Value-based optimization of healthcare resource allocation for COVID-19 hot spots

TL;DR

This paper presents a value-based nonlinear programming model to optimize healthcare resource allocation for COVID-19 hot spots, aiming to improve decision-making amid uncertainty and dynamic conditions.

Contribution

It introduces a novel value-based optimization framework that coordinates demand forecasts with resource allocation across locations and time for COVID-19 hot spots.

Findings

The model effectively allocates hospital beds over time and space.

Value-based approach improves resource utilization.

Supports decision-making under uncertainty.

Abstract

With the emerging COVID-19 crisis, a critical task for public health officials and policy makers is to decide how to prioritize, locate, and allocate scarce resources. To answer these questions, decision makers need to be able to determine the location of the required resources over time based on emerging hot spot locations. Hot spots are defined as concentrated areas with sharp increases in COVID19 cases. Hot spots place stress on existing healthcare resources, resulting in demand for resources potentially exceeding current capacity. This research will describe a value based resource allocation approach that seeks to coordinate demand, as defined by uncertain epidemiological forecasts, with the value of adding additional resources such as hospital beds. Value is framed as a function of the expected usage of a marginal resource (bed, ventilator, etc). Subject to certain constraints, allocation decisions are operationalized using a nonlinear programming model, allocating new hospital beds over time and across a number of geographical locations. The results of the research show a need for a value based approach to assist decision makers at all levels in making the best possible decisions in the current highly uncertain and dynamic COVID environment.