Health Services Research Centre, Singapore Health Services, Singapore, Singapore.
Health Services and Systems Research, Duke-NUS Graduate Medical School, Singapore, Singapore.
J Nurs Manag. 2018 May;26(4):432-441. doi: 10.1111/jonm.12560. Epub 2017 Dec 26.
We propose a nurse scheduling framework based on a set of performance measures that are aligned with multiple outcome measures. A case study for the emergency department is presented.
A total of 142,564 emergency department attendances over 1 year were included in this study. Operational requirements, constraints and historical workload data were translated into a mixed-integer sequential goal programming model, which considers the following outcome measures: (1) nurse-patient ratios; (2) number of favourable/unfavourable shifts; and (3) dispersion of rest days. Computational studies compared the performance of the mixed-integer sequential goal programming results with manually generated historical nurse schedules.
The maximum nurse-patient ratio deviation against the target was approximately 10% compared to 47% generated by the historical rosters (a 10% deviation translates to approximately two nurses). An on-line decision support system, which integrates shift preferences, staff databases and a workload forecasting module, was also developed.
A decision support system based on the mixed-integer sequential goal programming modelling framework was proposed. The application of the model in a case study for an emergency department demonstrated improvements over existing manual scheduling methods.
This study demonstrates a mathematical, programming-based decision support system, which allows for managerial priorities and nurse preferences to be jointly considered in the automatic generation of nurse rosters.
我们提出了一个基于一组与多种结果衡量标准相匹配的绩效指标的护士排班框架。本文介绍了一个急诊科的案例研究。
本研究共纳入了 142564 例急诊科就诊患者，为期 1 年。将运营需求、约束条件和历史工作量数据转化为混合整数序列目标规划模型，该模型考虑了以下结果衡量标准：（1）护士与患者比例；（2）有利/不利班次的数量；和（3）休息日的分布。计算研究将混合整数序列目标规划结果的性能与手动生成的历史护士排班进行了比较。
与手动排班相比，最大护士与患者比例偏差约为 10%，而历史排班为 47%（10%的偏差约相当于两名护士）。还开发了一个在线决策支持系统，该系统集成了班次偏好、员工数据库和工作量预测模块。
提出了一个基于混合整数序列目标规划建模框架的决策支持系统。该模型在急诊科的案例研究中的应用表明，它优于现有的手动排班方法。
本研究展示了一个基于数学、编程的决策支持系统，该系统允许在自动生成护士排班时共同考虑管理优先级和护士偏好。
