Saving the Most Lives During Pandemics: A New Analytical Triage Framework for Improving Hospital Operational Efficiency and Fairness

Hospitals use triage systems to prioritize patients based on their medical needs and potential benefits from timely treatment, particularly when resources such as ICU beds are limited. Patients admitted on a first-come-first-served basis, without triage, risk increased overall mortality, as later arrivals with higher survival potential may be deprived of critical resources. We propose an analytical triage framework, employing epidemic modeling to evaluate the efficiency (lives saved) and fairness of various patient prioritization strategies. Central to our approach is the concept of "return," defined as the survival benefit gained per unit of treatment duration. Guided by this principle, we develop a greedy triage strategy that maximizes expected survival benefit per unit resource, and prove that this strategy maximizes the lives saved under realistic conditions. Numerical results show that except for some rare cases, our proposed strategy consistently outperforms other strategies (such as first-come-first-served) in most conditions, significantly reducing mortality during high-demand periods such as pandemics. Lastly, our analysis explores the balance between efficiency and fairness, helping doctors choose triage strategies that effectively maximize lives saved while maintaining equitable hospital resource allocation.