Development and Usability of Tools to Improve Hospital Resiliency to Capacity Surges

Abstract: Hospital capacity surges significantly affect nearly all hospitals under both routine and severe conditions ranging from seasonal flu, unpredictable admission spikes, local emergencies, and epidemics such as Covid. The inability to resiliently anticipate and adapt to these events can seriously strain bed, staff, and equipment availability, with significant associated impacts on patient care. We describe ongoing work to iteratively develop and improve usable analytic tools to help hospitals better and more resiliently predict and manage capacity surges. These models accurately project future day-to-day unit-specific room, equipment, and staff demand and shortfalls, self-tuning to any given hospital and surge pattern on a rolling basis, with re-sults displayed in intuitive and actionable manners. A key motivation is that such models, if well-designed for end-users, can help hospitals pre-emptively anticipate, prepare, and adapt appropriately locally, a fundamental concept of resiliency engineering. Participatory design, human factors, and usability analysis thus were used throughout this work to continuously improve the model’s features, interface, accuracy, and utility. Resulting functionality, model logic, and interface improvements are described, including 12%-62% improvements in all usability scores (ease of use, cognitive effort, layout navigation, time to complete, results interpretability) and 61%-95% improvements in accuracy.

Keywords: Hospital capacity, Covid-19 epidemic, Simulation modelling, Usability analysis

DOI: 10.54941/ahfe1006981

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