Determinants of Health Information Systems Usability in Public Hospitals in Kenya

Determinants of Health Information Systems Usability in Public Hospitals in Kenya | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL Healthcare Technology Letters This is a preprint and has not been peer reviewed. Data may be preliminary. 12 May 2026 V1 Latest version Share on Determinants of Health Information Systems Usability in Public Hospitals in Kenya Author : Nixon Amuomo 0000-0001-9942-5276 [email protected] Authors Info & Affiliations https://doi.org/10.22541/authorea.15003188/v1 14 views 7 downloads Contents Abstract Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Public hospitals in Kenya are rolling out health information systems (HIS), such as Kenya Electronic Medical Records (KenyaEMR) and the District Health Information System (DHIS2), to support the national government’s Universal Health Coverage (UHC) objectives. Information and Communication Technology (ICT) improves transparency, efficiency, and effectiveness in service delivery. This study sought to assess the usability of digital health information systems and identify any gaps in the health information systems lifecycle in public hospitals in Kenya. The researcher purposively selected public hospitals in Migori County due to time and financial constraints. The study adopted a survey design, using questionnaires as the data collection instrument. A drop-and-pick approach was used, with 35 questionnaires circulated and 30 completed and returned, yielding a response rate of 85.71%. The study used a five-point Likert scale with 40 statements on HIS use in the current facility environment. Descriptive statistics were analyzed using SPSS software. The findings indicate that the top three consumers of health information systems data in public hospitals in Kenya are the health records officers (HROs) at 26.7%, the outpatient department at 20.0%, and the revenue department at 13.3%. According to stakeholders, the most pressing issue is compatibility between systems and physicians’ tasks. Information & Authors Information Version history V1 Version 1 12 May 2026 Collection Healthcare Technology Letters Keywords decision support systems medical information systems assisted living biocybernetics biomedical measurement blind source separation brain electroencephalography medical signal processing neural nets patient monitoring time-frequency analysis data integration decision making decision support systems medical information systems decision support systems medical information systems assisted living biocybernetics biomedical measurement blind source separation brain electroencephalography medical signal processing neural nets patient monitoring time-frequency analysis Authors Affiliations Nixon Amuomo 0000-0001-9942-5276 [email protected] Rongo University, Rongo, Kenya View all articles by this author Metrics & Citations Metrics Article Usage 14 views 7 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Nixon Amuomo. 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