Factors influencing adoption of electronic medical records amongst private dental clinics in Nairobi and Mombasa Counties, Kenya

Abstract

Despite the recognized benefits of Electronic Medical Records (EMRs) in improving healthcare efficiency, data management, and continuity of patient care, adoption within Kenya’s private dental sector remains inconsistent and insufficiently understood. This study investigated the factors influencing EMR adoption among private dental clinics in Nairobi and Mombasa Counties, Kenya. Guided by the Diffusion of Innovations (DOI) theory and the Technology Acceptance Model (TAM), the study examined the association between system-related, organizational, and policy/environmental factors and EMR adoption and integration within routine dental practice. The study employed a quantitative analytical cross-sectional design among registered private dental clinics in Nairobi and Mombasa Counties. Data were collected using a structured online questionnaire administered to respondents involved in EMR implementation and management. Data were collected using a structured online questionnaire and analyzed using chi-square tests, Pearson correlation, multiple regression, and ordered logistic regression analysis. The findings demonstrated significant associations between EMR adoption and clinic location (χ² = 34.107, p < 0.001), clinic size (χ² = 44.402, p < 0.001), and clinic ownership (χ² = 22.337, p = 0.001). Correlation analysis revealed that organizational factors had the strongest positive relationship with EMR adoption (r = 0.385, p < 0.01), followed by system-related factors (r = 0.251, p < 0.01), while policy and environmental factors demonstrated no statistically significant relationship with EMR adoption (r = -0.054, p > 0.05). Multiple regression analysis identified organizational readiness as the only statistically significant predictor of EMR adoption (β = 0.278, p < 0.001). Similarly, ordered logistic regression analysis showed that organizational readiness significantly increased the likelihood of higher levels of EMR adoption (β = 1.411, p < 0.001), while demographic and policy/environmental variables remained statistically insignificant. Qualitative responses suggested that EMR implementation was perceived to be more integrated in Nairobi than in Mombasa, although the contextual factors underlying these differences were beyond the scope of the present study. The study concludes that organizational readiness—including leadership support, workflow integration, resource availability, technical support, and staff training—was more strongly associated with EMR adoption than system-related or policy/environmental factors. The study recommends that private dental clinics in Kenya to strengthen organizational readiness through leadership engagement, continuous staff training, workflow integration, and structured technical support to promote sustainable EMR adoption.