BackgroundGood quality and timely data from health information systems are the foundation of all health systems. However, too often data sit in reports, on shelves or in databases and are not sufficiently utilised in policy and program development, improvement, strategic planning and advocacy. Without specific interventions aimed at improving the use of data produced by information systems, health systems will never fully be able to meet the needs of the populations they serve.ObjectiveTo employ a logic model to describe a pathway of how specific activities and interventions can strengthen the use of health data in decision making to ultimately strengthen the health system.DesignA logic model was developed to provide a practical strategy for developing, monitoring and evaluating interventions to strengthen the use of data in decision making. The model draws on the collective strengths and similarities of previous work and adds to those previous works by making specific recommendations about interventions and activities that are most proximate to affect the use of data in decision making. The model provides an organizing framework for how interventions and activities work to strengthen the systematic demand, synthesis, review, and use of data.ResultsThe logic model and guidance are presented to facilitate its widespread use and to enable improved data-informed decision making in program review and planning, advocacy, policy development. Real world examples from the literature support the feasible application of the activities outlined in the model.ConclusionsThe logic model provides specific and comprehensive guidance to improve data demand and use. It can be used to design, monitor and evaluate interventions, and to improve demand for, and use of, data in decision making. As more interventions are implemented to improve use of health data, those efforts need to be evaluated.

BackgroundImproving a health system requires data, but too often they are unused or under-used by decision makers. Without interventions to improve the use of data in decision making, health systems cannot meet the needs of the populations they serve. In 2008, in Côte d'Ivoire, data were largely unused in health decision-making processes.ObjectiveTo implement and evaluate an intervention to improve the use of data in decision making in Cote d'Ivoire.DesignFrom 2008 to 2012, Cote d'Ivoire sought to improve the use of national health data through an intervention that broadens participation in and builds links between data collection and decision-making processes; identifies information needs; improves data quality; builds capacity to analyze, synthesize, and interpret data; and develops policies to support data use. To assess the results, a Performance of Routine Information System Management Assessment was conducted before and after the intervention using a combination of purposeful and random sampling. In 2008, the sample consisted of the central level, 12 districts, and 119 facilities, and in 2012, the sample consisted of the central level, 20 districts, and 190 health facilities. To assess data use, we developed dichotomous indicators: discussions of analysis findings, decisions taken based on the analysis, and decisions referred to upper management for action. We aggregated the indicators to generate a composite, continuous index of data use.ResultsFrom 2008 to 2012, the district data-use score increased from 40 to 70%; the facility score remained the same – 38%. The central score is not reported, because of a methodological difference in the two assessments.ConclusionsThe intervention improved the use of data in decision making at the district level in Côte d'Ivoire. This study provides an example of, and guidance for, implementing a large-scale intervention to improve data-informed decision making.

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Despite the potential impact of health information system (HIS) design barriers on health data quality and use and, ultimately, health outcomes in low- and middle-income countries (LMICs), no comprehensive literature review has been conducted to study them in this context. We therefore conducted a formal literature review to understand system design barriers to data quality and use in LMICs and to identify any major research gaps related understanding how system design affects data use. We conducted an electronic search across 4 scientific databases-PubMed, Web of Science, Embase, and Global Health-and consulted a data use expert. Following a systematic inclusion and exclusion process, 316 publications (316 abstracts and 18 full papers) were included in the review. We found a paucity of scientific publications that explicitly describe system design factors that hamper data quality or data use for decision making. Although user involvement, work flow, human-computer interactions, and user experience are critical aspects of system design, our findings suggest that these issues are not discussed or conceptualized in the literature. Findings also showed that individual training efforts focus primarily on imparting data analysis skills. The adverse impact of HIS design barriers on data integrity and health system performance may be even bigger in LMICs than elsewhere, leading to errors in population health management and clinical care. We argue for integrating systems thinking into HIS strengthening efforts to reduce the HIS design-user reality gap.