoiding a visit to a facility. All patients make joining decisions based on comparing their own risk perceptions versus their anticipated benefits: They then choose to enter a facility only if it is beneficial enough. Also, after initial contact, their severities may evolve, which may change their decision. The hospital system's objective is to allocate service capacity across facilities so as to minimize costs from patient deaths or defections. We model the system using a fluid approximation over multiple periods, possibly with different demand profiles. While the feasible space for this problem can be extremely complex, it is amenable to decomposition into different sub-regions that can be analyzed individually, the global optimal solution can be reached via provably parsimonious computational methods over a single period and over multiple periods with different demand rates. Our analytical and computational results indicate that endogeneity results in non-trivial and non-intuitive capacity allocations that do not always prioritize high severity patients, for both single and multi-period settings.