SAS Debuts Tools to Gauge Risks and Impacts of Reopening
How will reopening affect your area? New tools enable health systems, communities to protect at-risk individuals and forecast outbreak impacts
As states loosen stay-at-home orders and businesses reopen, protecting vulnerable populations and the public at large from the COVID-19 virus is paramount. To help fuel better decisions, analytics leader SAS debuts new tools to empower providers, data scientists and citizens alike – free for public use. The applications democratize powerful analytics to boost preparedness and mitigation efforts with real-time pandemic data.
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Optimization models inform hospital plans to restart elective surgeries and other health services
Many important inpatient and outpatient services were suspended during the pandemic response. SAS and Cleveland Clinic are making optimization models publicly available on GitHub for data scientists. Based on inputs from hospitals, the models can be used to identify optimal restarting plans. Health care organizations may use the models to balance economics and efficient use of resources and quality of clinical care. Using the models, a hospital may consider medical facilities, services lines (such as orthopedics) and subservices (such as sports injuries and joint) compared with shared resource constraints, like COVID-19 test kits, ventilators, shared beds, operating rooms and more.
Additionally, hospitals and clinics may use the models to prepare for new waves of COVID-19 (or other resource stresses) by understanding critical resource utilization (such as ICU beds and ventilators) within elective services and how to keep some of those resources available while maintaining healthy hospital economics.
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Epidemiological Scenario Analysis projects disease impact and medical resource needs
Designed for data and analytics professionals, COVID-19 Epidemiological Scenario Analysis is an extension of the collaboration between SAS and the Cleveland Clinic, helping hospitals plan current and future needs with predictive modeling. A new user-driven interface now enables the analytics community to run the same public models that are also available on GitHub.
The new analysis environment uses AI-enabled SAS Health on SAS Viya to project the impact of a disease outbreak on the population through various user-driven scenarios, following the population flow through an epidemic’s four stages: susceptibility, exposure, infection and recovery. The scenario results help governments, health care organizations and equipment manufacturers better understand medical resource needs (like hospital and ICU beds and ECMO and ventilator utilization).