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Human resource management: new approach to nurse scheduling by considering human error

机译:人力资源管理:考虑人为错误的护士调度新方法

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Human error is a critical concern in healthcare systems from primary care clinics to operating rooms in hospitals. Prevention or reduction of the chance of occurrence of such errors through increasing human reliabilities is tremendously important and deserves to be focused within personnel scheduling problems in healthcare systems. The present study is to develop a new multi-objective integer mathematical model which includes human errors of nurses to determine optimal shift scheduling of nurses. In addition to medical errors, several constraints in real-world problem including "minimum number of available nurses in each shift", "restrictions on shift rotation for each nurse", and "Minimum and maximum working hours in a week" are also taken into account. Nurses' preference score, allocation costs, penalty cost of violating soft constraints, and human errors are all considered as objectives to be optimized. The multi-objective model, developed in this study, is solved by employing the weighted-sum method. To verify and validate the proposed model, a test problem is also solved. Sensitivity analysis on the model indicates that the solution method can reach acceptable solutions within an acceptable time. The present study is to help decision-makers to achieve optimal scheduling for decreasing costs and improving safety in healthcare systems. Based on this approach, decision makers can totally minimize the number of errors by considering the number of nurses required in each grade as well as proper allocation of them to different work shifts.
机译:从初级保健诊所到医院的手术室,人为错误是医疗系统中的关键问题。通过提高人员可靠性来防止或减少此类错误的发生机会极为重要,应将重点放在医疗保健系统中的人员调度问题中。本研究旨在开发一种新的多目标整数数学模型,该模型包括护士的人为错误,以确定护士的最佳轮班安排。除医疗错误外,还考虑了现实世界中的一些约束,包括“每个班次的可用护士人数最少”,“每个护士的班次轮换限制”以及“一周的最小和最大工作时间”帐户。护士的偏爱评分,分配成本,违反软约束的处罚成本以及人为错误均被视为要优化的目标。本研究开发的多目标模型是采用加权和方法求解的。为了验证和验证所提出的模型,还解决了测试问题。对模型的敏感性分析表明,求解方法可以在可接受的时间内达到可接受的解。本研究旨在帮助决策者实现最佳计划,以降低成本并提高医疗保健系统的安全性。基于这种方法,决策者可以通过考虑每个年级所需的护士人数以及根据不同的工作班次适当分配护士的数量,来最大程度地减少错误的数量。

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