Factors Associated with Successful Electronic Medical Records (EMR) Implementation in Kenya
Owiso, George Otieno
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Background: Implementation of Electronic Medical Records (EMR) remains a relatively recent development in response to emerging needs to manage large volumes of data that are often associated with longitudinal care. Kenya’s HIV program created a large demand for responsive data management systems that guarantee availability and quality of data necessary to manage patients and monitor the program. Subsequent support for national rollout of EMRS resulted in over 640 new implementations in public health facilities. Such large national rollouts can be expensive, especially in the context of limited resources and competing needs to focus resources on achievement of treatment targets. The utility of investments in EMRs depends upon successful uptake leading to complete and high-quality data. Systems and processes for training, supervising, and supporting health care workers tasked with using EMRs have an important role in successful uptake and use of EMRs. This study explored the hypothesis that the type of the health worker trained as a facility EMR champion and the service delivery implementing partner could be major determinants of implementation outcomes. Methods: The researcher conducted a retrospective descriptive study to determine which factors were associated with data quality and the proportion of legacy paper records successfully migrated into the EMR. The study focused on 344 facilities supported by the International Training and Education Center for Health (I-TECH) between 2012 and 2016, which implemented the KenyaEMR system. Our outcomes of interest were data quality and proportion of records successfully migrated into the EMR. Key predicators of interest were availability and type of EMR champion and implementing partner. Key confounding variables were type of facility, number of patient records in legacy paper systems (volume), age of the champion, region of implementation, and the implementation mode. We conducted descriptive analyses of characteristics of EMR champions and health facilities, as well as exploratory bivariable and multivariable analyses using logistic and linear regression to explore the association between the outcomes and predictors of interest. We considered measures of association to be statistically significant at the p<0.05 level. All the analyses were conducted in STATA version 14. Results: Of 344 facilities where KenyaEMR was implemented, 307 reported migration statuses; of these, 169 had a trained champion. 94 facilities out of the 344 had records of data quality audit. This study found no significant association between the type of champion and the proportion of data migrated at the facility. Similarly, there was no association between the type of champion and the quality of data in the facilities. However, there was significant association between the current implementation mode and data migration status. Facilities using the system as point of care were more likely to report above median data migration compared to those using it as a retrospective data entry implementation (OR 13.2; CI: 1.61-107.8; p=0.016). Similarly, by comparing the partner with highest number of facilities to all other partners, there was significant association between implementing partners and quality of data after adjusting for patient volume, champion age, facility type and region. (coef. 23.9; 95% CI: 10.67-37.26; p=0.001). Regional disparities tended to follow the implementing partners within those regions. The study concluded that champion characteristics other than age and type could influence implementation outcomes. Structural factors (i.e. the partner supporting the facility) appeared to have significant influence on outcomes. There is a need to study those partner and health system characteristics to understand the variations and specific predictors of EMR outcomes.
- Global health