构想南非公共卫生部门基于云的医院管理系统模型

T. S. Magudulela, B. M. Kalema, M. A. Segooa
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引用次数: 0

摘要

背景与研究目的:在医疗环境中,信息的实时访问是必不可少的,因为它不仅可以帮助医务人员获得充分和及时的信息,而且可以帮助患者更容易地得到服务。农村地区的医院带宽较低,信息技术基础设施差,导致网络间歇性中断,服务提供缓慢。这就需要将医院管理系统(HMS)部署在云环境中,以减少导致服务交付不良的挑战。这项研究的目的是:为南非公共卫生部门开发一个基于云的医疗卫生管理系统模型。材料和方法:本研究确定了南非豪登省的三家公共区市立医院,这些医院已经在使用HMS并使用它们进行数据收集。每所医院最多有50名保健工作者,这构成了来自三家医院的150名人口,从中选择了108名答复者的样本。采用封闭式问卷收集数据,使用SPSS v25进行定量分析。结果表明,该模型的预测能力为60.9% (R2=0.609),除环境因素外,其余构建对基于云的HMS的成功实施都有显著贡献。社会方面的预测能力最高,为60.0% (β=0.600), p=0.001;其次是风险分析和对照组,占41.3% (β=0.413), p=0.009。另一方面,环境因素的预测最小且不显著,为12.3%。结论:这项研究有助于持续呼吁有无缝的医疗保健提供系统。本研究建立的模型扩展了利用技术创新实现医疗服务现代化的研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Conceptualizing a model for cloud-based hospital management systems for the South African public health sector
Background and Aim of Study: Real-time access of information in the healthcare environment is essential, as it not only helps medical personnel to have adequate and timely information, but it also assists patients to be served more easily. Hospitals in rural areas are operating at a low bandwidth and have poor IT infrastructure that causes intermittent networks leading to disruptions and slow service delivery. This necessitates the Hospital Management System (HMS) to be deployed in the cloud environment to reduce the challenges leading to poor service delivery. The aim of the study: to develop a model for cloud-based HMS for the South African public health sector. Material and Methods: This study identified three public district municipality hospitals in Gauteng Province, South Africa, that were already using HMS and used them for data collection. Each hospital had up to 50 healthcare workers, and this formed the population of 150 from the three hospitals, from which a sample size of 108 respondents was selected. Data were collected using a closed-ended questionnaire and analyzed quantitatively using SPSS v25. Results: The results demonstrated that the suggested model has a good prediction power of 60.9% (R2=0.609) and that with the exception of environmental aspects, the rest of the constructs has a significant contribution to the successful implementation of the cloud-based HMS. Social aspects had the highest prediction power of 60.0% (β=0.600) at p=0.001; followed by risk analysis and control with 41.3% (β=0.413) at p=0.009. On the other hand, environmental aspects had the least and non-significant prediction of 12.3%. Conclusions: This study contributes to the ongoing call to have seamless healthcare provision systems. The model developed in this study extends the research of modernizing healthcare provision by leveraging technological innovations.
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