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<p>This paper addresses the issue of nursing workload imbalances in healthcare environments with highly stochastic patient demand. The core objective is to explore the effects of shift design and scheduling decisions on nursing workload in emergency departments, utilizing the German University Hospital Augsburg as a case study. By introducing a mixed-integer optimization model, we provide a method to balance hourly workload over a typical one-month scheduling horizon, measured as an acuity-adjusted patient-to-nurse ratio, where acuity is represented at the level of care activities rather than individual patients. Our model accounts for real-world parameters including staff details, qualifications, work preferences, and extensive patient data. We further explore the potential impact of various shift designs on the desired target level of workforce balance and care quality. The results provide actionable insights into strategic staffing decisions, their effects on nursing workload, and potential solutions to healthcare staffing challenges. The findings also form the basis for future research in workforce optimization in healthcare.</p>

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Shift design and assignment optimization to balance nurse workloads in the emergency department

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月25日

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  • Jan Schoenfelder,
  • Sebastian Kraul,
  • Steffen Netzband

摘要

This paper addresses the issue of nursing workload imbalances in healthcare environments with highly stochastic patient demand. The core objective is to explore the effects of shift design and scheduling decisions on nursing workload in emergency departments, utilizing the German University Hospital Augsburg as a case study. By introducing a mixed-integer optimization model, we provide a method to balance hourly workload over a typical one-month scheduling horizon, measured as an acuity-adjusted patient-to-nurse ratio, where acuity is represented at the level of care activities rather than individual patients. Our model accounts for real-world parameters including staff details, qualifications, work preferences, and extensive patient data. We further explore the potential impact of various shift designs on the desired target level of workforce balance and care quality. The results provide actionable insights into strategic staffing decisions, their effects on nursing workload, and potential solutions to healthcare staffing challenges. The findings also form the basis for future research in workforce optimization in healthcare.