From Real-world Individuals' Data to National Health Indicators: Multiphase Pilot Study in Gabon.

BACKGROUND: Achieving health goals requires informed decision-making supported by transparent, reliable, and relevant health information. This helps decision makers, such as health managers, to better understand the functioning of their health system and improve their ability to respond quickly to health demands. To achieve this, the health system needs to be supported by a digitized decision-making information system. In Sub-Saharan African countries, inadequate digital infrastructure, including limited internet connectivity and insufficient access to appropriate computer software, makes it difficult to collect, process, and analyze data for health statistics. The processing of data is done manually in this case; however, this situation affects the quality of the health statistics produced and compromises the quality of health intervention choices in these countries. OBJECTIVE: This study aimed to describe the conceptual approach of a data production and dissemination platform model proposed and implemented in Gabon. More precisely, it aimed to present the approach applied for the multidimensional analysis of the data production and dissemination process in the existing information system and present the results of an evaluation of the proposed model implemented in a real context. METHODS: The research was carried out in 3 phases. First, a platform was designed and developed based on the examination of the various data production and indicator generation procedures. Then, the platform was implemented in chosen health facilities in Gabon. Finally, a platform evaluation was carried out with actual end users. RESULTS: A total of 14 users with 12 years of average experience in health data management were interviewed. The results show that the use of the proposed model significantly improved the completeness, timeliness, and accuracy of data compared with the traditional system (93% vs 12%, P<.001; 96% vs 18%, P<.001; and 100% vs 18%, P<.001; respectively). CONCLUSIONS: The proposed model contributes significantly to the improvement of health data quality in Gabon.