Abstract
BackgroundData harmonisation (DH) has emerged amongst health managers, information technology specialists and researchers as an important intervention for routine health information systems (RHISs). It is important to understand what DH is, how it is defined and conceptualised, and how it can lead to better health management decision-making. This scoping review identifies a range of definitions for DH, its characteristics (in terms of key components and processes), and common explanations of the relationship between DH and health management decision-making.MethodsThis scoping review identified relevant studies from 2000 onwards (date filter), written in English and published in PubMed, Web of Science and CINAHL. Two reviewers independently screened records for potential inclusion for the abstract and full-text screening stages. One reviewer did the data extraction, analysis and synthesis, with built-in reliability checks from the rest of the team. We developed a narrative synthesis of definitions and explanations of the relationship between DH and health management decision-making.ResultsWe sampled 61 of 181 included to synthesis definitions and concepts of DH in detail. We identified six common terms for data harmonisation: record linkage, data linkage, data warehousing, data sharing, data interoperability and health information exchange. We also identified nine key components of data harmonisation: DH involves (a) a process of multiple steps; (b) integrating, harmonising and bringing together different databases (c) two or more databases; (d) electronic data; (e) pooling data using unique patient identifiers; and (f) different types of data; (g) data found within and across different departments and institutions at facility, district, regional and national levels; (h) different types of technical activities; (i) has a specific scope. The relationship between DH and health management decision-making is not well-described in the literature. Several studies mentioned health providers’ concerns about data completeness, data quality, terminology and coding of data elements as barriers to data utilisation for clinical decision-making.ConclusionTo our knowledge, this scoping review was the first to synthesise definitions and concepts of DH and address the causal relationship between DH and health management decision-making. Future research is required to assess the effectiveness of data harmonisation on health management decision-making.
Highlights
Data harmonisation (DH) has emerged amongst health managers, information technology specialists and researchers as an important intervention for routine health information systems (RHISs)
When RHISs are functioning properly, they can help health practitioners and managers identify and close gaps in health service delivery as well as inform their planning, implementation and monitoring of interventions [4, 5]. They can help deal address problems related to using different variables and indicators for collecting, analysing and reporting health information across programmes [6], which is common in low-andmiddle-income (LMIC) settings
We screened a total of 1331 records;1232 titles and abstracts identified from searching three electronic databases, and 99 from screening for a Cochrane systematic review assessing the effectiveness of RHIS interventions on health systems management [26] and grey literature
Summary
Data harmonisation (DH) has emerged amongst health managers, information technology specialists and researchers as an important intervention for routine health information systems (RHISs). Data harmonisation (DH) in healthcare is a digital, technology-based innovation that can potentially help routine health information systems (RHISs) function at their best. When RHISs are functioning properly, they can help health practitioners and managers identify and close gaps in health service delivery as well as inform their planning, implementation and monitoring of interventions [4, 5]. They can help deal address problems related to using different variables and indicators for collecting, analysing and reporting health information across programmes [6], which is common in low-andmiddle-income (LMIC) settings. Other challenges to effective RHIS functioning include the production of poor-quality data that cannot be exchanged and programmatic fragmentation across levels of the health system, which can result in the duplication and excessive production of data [7]
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