Influence of Management of Information Systems’ Capacity Utilisation on Healthcare Facilities. A Case of Healthcare Facilities in Homabay Sub-County, Kenya

Authors

  • Ouru John Nyaegah Coordinator School of Open and Distance Learning, University of Nairobi, P.O. Box 30197-00100, Nairobi-Kenya

Keywords:

Influence, Institutional Capacity, Utilization, Health Information Systems, Employee Capacity, Infrastructure, and, Healthcare Workers

Abstract

The study aimed at determining the influence of institutional capacity on utilization of Sub-County Health Information System in health facilities in Homabay sub-county. Utilization of Health Information System was raising concerns both locally and globally. This is in view of the role of health managers to take up proactive leadership in demand for and use of data for decision making. The system also helps decision makers to detect and control emerging and endemic health problems, monitor progress towards health goals and promote equity if well utilized.  Low utilization of health information system by healthcare workers in health facilities was identified as a challenge in many developing countries including Kenya. This problem was identified in Homabay Sub-County where the health care workers did not fully utilize information from Sub-county Health Information System despite enormous resources that are provided to help in its implementation. The specific objectives were, firstly to determine how employee capacity influence utilization of district health information system in health facilities in Homabay sub-county. Secondly to establish how availability of funds influence utilization of district health information system in health facilities in Homabay sub-county. Thirdly to assess the extent to which size of healthcare facility influence utilization of district health information system in health facilities in Homabay sub-county. The study was conducted in Homabay sub-county, between July and September 2019.

It adopted descriptive research design where both quantitative and qualitative data was collected using questionnaires from a total of 20 District Health Information System managers and 124 users in public health facilities in Homabay Sub County. The researcher adopted stratified random sampling to select the respondents since the population was heterogeneous consisting of different cadres of healthcare workers. Quantitative data was analyzed using both descriptive and inferential statistics where Pearson-Product Moment Correlation was applied to be able to determine the relationship between institutional capacity and utilization of Sub-county Health Information System in Homabay Sub-County. Validity of the research instruments was obtained through piloting and expert evaluation. Reliability was tested using a test retest method. The findings of the study included a negative correlation between employee capacity and utilization of Sub-county health information system, with values being significant for users (-0.479) and insignificant for managers (-0.349). Inadequate employee capacity was mainly due to lack of trainings and skills while availability of funds was mainly due to inadequate staff and lack of infrastructures to support and encourage the use of Sub-county Health Information System. In view of the findings of the study, the researcher gave recommendations. This was necessary as it ensured that both managers had the capacity and skills to effectively utilize Sub-county Health Information System in their facilities. The study came up with suggestions for further studies on the subject so that the findings in this study can be confirmed and verified by many more studies.

References

. WHO. (2011). The benefits of health information technology: A review of the recent literature shows predominantly positive results. Health Affairs, (3), 464-471.

. Kimani, J., & Namusonge, S. (2015). Factors Affectinng the Utilization of Health Information Technology Projects in Nairobi County. Strategic Journals, Vol. 2 (57), pp 286-315.

. Karuri, et al. (2014). DHIS2: The tool to Improve Health Data Demand and Use in Kenya. Journal of Health Informatics in Developing Countries; Vol. 8 No. 1.

. Rahimi, B., Vimarlund, V., & Timpka, T. (2009). Health Information System Implementation: A Qualitative Meta-analysis. J Med Syst , 33:359–368, DOI 10.1007/s10916-008-9198-9.

. Dieleman, M., Cuong, P. V., Anh, L. V., & Martineau, T. (2003). Identifying Factors for Job Motivation of Rural Health Workers in North Viet Nam. Human Resources for Health, 1:10.

. Kijsanayotin, B., Pannarunothai, S., & Speedie, M. S. (2008). Factors Influencing Health Information Technology Adoption in Thailand’s Community Health Centers: Applying the UTAUT model. Elsevier, doi:10.1016/j.ijmedinf.2008.12.005.

. Littlejohns, P., Wyatt, J. C., & Garvican, L. (2003). Evaluating Computerised Health Information Systems: Hard Lessons Still to be Learnt. BMJ 2003;326:860–3.

. Garrib, A., Stoops, N., McKenzie, A., Dlamini, L., Govender, T., Rohde, J., & Herbst, K. (2008). An Evaluation of the District Health Information System in Rural South Africa. S Afr Med J; 98: 549-522.

. Chaulagai, C. N., Moyo, M. C., Jaap, K., Moyo, H. B., Sambakunsi, T. C., Khung, F. M., & Naphin, P. D. (2005). Design and Implementation of a Health Management Information System in Malawi: Issues, Innovations and Results. London: Oxford University Press in Association with The London School of Hygiene and Tropical Medicine, doi:10.1093/heapol/czi044.

. RBHS, R., USAID, U., & MOHSW, L. (2012). Performance of Routine Health Information System Management in Liberia: PRISM Assessment.

. Nutley, T., & Reynolds, H. W. (2011). Improving the use of health data for health system strengthening. Global health action.

. Christensen A., & Hawley, K. M. (2012). Understanding barriers to evidence-based assessment:Clinician attitudes toward standardized assessment tools. Journal of ClinicalChild &Adolescent Psychology, 39(6), 885-896

. Israel, G.D. (1992). Sampling the Evidence of Extension Program Impact: Program Evaluationand Organizational Development. IFAS, University of Florida.

. Kothari C. (2009). Research Methodology: Methods and Techniques. New Age International.

. Gordana, B., Kovic, I., Zombori, D., Lulic, I., & Petrovecki, M. (2005). Nurses’ attitudes towards computers: cross sectional questionnaire study.Croat Med J, 46(1), 101-104.

. Heater, J. C., & Stefik, M. J., (1989). U.S. Patent No. 4,814,552. Washington, DC: U.S. Patent and Trademark Office.

. Jasperson, J. S., Carter, P. E., & Zmud, R. W. (2005). A comprehensive conceptualization of post-adoptive behaviors associated with information technology enabled work systems. Mis Quarterly, 29(3), 525- 557.

. Galbraith, J.R., (1974). Organization Design: An Information Processing View. Interfaces 4, (1974): 28– 36.)

. Omar, M.A., & Charimari. L.S. (2005). The District Health Information System and its Potential in the Management of District and Rural Hospitals. Nuffield Institute for Health; University of Leeds, United Kingdom. Journal of world Hospitals, 30: 3.).

. Mengiste, S. A. (2010). Analysing the Challenges of IS implementation in Public Health Institutions of a Developing Country: The Need for Flexible Strategies. Journal of Health Informatics in Developing Countries, Vol 4, No, 1 ment 45, (2000): 317–30).

. Karuri (b), J., Waiganjo, P., & Orwa, D. (2014). Implementing a Web-based Routine Health Information System in Kenya: Factors Affecting Acceptance and Use. International Journal of Science and Research, Volume 3 Issue 9.

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Published

2020-04-30

How to Cite

Nyaegah, O. J. . (2020). Influence of Management of Information Systems’ Capacity Utilisation on Healthcare Facilities. A Case of Healthcare Facilities in Homabay Sub-County, Kenya. International Journal of Sciences: Basic and Applied Research (IJSBAR), 51(2), 58–68. Retrieved from https://gssrr.org/index.php/JournalOfBasicAndApplied/article/view/10617

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