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      Factors That Influence Data Use to Improve Health Service Delivery in Low- and Middle-Income Countries

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          Abstract

          We identified factors that may influence the relationship between information generation and improvement of health service delivery: governance (leadership, participatory monitoring, regular review of data); production of information (presentation of findings, data quality, qualitative data); and health information system resources (electronic health management information systems, organizational structure, training).

          Abstract

          Key Findings

          We identified factors that may influence the relationship between information generation and improvement of health services:

          • Governance (leadership, participatory monitoring, regular review of data)

          • Production of information (presentation of findings, data quality, qualitative data)

          • Health information system resources (electronic health management information systems, organizational structure, training)

          Key Implications

          • Health system researchers should consider how these factors may apply in the field to build a stronger evidence base for how to effectively translate information drawn from health service delivery indicators into improvements in primary health care service delivery.

          • Program managers, district level staff, health facility managers, and health care workers should consider what support they need to use available data to improve decision making at the local level and their role in advocating for improved health service delivery in their communities.  

          ABSTRACT

          Background:

          Health service delivery indicators are designed to reveal how well health services meet a community’s needs. Effective use of the data can enable targeted improvements in health service delivery. We conducted a systematic review to identify the factors that influence the use of health service delivery indicators to improve delivery of primary health care services in low- and middle-income settings.

          Methods:

          We reviewed empirical studies published in 2005 or later that provided evidence on the use of health service delivery data at the primary care level in low- and middle-income countries. We searched Scopus, Medline, the Cochrane Library, and citations of included studies. We also searched the gray literature, using a separate strategy. We extracted information on study design, setting, study population, study objective, key findings, and any identified lessons learned.

          Results:

          Twelve studies met the inclusion criteria. This small number of studies suggests there is insufficient evidence to draw reliable conclusions. However, a content analysis identified the following potentially influential factors, which we classified into 3 categories: governance (leadership, participatory monitoring, regular review of data); production of information (presentation of findings, data quality, qualitative data); and health information system resources (electronic health management information systems, organizational structure, training). Contextual factors and performance-based financing were also each found to have a role; however, discussing these as mediating factors may not be practical in terms of promoting data use.

          Conclusion:

          Scant evidence exists regarding factors that influence the use of health service delivery indicators to improve delivery of primary health care services in low- and middle-income countries. However, the existing evidence highlights some factors that may have a role in improving data use. Further research may benefit from comparing data use factors across different types of program indicators or using our classification as a framework for field experiments.

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          Most cited references50

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          Improving the use of health data for health system strengthening

          Background Good 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. Objective To 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. Design A 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. Results The 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. Conclusions The 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.
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            An evaluation of the District Health Information System in rural South Africa.

            Since reliable health information is essential for the planning and management of health services, we investigated the functioning of the District Health Information System (DHIS) in 10 rural clinics. Semi-structured key informant interviews were conducted with clinic managers, supervisors and district information staff. Data collected over a 12-month period for each clinic were assessed for missing data, data out of minimum and maximum ranges, and validation rule violations. Our investigation was part of a larger study on improving information systems for primary care in rural KwaZulu-Natal. We assessed data quality, the utilisation for facility management, perceptions of work burden, and usefulness of the system to clinic staff. A high perceived work burden associated with data collection and collation was found. Some data collation tools were not used as intended. There was good understanding of the data collection and collation process but little analysis, interpretation or utilisation of data. Feedback to clinics occurred rarely. In the 10 clinics, 2.5% of data values were missing, and 25% of data were outside expected ranges without an explanation provided. The culture of information use essential to an information system having an impact at the local level is weak in these clinics or at the sub-district level. Further training and support is required for the DHIS to function as intended.
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              Performance-based financing in low-income and middle-income countries: isn’t it time for a rethink?

              This paper questions the view that performance-based financing (PBF) in the health sector is an effective, efficient and equitable approach to improving the performance of health systems in low-income and middle-income countries (LMICs). PBF was conceived as an open approach adapted to specific country needs, having the potential to foster system-wide reforms. However, as with many strategies and tools, there is a gap between what was planned and what is actually implemented. This paper argues that PBF as it is currently implemented in many contexts does not satisfy the promises. First, since the start of PBF implementation in LMICs, concerns have been raised on the basis of empirical evidence from different settings and disciplines that indicated the risks, cost and perverse effects. However, PBF implementation was rushed despite insufficient evidence of its effectiveness. Second, there is a lack of domestic ownership of PBF. Considering the amounts of time and money it now absorbs, and the lack of evidence of effectiveness and efficiency, PBF can be characterised as a donor fad. Third, by presenting itself as a comprehensive approach that makes it possible to address all aspects of the health system in any context, PBF monopolises attention and focuses policy dialogue on the short-term results of PBF programmes while diverting attention and resources from broader processes of change and necessary reforms. Too little care is given to system-wide and long-term effects, so that PBF can actually damage health services and systems. This paper ends by proposing entry points for alternative approaches.
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                Author and article information

                Journal
                Glob Health Sci Pract
                Glob Health Sci Pract
                ghsp
                ghsp
                Global Health: Science and Practice
                Global Health: Science and Practice
                2169-575X
                1 October 2020
                1 October 2020
                : 8
                : 3
                : 566-581
                Affiliations
                [a ]Research School of Population Health, Australian National University , Canberra, Australia.
                [b ] University of New South Wales , Kensington, Australia.
                Author notes
                Correspondence to Nicole Rendell ( nicole.rendell@ 123456anu.edu.au ).
                Article
                PMC7541116 PMC7541116 7541116 GHSP-D-19-00388
                10.9745/GHSP-D-19-00388
                7541116
                33008864
                6177a0de-f34c-4dc8-bdb4-a88c6323b304
                © Rendell et al.

                This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly cited. To view a copy of the license, visit http://creativecommons.org/licenses/by/4.0/. When linking to this article, please use the following permanent link: https://doi.org/10.9745/GHSP-D-19-00388

                History
                : 12 November 2019
                : 7 July 2020
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