Saving lives during a global pandemic through medical resource optimization

An analytical partnership to track, process and inhibit the spread of COVID-19

A medical center operating analyzes to combat COVID-19 is Cleveland Clinic, a renowned global health care provider operating in the United States, Canada, England and the United Arab Emirates. The Cleveland Clinic is at the forefront of the coronavirus pandemic, determined to optimize hospital readiness before, during and after regional peaks.

The Cleveland Clinic partnered with SAS to create innovative models that help predict patient volume, bed capacity, medical device availability and more. Armed with this information, the Cleveland Clinic is better positioned to support its decision-making and address the COVID-19 challenges it faces today, as well as plan for future requirements. And as the dynamics of the pandemic evolve, models can adapt in real time – like taking into account social distance to “flatten the curve” and reduce the spread.

Another unique aspect of the models is that they do not predict a projection based on a single set of assumptions. Instead, they create scenarios of worst case, best case and most likely. This multi-scenario analysis informs resource planners to prepare for the next. An example is the Cleveland Clinic’s response to possible COVID-19 scenarios generated by the models. The medical center activated a plan that prepared it for the worst case scenario – it built a 1,000-bed flood hospital on its Cleveland campus of education for COVID-19 patients who do not need ICU care.

Join the fight to mitigate COVID-19

SAS and the Cleveland Clinic are pleased to announce that theirs COVID-19 predictable models are freely available via GitHub, and other hospitals and agencies are encouraged to access and use them. Not only do they provide important information to optimize healthcare delivery, they also predict supply chain impacts, financing, workforce and other key areas.

“These predictive models were developed jointly by two organizations that understand patient populations, data and modeling,” said Chris Donovan, CEO of Enterprise Information Management and Analytics at Cleveland Clinic. “We share the models publicly so that health systems and government agencies can use them globally in their own communities. Our hope is that others will also contribute their ideas and improvements to the models. “

The analysis is centered around the epidemiological SEIR model that models the flow of individuals through four stages of a disease: susceptible, exposed, infected and recovered. The SEIR model developed by SAS and the Cleveland Clinic is based on an open source model from the University of Pennsylvania that has been re-coded and expanded on SAS® platform. The model is continuously improving with real-time feedback from epidemiologists and data scientists at the Cleveland Clinic. The resulting models include flexible control of model parameters and different model methods that account for regional health and demographic variations and assumptions at the state level.

“These models can help hospitals, health facilities, state health departments, and authorities predict the impact of COVID-19 and prepare for the future,” said Steve Bennett, director of SAS Global Governance. “The models can also help more vulnerable, less developed health systems in the fight against COVID-19.”

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