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Injured people require hospital emergency services and timely medical treatment after extreme earthquakes. Earthquake-related patients often have trauma injuries and stress-linked (ischemic) ailments 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 are often modeled from injury profiles after moderate earthquakes in the United States without including epidemiology data after large earthquakes globally. This approach has led to oversimplified hospital emergency services and resource utilization representation. This research presents a new hospital operations model based on patient injury type and worldwide earthquake epidemiology to fill these gaps. We build the model using discrete-event simulations to capture dynamic metrics on hospital operational outcomes after the earthquake, such as patient time-to-treatment and unassisted patient ratio. We then studied how these metrics vary with different levels of functional capacity in the specific hospital resources. Our results showed that waiting times for emergency department (ED)-level patients vary non-linearly with changes in the number of functional service areas. Also, significant reduction in the waiting time for hospital-level procedures was found for relatively small decrease in the bed occupancy rate, for example, if reverse triage procedures are activated (i.e. a discharge of non-critical patients admitted before the earthquake). Our findings provide a valuable tool for decision-making in hospital preparedness as they explicitly measure the impacts of functional capacity on key healthcare metrics for specific earthquake-related patients.
Merino et al. (Wed,) studied this question.