Team Based, Risk Adjusted Staffing during a Pandemic: an Agent Based Approach

Prabhu, V. G.; Taaffe, K.; Caglayan, C.; Isik, T.; Song, Y. J.; Hand, W.; Ieee,

Since the World Health Organization declared the novel coronavirus disease a pandemic, more than 2 million cases of infections and 140,000 deaths have been reported across the world. Specialty physicians are now working as frontline workers due to hospital overcrowding and a lack of providers, and this places them as a high-risk target of the epidemic. Within these specialties, anesthesiologists are one of the most vulnerable groups as they come in close contact with the patient's airway. An agent-based simulation model was developed to test various staffing policies within the anesthesiology department of the largest healthcare provider in Upstate South Carolina. We demonstrate the benefits of a restricted, no mixing shift policy, which segregates the anesthesiologists as groups and assigns them to a shift within a single hospital. Results consistently show a reduction in the number of deaths, anesthesiologists not available to work, and the number of infected anesthesiologists.