Early-Warning Systems to Monitor Progress in Health System Performance in Mesoamerica: Leveraging Routine Health Information Systems

Presentation at the Global Maternal Newborn Health Conference, October 21, 2015

Background: Monitoring progress during implementation of results based financing (RBF) programs has oftentimes been curtailed by weak RHIS. Delays that normally plague implementation can be increased by implementers’ lack of timely, credible and actionable data regarding inputs, coverage and quality of services provided. Implementers require “early-warning systems” to provide reliable, friendly, and actionable information about progress or lack thereof, to foster a culture of evidence-based decision making.

Methodology: An early-warning system was built with all countries participating in the Salud Mesoamerica 2015 Initiative (SM2015). Sourcing data from existing RHIS, we launched an assessment of the availability of routine indicators to monitor progress, revealing that official RHIS contained only 50% of data needed for SM2015. Using an open-source platform, two sets of information technology tools were developed: a meta-database and customizable dashboard called eTAB (developed in El Salvador), and an android-application, Quality Improvement Analytics (QIA), which collects and visualizes quality indicators currently unavailable in RHIS (piloted in Belize). Primary sources were modified, steps for data collection reduced, and “data quality alerts” introduced, without creating parallel systems, helping to strengthen existing ones.

Results: Five of the eight SM2015 countries are using the eTAB to monitor progress. Belize has scaled up the QIA; Honduras, El Salvador and Chiapas are exploring replication. Using open-source software eliminates license fees; countries continue to make changes as needed.

Conclusions: Results-based funded programs require results-oriented monitoring tools. The Mesoamerican countries have created a low-cost, early-warning system gearing their HIS towards providing more-reliable, opportune performance data for decision making, even with existing technology, human resources and communication constraints. As tools were developed in-country, they are more likely to be sustained. While existing HIS´s still need further improvement, the process leading to this early warning system has contributed to improve and scale-up a very important building block in any health system.