title: Determining minimum staffing levels during snowstorms using an integrated simulation, regression, and reliability model.
abstract: Emergency medical services (EMS) provide life-saving care and hospital transport to patients with severe trauma or medical conditions. Severe weather events, such as snow events, may lead to adverse patient outcomes by increasing call volumes and service times. Adequate staffing levels during such weather events are critical for ensuring that patients receive timely care. To determine staffing levels that depend on weather, we propose a model that uses a discrete event simulation of a reliability model to identify minimum staffing levels that provide timely patient care, with regression used to provide the input parameters. The system is said to be reliable if there is a high degree of confidence that ambulances can immediately respond to a given proportion of patients (e.g., 99 %). Four weather scenarios capture varying levels of snow falling and snow on the ground. An innovative feature of our approach is that we evaluate the mitigating effects of different extrinsic response policies and intrinsic system adaptation. The models use data from Hanover County, Virginia to quantify how snow reduces EMS system reliability and necessitates increasing staffing levels. The model and its analysis can assist in EMS preparedness by providing a methodology to adjust staffing levels during weather events. A key observation is that when it is snowing, intrinsic system adaptation has similar effects on system reliability as one additional ambulance.
authors: Computational and Applied Mathematics, Rice University, Houston, TX, USA. agkunkel@gmail.com
authors: Amber
authors: A
authors: Kunkel
authors: Laura A
authors: LA
authors: McLay
mesh_terms: D004632:Emergency Medical Services; D006302:Health Services Research; D006801:Humans; D015233:Models, Statistical; D012914:Snow; D014768:Virginia; D000078329:Workforce