关于巴基斯坦公立或私立医院的效果和效率的研究

Noman Islam, Muhammad Usman Raees, Darakshan Syed
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引用次数: 0

摘要

医院在人类生活中扮演着非常重要的角色。人们都在寻求优质、及时的医疗保健设施。本文讨论的是医疗服务的效率和效果。我们需要找出提高政府医院和私立医院绩效的因素和机制。本研究旨在找出巴基斯坦公立和私立医院所需的重要变量,以找到最佳解决方案。本文的主要贡献如下。本文首先讨论了医院管理对卓越技术和卓越人际关系的需求。本文还对决定医院有效性和效率的重要变量的文献进行了定性审查。然后,本文使用不同的问卷对公立和私立医院的有效性和效率进行了公开调查。这些问卷由患者亲属或患者本人在医院就诊时填写。在这个数据集上,本文应用了一种机器学习算法,即随机森林算法,来预测哪种医院类型适合他们,同时考虑到各种变量。这些变量包括:医院服务、入院流程、治疗、医生行为、及时治疗以及员工对 SOP 的了解。数据被分成 75% 的训练数据集和 25% 的测试数据集。使用 Python 库 SK-learn 实现。分类器在测试数据集上的准确率为 96.91%。论文随后确定了对衡量医院有效性和效率贡献最大的变量。该算法还对这些可用于提高医院绩效的特征进行了排名。它还为患者选择医疗机构提供了一个基准。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Study on the Effectiveness and Efficiency of Public or Private Hospitals within Pakistan
Hospitals play a very important role in human lives. People are in search of quality and timely healthcare facilities. This paper talks about the efficiency and effectiveness of healthcare services. One needs to identify the factors and mechanisms to enhance the performance of government and private hospitals. This study aims to find the important variables that were required to find the best solution for public and private hospitals in Pakistan. The major contributions of the paper are as follows. The paper first deliberates on the need for technical excellence as compared to interpersonal excellence for hospital management. It also performs a qualitative review of the literature on the important variables for determining the effectiveness and efficiencies of the hospital. Then, the paper presents a public survey about the effectiveness and efficiency of public and private hospitals using different questionnaires. These questionnaires were filled by the relatives of patients or patients themselves when they visited hospitals. On this dataset, the paper applies a machine learning algorithm i.e., random forest, to predict which hospital type is suitable for them while considering the variables. These variables include; the services of the hospital, admission process, treatment, doctor's behavior, timely treatment, and knowledge of the staff about SOPs. The data was split into 75 % training and 25 % testing dataset. Python’s Library SK-learn was used for implementation. The accuracy of the classifier on the test dataset is 96.91 %. The paper then determines the variables that are contributing the most to the measure of effectiveness and efficiencies of hospitals. The algorithm also ranks these features that can be used to improve a hospital's performance. It also provides a benchmark to the patients in the selection of hospitals for healthcare facilities.
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