Publication:

Healthcare providers’ perceptions of the impact of electronic medical records on diabetes care at Nakuru county referral and teaching hospital, Nakuru county: an ordinal regression analysis of cadre and experience in Kenya

Background Electronic Medical Records (EMRs) are central to improving diabetes care, yet provider perceptions remain critical to their effective adoption, particularly in resource-limited settings. This study assessed healthcare providers’ perceptions of the impact of EMRs on diabetes care and examined the influence of professional cadre and experience in Kenya.

Methods A cross-sectional study was conducted among 119 healthcare providers. Perceptions of EMR impact were measured using a Likert scale. Chi-square tests identified associations between socio-demographic variables and perception outcomes. Variables meeting inclusion criteria were entered into an ordinal logistic regression model to determine independent predictors. Model fit was assessed using likelihood ratio tests and Nagelkerke pseudo-R².

Results Overall, 86.5% of respondents reported positive perceptions of EMR impact on diabetes care (73.9% agree; 12.6% strongly agree). Chi-square analysis showed significant associations between perception and cadre (χ²=33.517, p<0.001), while gender, age and education were not significant. The ordinal regression model was statistically significant (χ²(5)=29.246, p<0.001) and explained 27% of variance. Nurses (OR=27.03, 95% CI: 6.27–116.51, p<0.001) and doctors (OR=19.61, 95% CI: 3.37–114.12, p=0.001) were significantly more likely to report positive perceptions compared to clinical officers. Although not statistically significant, shorter employment duration showed a consistent trend toward more favourable perceptions.

Conclusions Healthcare providers in Kenya demonstrate overwhelmingly positive perceptions of EMRs in diabetes care, with cadre emerging as a key determinant. Tailored training and cadre-specific system optimization are recommended to enhance adoption and bridge experience-related gaps, supporting scalable EMR implementation in similar settings.