A Simulation-Based Approach for Optimal Nurse Scheduling in an Emergency Department
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The purpose of this research is to determine an appropriate nurse staffing strategy for the Intermediate Care Unit (ICU) and the Critical Care Unit (CCU) of the Emergency Department at York Hospital in York, Pennsylvania. This strategy must adhere to certain administrative policies while keeping patient waiting times within allowable limits. Determining the proper number of resources in an emergency department is a difficult problem because while assistance must be provided without delay at any time, the available resources are restricted by the hospital budget. This research involves simulating the operations of the Emergency Department at York Hospital using the software package Arena 7.0 to evaluate how the system is impacted by various nurse staffing strategies. A microcomputer-based decision support system (DSS) for nurse scheduling that was first developed by Sitompul in 1991 has been implemented using Turbo Pascal 6.0 to generate twenty possible nurse staffing plans. The best alternative staffing plan has been evaluated by the simulation model to determine its effect on waiting times for patients. Specifically, patients are divided into five ESI levels, where ESI-1 patients are the most serious and ESI-5 patients are the least serious, and waiting times are provided for each patient type. While the DSS approach is useful in generating specific working schedules that are acceptable to the nurses' requirements, it is limited when developing an overall staffing plan. Specifically, the DSS requires a user-defined ratio of nurses working the various shifts, and this ratio must remain constant throughout each month even if patient arrival rates are known to be time dependent. As an alternative approach, OptQuest for Arena was employed to search for an overall nurse staffing plan. After providing Arena with 50 DSS-generated schedules that satisfy the nurses' requirements, OptQuest was used to determine the best schedule for each nurse to follow in order to minimize the average waiting time in the system for patients. Although the average waiting time obtained by the OptQuest staffing plan decreased from the current staffing plan for all patient types, a paired-t comparison determined using Arena's Output Analyzer indicated no statistical difference (at the 95% confidence level) between the DSS and OptQuest scenarios, in terms of the average waiting time for ESI-1 and ESI-2 patients. Further analysis indicated that a system bottleneck occurred in the triage area of the emergency department during evening hours. After adding one additional triage nurse in the evening shift, the OptQuest-generated staffing plan was re-evaluated. The results indicate that the suggested staffing plan reduced the average waiting time in the current staffing plan by 34.33%, 32.73%, 47.87%, 54.92%, and 52.41% for ESI-1, ESI-2, ESI-3, ESI-4, and ESI-5 patients, respectively. In addition, the average waiting time of ESI-1, ESI-2, ESI-3, ESI-4, and ESI-5 patients for the suggested staffing plan was 19.27%, 19.36%, 39.37%, 48.55%, and 46.64%, respectively, less than for the staffing plan determined when using the DSS approach alone.
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