{"title":"Conceptualizing a model for cloud-based hospital management systems for the South African public health sector","authors":"T. S. Magudulela, B. M. Kalema, M. A. Segooa","doi":"10.26697/ijsa.2023.2.5","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":52800,"journal":{"name":"International Journal of Science Annals","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Science Annals","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.26697/ijsa.2023.2.5","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
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.