Journal:Definitions, components and processes of data harmonization in healthcare: A scoping review

Full article title	Definitions, components and processes of data harmonization in healthcare: A scoping review
Journal	BMC Medical Informatics and Decision Making
Author(s)	Schmidt, Bey-Marrié; Colvin, Christopher J.; Hohlfeld, Ameer; Leon, Natalie
Author affiliation(s)	South African Medical Research Council, University of Cape Town, University of Virginia, Brown University
Primary contact	Online contact form
Year published	2020
Volume and issue	20
Page(s)	222
DOI	10.1186/s12911-020-01218-7
ISSN	1472-6947
Distribution license	Creative Commons Attribution 4.0 International
Website	https://bmcmedinformdecismak.biomedcentral.com/articles/10.1186/s12911-020-01218-7
Download	https://bmcmedinformdecismak.biomedcentral.com/track/pdf/10.1186/s12911-020-01218-7 (PDF)

Abstract

Background: Data harmonization (DH) has is increasingly being used by 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 conceptualized, 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.

Methods: This scoping review identified more than 2,000 relevant studies (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.

Results: Of the 181 studies ultimately included in this scoping review, 61 included synthesis definitions and concepts of DH in detail. From these, we identified six common terms for data harmonization: "record linkage," "data linkage," "data warehousing," "data sharing," "data interoperability," and "health information exchange." We also identified nine key components or characteristics of data harmonization: it involves (a) multi-step processes; (b) integration and harmonization of different databases; (c) the use of two or more databases; (d) the use of electronic data; (e) pooling of data using unique patient identifiers; (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; and (i) 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 use for clinical decision-making.

Conclusion: To our knowledge, this scoping review was the first to synthesize 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 harmonization on health management decision-making.

Keywords: data harmonization, health information exchange, health information system, scoping review

Background

Data harmonization (DH) in healthcare is a digital, technology-based innovation that can potentially help routine health information systems (RHISs) function at their best. It can help organize and integrate large databases containing routine health information. Designing, developing, and implementing DH interventions has the potential to strengthen aspects of the health system, by enhancing RHISs to a state of high-quality and relevant information that can support decisions, actions, and changes across all components and levels of the health system. 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. They can also help address problems related to using different variables and indicators for collecting, analyzing, and reporting health information across healthcare administration and management programs, which is common in low-and-middle-income (LMIC) settings. Other challenges to effective RHIS functioning include the production of poor-quality data that cannot easily be exchanged, as well as programmatic fragmentation across levels of the health system, which can result in the duplication and excessive production of data.

Lack of standardized data production processes, fragmentation of databases, and errors and duplication in data production are only some of the challenges of RHISs, which may, at first glance be categorized as technical challenges. Solutions to such apparently technical challenges include introducing new data forms, setting up warning systems to detect potential errors, and developing algorithms for integrating different databases.

However, DH interventions for RHISs may not be used effectively if data production and utilization processes are viewed as merely technical. Given that RHISs are embedded in complex health systems, DH interventions to improve RHIS functions are also influenced by the broader setting, in which dynamic and complex social and technical factors interact. There is a need to consider the influence of social factors as well. These may include people’s competencies in dealing with new data production processes, institutional values about data utilization, and existing relationships between data producers and decision-makers.

There is growing recognition that the development and implementation of DH interventions occurs in multiple technical and social contexts, and that DH interventions may differ in definition, purpose, and intended outcomes. As such, various terms are used to describe interventions with similar aims and activities to data harmonization. For example, terms such as "record linkage," "data warehousing," "data sharing," and "health information exchange" are all used to describe data harmonization-type activities; it is not always clear to which extent these interventions are similar in practice, scope, and relevance. The use of multiple terms may not be a problem in itself, but a common understanding of the components and processes will bring more clarity about what actually constitutes "data harmonization," and it will make it easier to compare and appraise the relevance and usefulness of DH interventions across settings.

Although DH has the potential to enhance RHISs, it is still unclear whether or how it affects health management decision-making. In some cases, DH interventions may not directly improve management decision-making, especially when interventions are more focused on the technical aspects of data production and less on the organizational and behavioral aspects of data use for decision-making. The scope of this review is to therefore understand the different ways in which DH is defined, to identify its components and processes, and to describe whether or how DH can affect health management decision-making. Greater clarity about the range of definitions, components, and processes of DH interventions, as well as its intended outcomes, can help to better evaluate DH's relevance, usefulness, and impact.

Methods

This scoping review was conducted according to the methods outlined by Arksey and O’Malley. They recommend a process that is “not linear but, requiring researchers to engage with each stage in a reflexive way” to achieve both "in-depth and broad" results. This review followed the standard steps for systematic reviews: identifying the research question, identifying relevant studies, selecting studies for inclusion, extracting data, and synthesizing data. These are detailed in our published study protocol.

