Kenya Government launches system to monitor HIV data


The Kenya government through the Ministry of Health has unveiled the National Electronic Medical Record (EMR) Data warehouse to collect and store patient and population data on HIV. The data will help guide and inform policy formulation in Kenya regarding the disease. The system is part of the government’s ambitious plan to eliminate the disease by 2030 and will enable for the tracking of patients as well as geographical monitoring of the epidemic.  

The repository will be interactive allowing users to filter and generate trends with the data. Currently, the repository has 2,000 sites mapped, where patients can access HIV treatment.

Speaking during its launch, Director of Medical Services, Dr. Jackson Kioko said “I cannot overemphasise the urgent need for adoption and scale-up of innovative and cost effective eHealth solutions, customised to meet the needs of the patient, our health service providers and health programme”. Dr. Kioko also launched the revised HIV M&E tools and stressed the need for the health sector to adopt a Unique Patient Identifier to optimize comprehensive care of patients, by ensuring that every individual seeking health services could be uniquely identified.

Such a solution was previously implemented in 2014, through the development of a decision support tool applicable at the district level within Kenya. The District Health Profile Tool uses data integration techniques, which enable district health management teams to monitor the success of policies being implemented. The District Health Profile Tool focuses on better data aggregation and interpretation.

The technology profile of the tool allows remote access from anywhere in the globe. The tool also operates in a question application format. There were 11 fundamental questions generated regarding healthcare facilities related to HIV positive community. This leads to an improved decision review system. The data aggregation is managed from spreadsheets, upon which the tool performs complex analysis, reducing human error.