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KeMU Digital Health Repository

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Welcome to African Digital Health Library community Kenya node. The African Digital Health Library (ADHL), Kenya node, makes available health-related content ranging from theses, dissertations,from the School of Medicine, Kenya Methodist University. ADHL currently operates in 5 Sub Saharan African Countries and is supported by the medical librarians. It is a collaborative effort among medical librarians at major universities in sub-Saharan Africa Kenya, Mali, Nigeria, Zambia and Zimbabwe and is funded by the Office of Global AIDS/US Department of State and the U.S. National Library of Medicine / National Institutes of Health. The Kenyan node is managed through the Kenya Methodist University Library and The University of Nairobi.

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    Integration of Health Management Information Systems in Health Care Organizations in Kenya
    (KeMU, 2018-10) Kyalo, Caroline Kawila
    Global attention to health systems strengthening has led to different quality improvement approaches in developed and developing countries. The awakening realization that information is critical for health systems functioning has directed most countries towards strengthening their existing information systems or developing new ones. Despite efforts to improve on existing information systems, fragmented information systems have emerged hindering the efforts of addressing the concern of integrating health management information systems. An integrated health management information system has greater benefits for example enhancing easy retrieval of data, timely information sharing and evidence based decision making. The purpose of this study was to prescribe a model that will facilitate the Integration of Health Management Information Systems in healthcare organizations. The objectives of the study were; to establish whether the organization factor; technical factor; behavioral factor of care providers and leadership style influenced integration of Health Management Information System in healthcare organizations in Kenya. The study was anchored on the System theory but underpinned on performance of routine information system framework. Guided by the philosophy of logical positivism and interpretivism. A mixed method research design involving quantitative and qualitative designs was used to obtain information from three counties; Kiambu (peri-urban), Kitui (rural) and Mombasa (urban) in Kenya. A study population was 479 public healthcare organization. Multi-stage sampling technique was used to select organizations that participated in this study. A sample size of 144 public healthcare organizations was drawn using the Kothari formula of calculating sample size. In each healthcare organization selected, two self-administered questionnaires were used to collect data from 243 respondents who were either the in charges or health records officers. Data was analyzed using SPSS version 21 and summary statistics such as mean scores, standard deviation and inferential statistics namely correlation and regression results were used to present the data. The study results provided statistical evidence that a positive and significant relationship exists between the organization factor (r=.472**, P˂.005), technical factor (r=.683**, P˂.005), behavioral factor (r=.507**, P˂.005) and leadership style (r=.731**˂.005) and integration of health management information systems. Technology adoption was found to significantly moderate the relationship between technical factor and integration of health management information systems, while need for information timeliness was found to moderate organization factor, technical factor and leadership style. The integration of HMIS model proposed in this study was found to be fit because it explained up to (r2 =.648) total variation in the integration of HMIS. The remaining beat of .352 is explained by the statistical error term. The std. error of .227 shows the model regression line deviates from the line of best fit. The study concluded that leadership style was quite significant in the efforts towards integration of HMIS and information timeliness was a very good moderator between the operation factors and integration of HMIS. The study recommends that; i) health system managers adopt the proposed design of data collection strategy emerging from the study findings ii) The extracted integration of HMIS model from the study findings composed of transformational leadership style, systems interoperability moderated by information timeliness and enhanced information culture should be adopted. Further studies can be done after the model is adopted to evaluate its performance.
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    Transforming the Health Sector in Kenya by Adopting Integrated Health Management Information System
    (International Journal of professional practice (IJPP), 2019-05) Kyalo, Caroline Kawila; Odhiambo-Otieno, George.W.
    Achieving better health outcomes is every health organization’s dream.Several health management strategies have been developed;a key one being production of quality information for purposes of informed decision making.Most public health facilities in Kenya are by large extent using manual health information systems,which are cumbersome and fragmented leading to delayed or uninformed decision making. Five key drivers are viewed to influence Integration of Health Management Information System(IHMIS): i) organizational factor, ii) technical factor, iii) behavioral factor, iv) adopted leadership style and v) operationalization of the functions of management.The purpose of the study was to prescribe an ideal design of an IHMIS.A mixed method research design was adopted to determine the significance of the drivers identified in influencing the IHMIS.Both semi-structured questionnaire and a key informant guide were used to collect data that was analyzed and reported using mean scores,correlation and regression analysis.Integration of HMIS was found to positively and significantly correlate with organizational factor,technical factor,behavioral factor,leadership style adopted and operationalization of the functions of management.The study also showed that all variables accounted for total variation in the integration of HMIS.In a multiple regression analysis,the study found the model valid.However only 4 out of 5 of the predictor variables(operationalization of management functions,behavioral factor,technical factor,leadership style adopted) were good in explaining total variations in the integrated HMIS in Kenya. This implies that the more these variables are improved the better it gets in achieving integration of HMIS which is achievable if all the study variables are taken into account.The study suggests that the ideal design of an integrated HMIS would be in the form of an automated web based system in each health organization as opposed to the current manual system.IHMIS will be more successful,if integration within a single health organization is successful.The study recommends a central database for each level of care.