Study objectives

This scoping review appraised the definitions, components, and processes of data harmonization activities, and it provided a broad explanation of the relationship between data harmonization interventions and health management decision-making. The specific objectives are:

1. to identify and provide an overview of the key components and processes of data harmonization studies;

2. to identify and synthesize the various definitions of data harmonization in healthcare; and

3. to describe the relationship between data harmonization interventions and health management decision-making.

We took a stepped approach in addressing these objectives. All included studies were used to address the first objective. To address the second objective, we sampled studies that were using alternative terms for DH interventions and used those to identify, synthesize, and compare similarities and differences in definitions. While executing these two objectives, we identified a smaller number of studies that contributed to the third objective.

Identifying relevant studies

Eligibility criteria

Peer-reviewed studies and gray literature were considered eligible for inclusion into the scoping review if they provided a definition or description of DH, and or, a more detailed conceptual explanation (in the form of a model, framework, or process) of a DH intervention. Additionally, studies were eligible if they provided an explanation of the causal relationship between DH and health management decision-making (such as through improved quality and accessibility of harmonized information for management and/or the use of harmonized health information for management decision-making). We considered any studies concerned with different technical activities of DH (such as linking, merging, cleaning and transferring). After screening, only studies for which we could access full-text articles were eligible for inclusion in the review.

Search strategy

A systematic literature search was conducted in PubMed, CINAHL, and Web of Science for eligible studies from January 1, 2000 to September 30, 2018. We limited our search to as far back as 2000, as digital technology-based innovations began during this period (such as health information exchange) in high-income countries (predominantly in the United States of America), and researchers and health system managers in LMICs became more interested in the integration of large digital databases. We present the search strategy in the study protocol. Based on preliminary searches, we anticipated that these databases would yield the highest results. The search strategies included a combination of keywords and Medical Subject Headings (MeSH) related to data harmonization (concept A) and health information systems (concept B). There were no geographic restrictions, but for logistical reasons of time and resources, we only searched for English studies.

Selecting studies for inclusion

Screening records

The first reviewer (BS) conducted all the searches with the help of a librarian and collated the records in the EndNote reference management program, where duplicates were removed. Two reviewers, (BS) and second reviewer (AH), then independently screened the records (titles and abstracts) to assess eligibility for full-text review. BS and AH resolved conflicts that emerged at this stage by talking through the inclusion criteria and arriving at a joint decision.

The full-texts of potentially eligible studies were retrieved and assessed by the two reviewers (BS and AH). Final inclusion into the review was based on whether at a minimum the study had a definition or description of a DH intervention or referred to its relationship with health management decision-making. The first reviewer read all full-texts and the second reviewer only read a sample (roughly a third) of the full-texts to verify the first reviewer’s decision about inclusion. BS and AH disagreed on four studies, and after discussion, agreed to exclude the studies.

After finalizing screening, the two reviewers then mapped out the characteristics of included studies in an Excel spreadsheet. They recorded the name of the first author; the date; the type of study (primary, review, conceptual, commentary); the term used for the intervention they described (DH or alternative); the country in which the study took place; the level at which the intervention was implemented (frontline, management, research); and indicated whether or not there was a conceptual model, framework, diagram, or process description of DH and health management decision-making. This detailed mapping of study characteristics was useful for informing sampling options for the second and third objectives.

Sampling of studies

A scoping review aims to map the literature on a particular topic rather than to provide an exhaustive explanation of a particular phenomenon of interest. Thus, the number of included studies was expected to be high in the scoping reviews. To manage the high numbers for a scoping review such as this one (where the aim was to provide definitions and concepts), it was necessary to make use of a qualitative sampling approach. A qualitative sampling approach for this review aimed for variation and depth rather than an exhaustive sample; reviewing too large a number of studies can impair the quality of the analysis and synthesis. We used two types of purposive sampling techniques called maximum variation sampling and theoretical sampling. These techniques were used to identify both the range, variation, and similarities or differences in definitions and concepts and intervention descriptions (as per the second objective), and to provide a rich synthesis of explanations of causal relationships between DH and health management decision-making (as per the third objective). For the first objective, we did not apply a sampling strategy. Thus, we included all the studies that at a minimum provided a definition or description of a DH intervention.

Data extraction

BS extracted data for the first objective from all the included studies (n = 181). AH independently extracted data from 81 (45%) of included studies to verify data extraction done by the first reviewer. We used an MS Excel spreadsheet for data extraction, as presented in Fig. 1. AH and BS extracted a few studies before clarifying the items in the spreadsheet. Once data extraction was complete, the reviewers were able to filter according to the individual items extracted in order to synthesize and compare the studies. Given the objectives of the scoping review, we did not extract any information relevant to conducting risk of bias or quality assessment. Not conducting risk of bias or quality assessment is consistent with scoping reviews of similar aims and methodological approaches.

