key: cord-1032156-9c0y8mx9
authors: Brüggemann, Sven; Chan, Theodore; Wardi, Gabriel; Mandel, Jess; Fontanesi, John; Bitmead, Robert R.
title: Decision support tool for hospital resource allocation during the COVID-19 pandemic
date: 2021-06-01
journal: Inform Med Unlocked
DOI: 10.1016/j.imu.2021.100618
sha: a8a64e67a8d8b36c0f84bfe37ca92d517c51f5e2
doc_id: 1032156
cord_uid: 9c0y8mx9

The SARS-CoV-2 (COVID-19) pandemic has placed unprecedented demands on entire health systems and driven them to their capacity, so that health care professionals have been confronted with the difficult problem of ensuring appropriate staffing and resources to a high number of critically ill patients. In light of such high-demand circumstances, we describe an open web-accessible simulation-based decision support tool for a better use of finite hospital resources. The aim is to explore risk and reward under differing assumptions with a model that diverges from most existing models which focus on epidemic curves and related demand of ward and intensive care beds in general. While maintaining intuitive use, our tool allows randomized “what-if” scenarios which are key for real-time experimentation and analysis of current decisions’ down-stream effects on required but finite resources over self-selected time horizons. While the implementation is for COVID-19, the approach generalizes to other diseases and high-demand circumstances.

The novel COVID-19 pandemic has created an unpreceded global strain on healthcare Overcrowded intensive care units (ICUs) present a complex challenge to administrators 39 who are plagued by having to judge who will receive treatment and who will not, since 40 admitting a patient today means potentially not being able to admit a needier patient for in-house resource prediction is the fact that operations are facility specific, e.g., 80 academic health systems such as the University of California, San Diego (UCSD) and 81 non-academic facilities typically compose teams very differently and adapt them to 82 current demand.

Simply, there is a lack of systematic integration of current decision's impact on future 84 capacity, associated staff and ancillary support requirements, and hence absence of 85 methodical support for health care professionals in their decision making. To address contingency planning, we describe a tool that uses local inputs to simulate demand for 117 finite and inter-dependent resources such as staff, medication and medical equipment 118 under different circumstances over a self-selected time horizon in a stochastic fashion.

Stochasticity is an essential ingredient and refers to the randomness of individual 120 patient response, which is aggregated and smoothed over the many patients to permit 121 an exploration of all possible, including rare, outcomes. In the remaining part of this 122 section, we present the tool in more detail.

Given our requirement of an easy-to-use graphical interface, the ability to conduct The resource requirements are computed given user-specified input parameters:

shortages of resources that have occurred during the pandemic at UCSD health system, primarily caused by COVID-19 patients. 

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