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Big Data Model to Help Optimize Hospital Resources During COVID-19

The big data model will help address short- and long-term resource needs for Monroe Carell Jr. Children’s Hospital at Vanderbilt during COVID-19.

Big data model to help optimize hospital resources during covid-19

Source: Thinkstock

By Jessica Kent

- Researchers at Vanderbilt’s Data Science Institute are developing a model that will use big data to address the impact of COVID-19 on policies, procedures, and resources now and in the future.

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The tool will help the Monroe Carell Jr. Children’s Hospital at Vanderbilt evaluate short- and long-term resource needs, so that the organization can be fully prepared to meet patients’ needs even as COVID-19 infection rates surge and diminish.

Students and staff at the Data Science Institute will build a model that allows users to visualize and simulate varying patient loads and possible hospital mitigation policies. These policies could include the deferral of elective surgeries, re-sterilization of personal protective equipment (PPE), and additional staffing.

This model will build on the capabilities of a model developed by the Department of Health Policy, which provides projections for overall patient counts. The new model will forecast the capacity of individual hospitals or the impact of possible hospital policies or mitigations.

Together, these models will provide a comprehensive picture of COVID-19 impacts, helping hospitals make decisions that will maximize their resources.

“We are witnessing an incredibly challenging and unprecedented time for the healthcare industry,” said chief data scientist Jesse Spencer-Smith. “That is why at the Data Science Institute, we are eager to partner with colleagues at Vanderbilt University Medical Center to help optimize resources.”

In the fight against COVID-19, hospitals and health systems have had to face PPE shortages and the threat of limited capacity. Leaders across the industry have acted quickly to build tools that will help organizations monitor and track resources and deliver quality care.

Recently, Cleveland Clinic built predictive analytics models that help forecast patient volume, bed capacity, ventilator availability, and other metrics. The models provide timely, reliable information for hospitals and health systems to optimize care delivery for COVID-19 and other patients.

“These predictive models were developed jointly by two organizations that understand patient populations, data and modeling,” said Chris Donovan, executive director of Enterprise Information Management & Analytics at Cleveland Clinic.

“We are sharing the models publicly so health systems and government agencies globally can use them in their own communities. Our hope is that others contribute their ideas and improvements to the models as well.”

Tools like these have played a major role in helping organizations keep up with, or ahead of, the pandemic. Real-time data has been a key factor in mitigating the effects of COVID-19, and will likely continue to feature largely in the industry’s response to the virus.

The team at Vanderbilt’s Data Science Institute expects that their model will help hospital administrators and policymakers optimize resources and understand how certain policies will impact outcomes and processes.

“This project involves all of the data scientists at the Data Science Institute, as well as geospatial mapping experts in the Vanderbilt Research IT Service and the Jean and Alexander Heard Libraries. It also allows our master’s students to collaborate on work that is of paramount importance,” said Douglas Schmidt, Cornelius Vanderbilt Professor of Engineering and co-director of the Data Science Institute.

“This is an opportunity for these young data scientists to develop their skills and have a tangible impact on addressing COVID-19.”