Khalifa University, Abu Dhabi, UAE.
Aston Business School, Aston University, Birmingham, UK.
Int J Health Care Qual Assur. 2020 Nov 13;ahead-of-print(ahead-of-print). doi: 10.1108/IJHCQA-03-2020-0052.
The aim of this research study is to develop a queue assessment model to evaluate the inflow of walk-in outpatients in a busy public hospital of an emerging economy, in the absence of appointment systems, and construct a dynamic framework dedicated towards the practical implementation of the proposed model, for continuous monitoring of the queue system.
DESIGN/METHODOLOGY/APPROACH: The current study utilizes data envelopment analysis (DEA) to develop a combined queuing-DEA model as applied to evaluate the wait times of patients, within different stages of the outpatients' department at the Combined Military Hospital (CMH) in Lahore, Pakistan, over a period of seven weeks (23rd April to 28th May 2014). The number of doctors/personnel and consultation time were considered as outputs, where consultation time was the non-discretionary output. The two inputs were wait time and length of queue. Additionally, VBA programming in Excel has been utilized to develop the dynamic framework for continuous queue monitoring.
The inadequate availability of personnel was observed as the critical issue for long wait times, along with overcrowding and variable arrival pattern of walk-in patients. The DEA model displayed the "required" number of personnel, corresponding to different wait times, indicating queue build-up.
ORIGINALITY/VALUE: The current study develops a queue evaluation model for a busy outpatients' department in a public hospital, where "all" patients are walk-in and no appointment systems. This model provides vital information in the form of "required" number of personnel which allows the administrators to control the queue pre-emptively minimizing wait times, with optimal yet dynamic staff allocation. Additionally, the dynamic framework specifically targets practical implementation in resource-poor public hospitals of emerging economies for continuous queue monitoring.
本研究旨在开发一种排队评估模型,以评估在新兴经济体中一家繁忙公立医院的非预约制门诊的人流量,构建一个专门用于实施所提出模型的动态框架,以便对排队系统进行持续监测。
设计/方法/途径:本研究利用数据包络分析(DEA)开发了一个排队-DEA 组合模型,用于评估巴基斯坦拉合尔联合军事医院(CMH)门诊不同阶段的患者等待时间,研究期间为 2014 年 4 月 23 日至 5 月 28 日的七周。医生/人员数量和咨询时间被视为产出,其中咨询时间是不可自由支配的产出。两个投入是等待时间和队列长度。此外,还使用 Excel 中的 VBA 编程开发了用于持续排队监测的动态框架。
研究发现人员配备不足是导致等待时间长的关键问题,此外还有过度拥挤和门诊患者到达模式的变化。DEA 模型显示了对应不同等待时间的所需人员数量,表明队列正在形成。
创新性/价值:本研究为一家繁忙的公立医院的门诊部门开发了排队评估模型,该模型适用于所有非预约制和没有预约系统的患者。该模型以所需人员数量的形式提供了重要信息,使管理人员能够预先控制队列,最大限度地减少等待时间,并进行最佳的动态人员配置。此外,该动态框架专门针对资源匮乏的新兴经济体中的公立医院进行了实际实施,以便对排队情况进行持续监测。