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By Catherine Graham /Published Oct 27

Coronavirus cases are trending up in a number of states, forecasting a fall surge that could push hospitals to capacity and deplete already scarce supplies. Experts worry that hospital systems in hard-hit areas may not be ready.

Managing the number of beds, staff, and personal protective equipment needed is especially challenging when the demand could change virtually overnight, as it did in March. Lessons from the spring can help hospitals avoid an even worse fall surge, says data scientist Kimia Ghobadi, a professor in the Department of Civil and Systems Engineering at Johns Hopkins University and a member of the Malone Center for Engineering in Healthcare.

Using data from hospital systems around the country, Ghobadi and her colleagues have developed mathematical models that determine optimal resource allocation for pandemic response.

One strategy that will help large medical systems like Johns Hopkins prepare for the next surge is to balance patient loads across hospitals so that no single facility becomes overwhelmed. For example, a patient may need to be transferred from a community hospital to a larger hospital because the larger facility has twice as many beds available that day. Transfers also help hospitals allocate scarce ICU resources for patients with severe cases of COVID-19.

“Resources will go to waste if every hospital is just preparing for their own worst-case scenario,” Ghobadi says. “Instead, we should look at this from a systems perspective. If we pool resources together, we can balance capacity and reduce the impact on individual hospitals, which can lead to better patient care.”

Backed by a grant from the U.S. Centers for Disease Control and Prevention, Ghobadi and researchers from the Center for Data Science and Emergency Medicine are applying mathematical models to optimally transfer patients within the Johns Hopkins Health System, which includes five hospitals in Maryland and Washington, D.C. The models analyze both clinical and operational data to best manage capacity and identify patients who are candidates to be transferred to another facility.

Automating this “load-balancing” process will benefit both busy providers and patients, Ghobadi believes.

“Hospital transfers often happen when there is already some care given,” Ghobadi says. “That is not always efficient, because in some ways you must start over. What does this patient need, and what resources do we have available for their hospitalization duration? We want to give providers a support tool that will help them decide where the patient can get the best care from the beginning.”