ABSTRACT
Sudden periods of extreme and persistent changes in the distribution of medical emergencies can trigger resource planning inefficiencies for Emergency Medical Services, causing delayed responses and increased waiting times. Predicting such changes and reacting adaptively can alleviate these adversarial impacts. In this paper, we propose a simple framework to enhance historically calibrated call volume models, the latter a focus of study in the arrival estimation literature, to give more accurate short-term prediction by refitting their residuals into time series. We discuss some justification of our framework from the perspective of doubly stochastic Poisson processes. We illustrate our methodology in predicting the hourly call volume to the 911 call center during the Covid-19 pandemic in NYC, showing how it could improve the performance of baseline historical estimators by close to 50% measured by the out-of-sample prediction error for the next hour. © 2021 IEEE.
ABSTRACT
In most emergency medical services (EMS) systems, patients are transported by ambulance to the closest most appropriate hospital. However, in extreme cases, such as the COVID-19 pandemic, this policy may lead to hospital overloading, which can have detrimental effects on patients. To address this concern, we propose an optimization-based, data-driven hospital load balancing approach. The approach finds a trade-off between short transport times for patients that are not high acuity while avoiding hospital overloading. In order to test the new rule, we build a simulation model, tailored for New York City's EMS system. We use historical EMS incident data from the worst weeks of the pandemic as a model input. Our simulation indicates that 911 patient load balancing is beneficial to hospital occupancy rates and is a reasonable rule for non-critical 911 patient transports. The load balancing rule has been recently implemented in New York City's EMS system. © 2021 IEEE.