To inform decision-making during the COVID-19 pandemic, Cleveland Clinic has created predictive analytics models that help forecast patient volume, bed capacity, ventilator availability, and other metrics.

With COVID-19 rapidly spreading among patient populations, hospitals and health systems are facing unprecedented strains on resources and capacity. Organizations are increasingly turning to real-time analytics tools to help track and predict healthcare demands.

The predictive models, developed by Cleveland Clinic and SAS, provide timely, reliable information for hospitals and health systems to optimize care delivery for COVID-19 and other patients.

The analytics models were designed to create worst-case, best-case, and most-likely scenarios, unlike some forecasts that focus on a prediction based on a single set of assumptions. The models can adjust in real time as situations and data change, such as social distancing measures and disease spread.

Using this information, Cleveland Clinic can predict and plan for future demands on the health system, including ICU beds, personal protective equipment, and ventilators. After reviewing possible COVID-19 surge scenarios generated by the models, Cleveland Clinic activated a plan to prepare for its worst-case scenario. The health system built a 1,000-bed surge hospital on its education campus for COVID-19 patients who don’t need ICU care.

Source: healthitanalytics.com