An Interactive Decision-Support Dashboard for Optimal Hospital Capacity Management

TL;DR

This paper presents an interactive dashboard that integrates real-time data, predictive analytics, and optimization models to assist hospital administrators in making informed capacity management decisions during surge periods, demonstrated through deployment at Johns Hopkins during COVID-19.

Contribution

The study introduces a user-friendly, participatory-designed decision-support dashboard that effectively combines data and models for hospital capacity management during crises.

Findings

Dashboard was successfully deployed at Johns Hopkins during COVID-19.

Hospital administrators used the dashboard daily for capacity decisions.

The tool improved decision transparency and trustworthiness.

Abstract

Data-driven optimization models have the potential to significantly improve hospital capacity management, particularly during demand surges, when effective allocation of capacity is most critical and challenging. However, integrating models into existing processes in a way that provides value requires recognizing that hospital administrators are ultimately responsible for making capacity management decisions, and carefully building trustworthy and accessible tools for them. In this study, we develop an interactive, user-friendly, electronic dashboard for informing hospital capacity management decisions during surge periods. The dashboard integrates real-time hospital data, predictive analytics, and optimization models. It allows hospital administrators to interactively customize parameters, enabling them to explore a range of scenarios, and provides real-time updates on recommended optimal decisions. The dashboard was created through a participatory design process, involving hospital administrators in the development team to ensure practical utility, trustworthiness, transparency, explainability, and usability. We successfully deployed our dashboard within the Johns Hopkins Health System during the height of the COVID-19 pandemic, addressing the increased need for tools to inform hospital capacity management. It was used on a daily basis, with results regularly communicated to hospital leadership. This study demonstrates the practical application of a prospective, data-driven, interactive decision-support tool for hospital system capacity management.