As hospitals struggle with high demand and increasing complexity of patient illness, a team of researchers, including Kimia Ghobadi, a John C. Malone Assistant Professor in the Whiting School of Engineering’s Department of Civil and Systems Engineering and doctoral student, Felix Parker, developed a way to help hospitals more effectively manage their capacity during high demand periods, supporting timely and effective care.
“Hospitals have limited resources and beds, and yet we’re seeing a steady increase in the number of patients, compounding delays in patient care and burnout in nurses and providers,” said Ghobadi. “Hospital managers have to make complex capacity decisions on a daily basis, and they can’t simply add more physical beds due to regulation, staffing limitations, and cost.”
The team’s approach, described on arXiv, offers healthcare networks an interactive decision-support dashboard to manage resources during sudden increases in patient admissions.
Underpinning the interface are data-driven models developed in the team’s previous study, which consider critical factors like the number of incoming patients, their expected length of stay, current hospital capacity and occupancy, and the health network’s existing plans to mitigate a surge.
In collaboration with the Johns Hopkins Hospital Capacity Command Center, the team’s dashboard was tested during the height of the COVID-19 pandemic and validated with subsequent simulations that showed promising potential to improve resource allocation and capacity management in healthcare systems.
“Our results show that we could reduce the need for additional beds in a single hospital by roughly 40%. This reduction increases when we have a network of hospitals working together, for instance, transferring just one patient every other day from one hospital to another in a network of five hospitals could decrease the need for additional beds by over 90%. Currently, hospitals usually only transfer patients for medical reasons. Our study highlights that occasional strategic transfers to balance capacity could help mitigate overcrowding, which would enable better care and more capacity for the clinical transfers, too,” Ghobadi said.
The researchers say that the models’ user-friendly interface, flexibility, and scalability make them particularly effective.
“We built our models to be flexible so every hospital can incorporate their distinct practical and operational preferences and needs,” said Ghobadi. “And with an easy-to-use interface, hospital stakeholders can easily modify and run the complex underlying models on the fly, enabling them to test out their surge strategies in real-time.”
Ghobadi posits that as the health care industry begins to integrate data and AI more extensively, these methods will become essential.
“There is an increasing amount of data in healthcare that could inform decisions. With challenges like rising patient numbers and potential for future fluctuations due to natural or man-made disasters, there is enthusiasm for using data and modeling to improve patient outcomes. These models can support human decision-makers with tailored interpretable recommendations and actionable insights. Our method is a step in that direction,” she said.
The team plans to continue their work with improvements for the decision-support dashboard and the underlying models. They aim to create forecasting models of patient demand, including patients admitted through the emergency departments. They also plan to gain insight into health systems’ surge policies to optimize them for high demand scenarios.
Additional Johns Hopkins collaborators include doctoral student Fardin Ganjkhanloo, former Chief Administrative Officer of Emergency Medicine and Capacity Management, James Scheulen, and Diego A. Martínez from Johns Hopkins Command Center and the Pontificia Universidad Católica de Valparaíso.