Description of Session
Globally, health programs are increasingly replacing manual systems with open source Electronic Medical Record (EMR) Systems. EMRs can provide efficiencies in documentation and retrieval of clinical records for individual patient care. This in turn translates to faster and better quality of care. Additionally, EMRs can simplify aggregation of patient records into routine reports, which health facilities are required to submit to a national Monitoring and Evaluation (M&E) system. In Kenya, KenyaEMR, built on OpenMRS, is the preferred EMR. While the Entity–Attribute–Value (EAV) data model used by the OpenMRS platform is great for clinical care, it is not optimised for retrieval of aggregate reports and custom reports. This presents a challenge to users who need to generate aggregate and custom reports, especially health facilities that handle data for large numbers of patients. The process for generating these reports can be time consuming and frustrating. OpenMRS developers agree that flattening the EAV database can increase the speed of running reports from the EMRs, and literature describes how to manually flatten the OpenMRS database to gain these efficiencies. However, the CDC-funded Kenya HMIS-II project has gone a step further and automated this process. The innovative process automates the Extraction Transformation and Loading (ETL) of data from the EAV database to flat tables that can be used for faster aggregate and custom reporting. This success resulted in greatly increased reporting for the 325 facilities in Kenya that use OpenMRS. This session will highlight the steps that were taken to increase reporting efficiencies in OpenMRS and how this increased uptake and use of the EMR and provided time-savings for frontline heath workers during routine reporting. This approach has been shown to be easily scalable and re-usable by various implementers of OpenMRS in other countries and is available for adoption by developers.
