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基于排队论的患者和员工满意度综合模型。

An Integrated Model of Patient and Staff Satisfaction Using Queuing Theory.

出版信息

IEEE J Transl Eng Health Med. 2015 Feb 6;3:2200110. doi: 10.1109/JTEHM.2015.2400436. eCollection 2015.

DOI:10.1109/JTEHM.2015.2400436
PMID:27170899
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC4848074/
Abstract

This paper investigates the connection between patient satisfaction, waiting time, staff satisfaction, and service time. It uses a variety of models to enable improvement against experiential and operational health service goals. Patient satisfaction levels are estimated using a model based on waiting (waiting times). Staff satisfaction levels are estimated using a model based on the time spent with patients (service time). An integrated model of patient and staff satisfaction, the effective satisfaction level model, is then proposed (using queuing theory). This links patient satisfaction, waiting time, staff satisfaction, and service time, connecting two important concepts, namely, experience and efficiency in care delivery and leading to a more holistic approach in designing and managing health services. The proposed model will enable healthcare systems analysts to objectively and directly relate elements of service quality to capacity planning. Moreover, as an instrument used jointly by healthcare commissioners and providers, it affords the prospect of better resource allocation.

摘要

本文探讨了患者满意度、等待时间、员工满意度和服务时间之间的关系。它使用了多种模型,以实现对体验和运营医疗服务目标的改进。患者满意度水平是使用基于等待时间(等待时间)的模型来估计的。员工满意度水平是使用基于与患者共度的时间(服务时间)的模型来估计的。然后提出了一种患者和员工满意度的综合模型,即有效满意度水平模型(使用排队论)。这将患者满意度、等待时间、员工满意度和服务时间联系起来,连接了两个重要的概念,即护理提供中的体验和效率,从而在设计和管理医疗服务方面采用了更全面的方法。所提出的模型将使医疗保健系统分析师能够客观和直接地将服务质量要素与能力规划联系起来。此外,作为医疗保健委托人和提供者共同使用的工具,它为更好的资源分配提供了前景。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/71d8/4848074/c32fd414a52c/komas10-2400436.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/71d8/4848074/0e1c1f4cf993/komas1-2400436.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/71d8/4848074/a2b2fee3362e/komas8-2400436.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/71d8/4848074/511cc49babd2/komas9-2400436.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/71d8/4848074/c32fd414a52c/komas10-2400436.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/71d8/4848074/0e1c1f4cf993/komas1-2400436.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/71d8/4848074/046654bfc258/komas2-2400436.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/71d8/4848074/c107e224e9bc/komas3-2400436.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/71d8/4848074/f9216087d691/komas6-2400436.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/71d8/4848074/ac2a815c2c25/komas7-2400436.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/71d8/4848074/6eebb36bc418/komas4-2400436.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/71d8/4848074/7778aec476c2/komas5-2400436.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/71d8/4848074/a2b2fee3362e/komas8-2400436.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/71d8/4848074/511cc49babd2/komas9-2400436.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/71d8/4848074/c32fd414a52c/komas10-2400436.jpg

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