Novel Methods to Forecast Emergency Department Crowding

Abstract

In health care, a widespread crisis of emergency department crowding has arisen from increasing patient demand and diminishing bed capacity. Predictable fluctuations in patient demand suggest that dynamic resource mobilization may allow for efficient, just-in-time allocation of personnel and beds; this strategy, however, would require a method to forecast near-future crowding. The dissertation presents techniques from queuing theory and discrete event simulation that enable accurate forecasting of emergency department operating conditions. A systematic review of the literature described the causes, effects, and solutions of emergency department crowding, revealing that several measures have been proposed to measure crowding, although none have been validated for the purpose of real-time forecasting. An independent, prospective validation of four previously published crowding measures indicated that three of them accurately discriminate present ambulance diversion, but none of them reliably forecast future ambulance diversion. A discrete event simulation, named “ForecastED”, was developed to mimic the process of patient flow through the emergency department, such that a single model could forecast many different outcome measures. A prospective evaluation of the ForecastED system demonstrated that the model provides reliable, real-time forecasts of seven different measures of crowding up to eight hours into the future. This technology may provide a foundation for health care providers to coordinate and avoid potentially dangerous crowding situations.