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Background: Evidence-based interventions are necessary for planning and investing in health information systems (HIS) and for strengthening those systems to collect, manage, sort and analyse health data to support informed decision-making. However, evidence and guidance on HIS strengthening in low- and middle-income countries have been historically lacking., Objective: This article describes the approach, methods, lessons learned and recommendations from 5 years of applying our learning agenda to strengthen the evidence base for effective HIS interventions., Methods: The first step was to define key questions about characteristics, stages of progression, and factors and conditions of HIS performance progress. We established a team and larger advisory group to guide the implementation of activities to build the evidence base to answer questions. We strengthened learning networks to share information., Results: The process of applying the learning agenda provided a unique opportunity to learn by doing, strategically collecting information about monitoring and evaluating HIS strengthening interventions and building a body of evidence. There are now models and tools to strengthen HIS, improved indicators and measures, country HIS profiles, documentation of interventions, a searchable database of HIS assessment tools and evidence generated through syntheses and evaluation results., Conclusion: The systematic application of learning agenda processes and activities resulted in increased evidence, information, guidance and tools for HIS strengthening and a resource centre, making that information accessible and available globally., Implications: We describe the inputs, processes and lessons learned, so that others interested in designing a successful learning agenda have access to evidence of how to do so.

The sustainable development goal (SDG) for health is linked to 67 indicators, eight times more than their predecessor, the Millenium Development Goals. In many low- and middle-income countries (LMICs), the information infrastructure is not yet able to collect and use the data needed for the indicators. As they seek to be responsive to the SDG agenda, LMICs must not lose sight of their local data needs; they should be cautious about embracing untested electronic technologies for data collection, analysis, and use; carefully balance the care provision and data collection responsibilities of care providers; and use evidence of what works in strengthening their health information systems (HIS). While attending to these concerns, countries can look for instances in which SDG indicators are in sync with their own HIS goals., (© The Author 2016. Published by Oxford University Press in association with The London School of Hygiene and Tropical Medicine. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Background: In many countries, the responsibility for planning and delivery of health services is devolved to the subnational level. Health programs, however, often fall short of efficient use of data to inform decisions. As a result, programs are not as effective as they can be at meeting the health needs of the populations they serve. In Kenya, a decision-support tool, the District Health Profile (DHP) tool was developed to integrate data from health programs, primarily HIV, at the district level and to enable district health management teams to review and monitor program progress for specific health issues to make informed service delivery decisions., Methods: Thirteen in-depth interviews were conducted with ten tool users and three non-users in six districts to qualitatively assess the process of implementing the tool and its effect on data-informed decision making at the district level. The factors that affected use or non-use of the tool were also investigated. Respondents were selected via convenience sample from among those that had been trained to use the DHP tool except for one user who was self-taught to use the tool. Selection criteria also included respondents from urban districts with significant resources as well as respondents from more remote, under-resourced districts., Results: Findings from the in-depth interviews suggest that among those who used it, the DHP tool had a positive effect on data analysis, review, interpretation, and sharing at the district level. The automated function of the tool allowed for faster data sharing and immediate observation of trends that facilitated data-informed decision making. All respondents stated that the DHP tool assisted them to better target existing services in need of improvement and to plan future services, thus positively influencing program improvement., Conclusions: This paper stresses the central role that a targeted decision-support tool can play in making data aggregation, analysis, and presentation easier and faster. The visual synthesis of data facilitates the use of information in health decision making at the district level of a health system and promotes program improvement. The experience in Kenya can be applied to other countries that face challenges making district-level, data-informed decisions with data from fragmented information systems.