tr-17-165-en-1.pdf (1 MB)
Evidence-informed decision making is essential for the success of health systems, programs, and services. Global commitments to improving health systems and outcomes have led to improved monitoring and evaluation and health information systems, thus providing an opportunity to use data for decision making and not simply for reporting. Overall, the relationships among improved information, demand for data, and continued data use constitute a cycle that leads to improved health programs and policies. Improving data demand and use is necessary to improve the effectiveness and sustainability of a health system.
MEASURE Evaluation, funded by USAID and the United States Presidents Malaria Initiative (PMI), undertook an assessment to understand the data-use context for those working in the Democratic Republic of the Congo in the National Malaria Control Program and the Division du Systme National d'Information Sanitaire (DSNIS, or Division of the National Health Information System), as well as in Haut Katanga, Kinshasa, and Lualaba provinces. The purpose of this assessment was to identify how data are currently being used for decision making and how future interventions can be designed to promote the demand for and use of data in decision making.
This mixed-methods assessment was based on MEASURE Evaluations conceptual approach and logic model, which provides guidance on best practices in data-informed decision making and data use. The model looks at three determinants of data use: technical, organizational, and behavioral. These determinants are adapted from the Performance of Routine Information Systems Management (PRISM) framework developed by Aqil, et al. (Aqil, et al., 2009). The assessment used four tools to assess an organizations data-use capabilities, as well as key barriers to and facilitators for developing and sustaining a culture of data use.This report shares the methods and findings of the assessment and recommendations for agencies in the DRC for the use of data in their decision-making processes.