Abstract
The coronavirus disease 2019 (COVID-19) global pandemic poses the threat of overwhelming healthcare systems with unprecedented demands for intensive care resources. Managing these demands cannot be effectively conducted without a nationwide collective effort that relies on data to forecast hospital demands on the national, regional, hospital and individual levels. To this end, we developed the COVID-19 Capacity Planning and Analysis System (CPAS)—a machine learning-based system for hospital resource planning that we have successfully deployed at individual hospitals and across regions in the UK in coordination with NHS Digital. In this paper, we discuss the main challenges of deploying a machine learning-based decision support system at national scale, and explain how CPAS addresses these challenges by (1) defining the appropriate learning problem, (2) combining bottom-up and top-down analytical approaches, (3) using state-of-the-art machine learning algorithms, (4) integrating heterogeneous data sources, and (5) presenting the result with an interactive and transparent interface. CPAS is one of the first machine learning-based systems to be deployed in hospitals on a national scale to address the COVID-19 pandemic—we conclude the paper with a summary of the lessons learned from this experience.
Highlights
The coronavirus disease 2019 (COVID-19) pandemic poses immense challenges to healthcare systems across the globe—a major issue faced by both policy makers and front-line clinicians is the planning and allocation of scarce medical resources such as Intensive Care Unit (ICU) beds (Bedford et al 2020)
In order to manage the unprecedented ICU demands caused by the pandemic, we need nationwide collective efforts that hinge on data to forecast hospital demands across various levels of regional resolution
We developed the COVID-19 Capacity Planning and Analysis System (CPAS), a machine learningbased tool that has been deployed to hospitals across the UK to assist the planning of ICU beds, equipment and staff (NHS 2020d)
Summary
The coronavirus disease 2019 (COVID-19) pandemic poses immense challenges to healthcare systems across the globe—a major issue faced by both policy makers and front-line clinicians is the planning and allocation of scarce medical resources such as Intensive Care Unit (ICU) beds (Bedford et al 2020). CPAS is designed to provide actionable insights into the multifaceted problem of ICU capacity planning for various groups of stakeholders; it fulfills this goal by issuing accurate forecast for ICU demand over various time horizons and resolutions It makes use of the state-of-the-art machine learning techniques to draw inference from a diverse repository of heterogeneous data sources. The front-line clinicians can make use of the tool to understand the risk profile of individual patients These four groups of stakeholders require insights on different time horizons and levels of aggregation. 5. The rest of the article is organized as follows: After formulating the ICU planning problem into a set of learning tasks, we introduce the individualized risk predictor, the aggregated trend forecaster, and the agent-based simulator in Sect.
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