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伊朗德黑兰医科大学护理轮班智能模型

Intelligent Model of Nursing Shift in Tehran University of Medical Sciences, Tehran, Iran.

作者信息

Torabi Mashallah, Goodarzi Maryam, Ahmadi Maryam, Hamidi Hamideh, Elmi Samira, Golmah Fatemeh, Mortezaie Samira, Nezari Parisa

机构信息

Research Center for Science and Technology in Medicine, Tehran University of Medical Sciences, Tehran, Iran.

Central Intelligent Secretariats, Desk Service and Office Automation, Tehran University of Medical Sciences, Tehran, Iran.

出版信息

Iran J Public Health. 2022 May;51(5):1125-1133. doi: 10.18502/ijph.v51i5.9427.

Abstract

BACKGROUND

Nurses play a key role in increasing the efficiency of healthcare systems. Given the 24-hour performance of hospitals and the small number of nurses in the field of treatment, it is quintessential to re-shift them in the hospital. This study set out to achieve coherence in nursing shift planning and justice in the order of shifts in hospital.

METHODS

This applied and a developmental study was performed from 2019 to 2020. We used genetic algorithm to provide operational solutions and define flexible shifts and plan nurses' working hours in Yas Hospital, Tehran University of Medical Sciences Hospital, Tehran, Iran.

RESULTS

Based on the selection of each nurse and determining the approved shifts of each ward, the possibility of appropriate planning was provided to determine the required shifts per month and to estimate the needs of each department.

CONCLUSION

Using genetic algorithm and nursing shift in office automation console provides useful tools for managers at all organizational levels, according to which a good balance between the hospital's need for nurse and nurses' demands in different time periods.

摘要

背景

护士在提高医疗系统效率方面发挥着关键作用。鉴于医院的24小时运转以及治疗领域护士数量较少,重新安排他们在医院的班次至关重要。本研究旨在实现护理排班的连贯性以及医院班次安排的公正性。

方法

本应用与发展性研究于2019年至2020年进行。我们使用遗传算法来提供操作方案、定义灵活班次并规划伊朗德黑兰医科大学附属亚斯医院护士的工作时间。

结果

基于每位护士的选择以及确定每个病房的批准班次,为确定每月所需班次和估计每个科室的需求提供了适当规划的可能性。

结论

在办公自动化控制台中使用遗传算法和护理排班为各级管理人员提供了有用的工具,据此可在医院对护士的需求与护士在不同时间段的需求之间实现良好平衡。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3b7f/9643239/3f29d4f8ae9d/IJPH-51-1125-g001.jpg

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