Factors influencing adoption of electronic health record systems in small private health facilities:a case of Ruiru district, Kiambu county, Kenya
This research sought to explore factors influence of the adoption of Electronic Health Record systems in the provision of health services in Kenya by focusing on small private health care facilities in Ruiru district, Kiambu County. Electronic health records assist in collecting and storage of patients’ data which can be retrieved in summary form to give a brief overview of the patients’ medical history. This study was guided by four research objectives: financial implication, level of ICT knowledge, access to ICT infrastructure and the perception of the health care practitioners. Other variables examined were; years of experience, gender, the duration of computer and Internet use, and the frequency of ICT use among health care practitioners. The study was hinged on the Technology Acceptance Model as the key theoretical model. The web of interrelationships between the study variables was demonstrated by a conceptual framework. The study adopted a descriptive survey design with a target population of 76 health care practitioners in charge of the registered small private health facilities and a sample size of 63 obtained from the Krejcie and Morgan table. A six level questionnaire with both structured and unstructured questions with a 5-point likert scale was used. Pilot testing of the instrument was done using a group of respondents with similar characteristics as the sample population prior to the main research study to verify its validity and reliability. Both quantitative and qualitative data was sought in this research. Quantitative data was coded and analyzed using SPSS version 20. Qualitative data was analyzed by making inferences from the expressions and opinions of the respondents around the variables. The findings were presented in frequency tables and explanation presented in prose. 90% of the small health facilities don’t have an ICT budget in their conception of operation. 92 % of the population is concerned about the high costs of investment while 72 % of the population is either sure or not sure that their costs from EHR implementation will be recovered. 57% of were of the age group that is technology receptive, and 94% had basic computer skills, but 72 % still felt that the EHR systems were still very complex to use. 74% of the population does not use computers in their practice. 81% had concerns about accessibility to training and support while 30% of the non adopters who had implemented the systems then stopped using them complained about their training and post-sale experience with their vendors. 89.4% of the population could use computers as a tool to aid their operation while 88.63% felt that the use of computers was reliable. 67% don’t have sufficient time required to learn the system proficiently. 75% reported concerns about data entry workload: Only 2% reported to have fully implemented EHR systems, 24% are using both paper and electronic systems while 74% are not using any EHR systems. This results show a very low adoption status of the EHR systems. From the findings it can be concluded that Lack of capital resources was analyzed to have the most influence in the adoption while security and privacy concern had the least influence. The main recommendations were for Medical training schools to adapt using of EHRs in training their students, vendors to ensure their software are quality and offer after sale services and for the Government of Kenya to come up with standardization policies for the developers to follow. Further studies can be carried out on comparison between the attitudes of adopters vs. Non-adopters and also to establish if location of health facility has influence on adoption i.e. urban or rural setting.