Open Source

Ministries of Health in Low and Middle Income Countries (LMIC) are making or trying to make public health decisions for infectious disease conditions like HIV using data garnered from sentinel events and disease tracking in the community. The process of gathering and aggregating data for these case-based reports for is, in all too often a cumbersome or paper-based process. The Center for Disease Control (CDC) was interested in prototyping and piloting approaches that could improve the efficiency and reliability of case reports in resource-constrained environments. One of their primary goals was to demonstrate how electronic data gathered in the front lines of care could be leveraged to automate and improve the reliability of data within case reports driving public health decisions at regional and country levels. OpenMRS is an open source medical record system platform often used in resource constrained environments. Since OpenMRS is used as an electronic medical record system in several African countries and has been connected to regional or country-level health exchanges, the CDC was interested in building a working solution for electronic case based reporting using OpenMRS and a health information exchange.

Objective:

We demonstrate an architecture for driving regional public health decisions with automated and semi-automated data collected from open source point of care systems in resource constrained environments.

The National Surveillance Team in the Enteric Diseases Epidemiology Branch of the Centers for Disease Control and Prevention (CDC) collects electronic data from all state and regional public health laboratories on human infections caused by Campylobacter, Salmonella, Shiga toxin-producing E. coli, and Shigella in LEDS. These data inform annual estimates of the burden of illness, assessments of patterns in bacterial subtypes, and can be used to describe trends in incidence. Robust digital infrastructure is required to process, validate, and summarize data on approximately 60,000 infections annually while optimizing use of financial and personnel resources.

Objective:

The œledsmanageR, a data management platform built in R, aims to improve the timeliness and accuracy of national foodborne surveillance data submitted to the Laboratory-based Enteric Disease Surveillance (LEDS) system by automating the data processing, validating, and reporting workflow.