Forecasting Patient Arrivals and Optimizing Physician Shift Scheduling in Emergency Departments.

Winter Simulation Conference(2023)

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摘要
Emergency Departments (EDs) are the primary access points for millions of patients seeking medical care. The increasing patient demand and lack of long-term dynamic planning strain the EDs in providing timely patient care, leading to crowding. While a well-recognized problem, ED crowding is still prevalent, where suboptimal resource allocation is one significant contributing factor. In this research, we developed an end-to-end solution that first forecasted the patient arrivals to the partner ED and then used an optimization model to develop an optimal physician staffing schedule to minimize the combined cost of patient wait times, handoffs, and physician shifts. Finally, the new schedule was tested using the validated simulation model to evaluate the ED performance. By generating shift schedules based on forecasts and testing them in the validated simulation model, we observed that patient time in the ED and handoffs could be reduced by 5.6% and 9.2% compared to current practices.
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关键词
Shift Schedule,Patient Arrival,Simulation Model,Resource Allocation,Waiting Time,Physician Patient,Patient Demand,Emergency Department Crowding,Root Mean Square Error,Mathematical Model,Patient Safety,Machine Learning Models,Performance Metrics,Mean Absolute Error,Time Slot,Forecasting Model,Non-significant Increase,XGBoost,Mean Absolute Percentage Error,Mixed Integer Linear Programming,Emergency Severity Index,Emergency Department Physicians,Autoregressive Integrated Moving Average,Mixed Integer Linear Programming Model,Number Of Physicians,Long-term Forecasting,Emergency Department Admission,Time Series Prediction,Onboarding,Margin Of Error
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