MODELING HOSPITAL RESOURCES BASED ON GLOBAL EPIDEMIOLOGY AFTER EARTHQUAKE-RELATED DISASTERS

Abstract

In an earthquake emergency, injured people require hospital emergency services and timely medical treatment. Earthquake-related patients often have trauma injuries and stress-linked (ischemic) illnesses that require multiple healthcare procedures, such as minor orthopedic treatment, surgical treatment of fractures, and thrombolysis or thrombectomy. Hospital operation models have been proposed to examine these healthcare procedures; however, they exhibit two fundamental gaps that hinder their ability to assess critical service areas after earthquakes. First, these models rest heavily on emergency procedures based on injury severity rather than type. Second, healthcare demands and injury profiles are often modeled after moderate earthquakes in the United States without including mass casualty data after large earthquakes. This approach has led to an oversimplified representation of the utilization of hospital emergency services and resources, especially for the various profiles of trauma patients after earthquakes globally. This research presents a new hospital operations model based on patient injury type and worldwide earthquake epidemiology data to fill these two gaps. We built the model using discrete event simulations to capture patients' healthcare metrics that vary rapidly after the earthquake. We then used these metrics to study how healthcare outcomes vary with different levels of functional capacity in specific hospital service areas. Our case study showed that ED-level patients wait 40% longer to receive treatment when half of the examination rooms in the emergency department (ED) are lost and 35% less time if examination rooms increase by 50%. Also, the waiting time for hospital-level procedures reduces up to 65% if the bed occupancy rate drops by 15%, e.g., if reverse triage procedures are activated through a discharge of 15% of patients hospitalized before the earthquake. Our findings provide a valuable tool for decision-making in hospital preparedness as it links expected patient volumes to key healthcare metrics according to hospital functional capacity.