A Mobile Based Electronic Medical Records System To Improve HIV Prevalence Estimates Among Most At Risk Populations In The Coast Region, Kenya

Fellow's Name: 

Stephen Omondi

Fellows Track: 

Health Informatics

Fellows Cohort: 

First Cohort 

 

Kenya has a severe, generalized HIV epidemic, but in recent years, the country has experienced a notable decline in HIV prevalence, attributed in part to significant behavioral change and increased access to ART (FHI, 2010a). Kenya has a large number of qualified, unemployed health care workers. The key to success will be developing effective mechanisms to engage these trained staff. In addition, efforts to employ auxiliary staff, such as adherence counselors and outreach workers, are a high priority.
In the Coast region this epidemic seems to be aggravated by the concentration of tourist destinations, the illicit drug problem and the lack of formal employment avenues. Young Kenyans get into prostitution and drug abuse as a result of desperation and the massive gap between the rich and the poor.
This project aims at improving HIV prevalence estimates among Most at Risk Populations (MARPS) in the Coast Region, Kenya by linking the existing Health Management Information System to a mobile based Electronic Medical Records system and also redesign it so that it can speedily generate reports. The MARPs indicators vary slightly from region to region and therefore the project extensively use existing documentation to accurately identify the indicators necessary to estimate prevalence.
The indicators identified are then designed into a form that is the basis of a web based electronic medical records (EMR) system that captures information using Nokia Data Gathering (NDG) software running on cell phones . The cell phones are remotely linked to a database which is hosted  by a secure and versatile server.
The use of cell phones and NDG aimed at circumventing the inherent problems that are associated with paper based systems while at the same time breaking down barriers that are location dependent.