Maximizing Patient Coverage Through Optimal Allocation of Residents and Scribes to Shifts in an Emergency Department

Developing three-phase modified bat algorithms to solve medical staff scheduling problems while considering minimal violations of preferences and mean workload

BACKGROUND

This research studies a medical staff scheduling problem, which includes government regulations and hospital regulations (hard constraints) and the medical staff's preferences (soft constraints).

OBJECTIVE

The objective function is to minimize the violations (or dissatisfaction) of medical staff's preferences.

METHODS

This study develops three variants of the three-phase modified bat algorithms (BAs), named BA1, BA2, and BA3, in order to satisfy the hard constraints, minimize the dissatisfaction of the medical staff and balance the workload of the medical staff. To ensure workload balance, this study balances the workload among medical staff without increasing the objective function values.

RESULTS

Based on the numerical results, the BA3 outperforms the BA1, BA2, and particle swarm optimization (PSO). The robustness of the BA1, BA2, and BA3 is verified. Finally, conclusions are drawn, and directions for future research are highlighted.

CONCLUSIONS

The framework of this research can be used as a reference for other hospitals seeking to determine their future medical staff schedule.

Utilization of heat-mapping tools to match a resident staffing template to emergency department arrival patterns

OBJECTIVES

Academic emergency departments (ED) rely on a steady flow of patients to provide residents with good clinical training. Understanding institutional volume patterns allows training directors to create a schedule that maximizes learning opportunities while also adequately staffing the ED. Our primary objective of this study was to utilize heat-mapping software to optimize resident staffing in an academic ED.

METHODS

Heat-mapping tools within Microsoft Excel were utilized to overlay ED patient arrival patterns on top of the potential patients per hour based on published productivity data for trainees and historical averages for advanced practice providers at our institution. Time frames for under- and overstaffing were identified and color-coded. This analysis informed a revised schedule template and the same heat-mapping process was used to determine the appropriateness of the revised staffing template.

RESULTS

The heat map for the original schedule template revealed understaffing in the morning and overstaffing the rest of the day. Informed by these findings, schedule adjustments were made. There was no net increase in the number of resident or advanced practice provider coverage hours. Prior to implementation, the ED was understaffed by 5% or more during 18.4% of operating hours. Following changes to the staffing template, only 5.9% of operating hours were understaffed (p < 0.001). Furthermore, significant understaffing (20% or more) decreased from 16.6% to 3.1% (p < 0.001).

CONCLUSIONS

Novel use of heat-mapping software has the potential to successfully match ED patient arrival patterns to an optimal resident staffing template. Future directions include incorporation of variable resident productivity to account for fatigue as the shift progresses.