Determinants of Use of Electronic Medical Records Systems to Deliver Hiv Care in Kenya

Abstract

Background: The increasing application of ICT to manage these countries’ Patient level health information systems (HIS) is expected to improve efficiencies, leading to availability of quality health information for clinical decision support, monitoring, evaluation and delivery of healthcare services and programs. In 2012, Kenya with the aid of its partners (USAID, PEPFAR and CDC) initiated the process of adoption and implementation of a various Electronic Medical Records Systems (EMRS) like IQ -Care, Kenya EMR and C Pad just but to name few as Patient level HIS for-HIV prevention and Care that will facilitate management of patient level health information for evidence-based decision making (I-TECH 2015). Problem: The process of adoption and implementation of Electronic Medical Records Systems (EMRS) in Kenya as Patient level HIS for HIV prevention care and treatment was initiated in 2012 (I-TECH, 2015). Unfortunately, these systems remain underutilized or all together abandoned, yet EMRS are known to revolutionize health care management (Ackerman et al., 2010). For maximum benefits to be reaped from this implementation, these EMRS needs to be optimally utilized across all categories of targeted users. Objective: Enhance knowledge on health IT use, by determining critical influencers of EMRS use to deliver HIV care in Kenya. Methodology: We reviewed works on IT implementation concepts and frameworks, aiming at determining the most appropriate model to adopt for this study. Majorly we focused on the theories and models’ strengths and weaknesses in selected case studies where employed in IT implementation works, their results and deductions drawn. We therefore adopted and validated UTAUT model for this work. Pretested questionnaires to collect quantitative data administered and data analyzed using SmartPLS 3 to determine the relationships between Independent and Dependent variables through the SEM modelling technique. Results: Findings point to multiple user-associated, institutional, social and behavioral factors were the critical determinants of successful EMR use. For the full data set model PE, EE, SI, and FC account 58.8% BI change to Use EMRS which in turn accounts 56.2% in actual use of EMRS Healthcare workers in HIV Care delivery settings. Gender affects the effect of PE on BI such that the association was high for women. The effect of EE on BI was moderated by Gender such that

effect was stronger for women than men. The effect of FC on BI to use of EMRS was moderated by Experience, such that the effect was strongest for the highly experienced HCWs. Conclusion: The study confirms that UTAUT model is applicable in this context but not all the factors currently included in UTAUT explain use of HIS in Kenya. This results also tend to agree with Karuri et al. (2017) that that the strength of factors that determine acceptance and use of health IT varies across different health workers' categories. These findings therefore bring new insights to technology adoption that is not in tandem with the works of Davis (1989) hence a new contribution to the current body of knowledge.