Figure 1. Extract of the Excel data extraction form

Data synthesis: Collating, summarizing, and reporting findings

The first reviewer (BS) conducted data analysis using manual coding and the filter option in MS Excel. Another reviewer (NL) reviewed the data analysis work on an ongoing basis as an additional quality check. For the first objective, we conducted a numerical analysis to provide an overview of the characteristics of all the included studies. For the second objective, we conducted a qualitative analysis to provide a narrative synthesis of the different DH definitions and concepts, and to identify different components or activities that are considered part of the DH processes. For the third objective, we reviewed data related to intentions, suggestions, and/or explanations of how DH may lead to improved health management decision-making. We extracted and analyzed data relevant to the second and third objectives at the same time. We first created a list of all the different terms used to describe DH interventions and then compared definitions across alternative terms by looking for similarities or differences in the definitions or descriptions of DH interventions. We then coded key components, processes, and outcomes of DH interventions and the factors reported as important in the relationship between DH and health management decision-making.

The findings are structured according to three themes matching the three study objectives: an overview of the key characteristics of included studies, alternative terms and definitions of DH, and a narrative synthesis of the relationship between DH and health management decision-making.

Reflexivity

Throughout the review, the authors were aware of their own positions and reflected on how these could influence the study design, search strategy, inclusion decisions, data extraction, and analysis, as well as the synthesis and interpretation of the findings. The review authors are trained in anthropology, epidemiology, health systems, and evidence synthesis research. The first author was involved in participant observation of an innovative DH project in the Western Cape Department of Health in South Africa as part of her doctoral research, where she grappled with questions that informed the objectives of this review. Three of the authors (BS, AH, and NL) were involved in a Cochrane Collaboration systematic review on RHIS interventions when this scoping review was conceptualized, so they were familiar with some of the health information systems (HIS) literature and had some appreciation for the conceptual and methodological complexities of studying the field of health information management. This experience informed the way the first author developed the search strategy. She used an iterative approach to narrow down the search as much as possible because of her prior knowledge that it was difficult to balance sensitivity and specificity when developing a search strategy for HIS literature that is often multi-disciplinary in nature.

Results

Results of the search

Figure 2 shows a PRISMA diagram of the search results. We screened a total of 1,331 records: 1,232 titles and abstracts identified from searching three electronic databases, and 99 from screening for a Cochrane Collaboration systematic review assessing the effectiveness of RHIS interventions on health systems management, as well as gray literature. Almost a quarter (289 of 1,331) were deemed potentially eligible for full-text screening. We accessed full-texts for 275 studies, and of those, 181 were included in the scoping review for the first objective. We excluded 94 full-text articles because they did not meet the minimum criteria; that is, they did not provide a definition or description of a DH intervention or activity. We sampled 61 studies from the 181 for the second and third objectives. We arrived at 61 studies by including all reviews (systematic or literature reviews) and all studies (irrespective of the type of study) that also had a process description, conceptual framework, or a theory of a DH intervention (that is, in addition to the minimum criteria for the first objective).

Figure 2. PRISMA diagram of eligible studies

An overview of key characteristics of data harmonization studies

A total of 181 studies were included into this scoping review for the first objective (see Table 1). Given the high number of included studies, we decided to only map the following key characteristics of those studies: first author, date, type of study, intervention term (DH or alternative), country, and level of the health care system. Most included studies (126 of 181) were either primary studies assessing various aspects of developing and implementing DH interventions (quantitative studies n = 86), or patient, providers, or stakeholders’ perspectives (qualitative studies n = 34), or a combination of both (mixed methods studies n = 6).

Table 1. Characteristics of included studies (n = 181)   CDE = clinical data exchange, CIE = clinical information exchange, DH = data harmonization, DL = data linkage, DS = data sharing, DW = data warehouse, EHR = electronic health record, HDE = health data exchange, HIE = health information exchange, IE = information exchange, IO = interoperability, LMICs = low-to-middle-income countries, RL = record linkage Study name Date Type of study Intervention term Country Level of the healthcare system Commentary Burris 2017 Commentary HIE USA Frontline: hospitals Figge 2010 Commentary HIE USA Management McIlwain 2009 Commentary HIE USA Management Murphy 2010 Commentary HIE USA Management Overhage 2007 Commentary HIE USA Management Rudin 2010 Commentary HIE USA Frontline: workers Conceptual Boyd 2014 Conceptual RL Australia Research Carr 2013 Conceptual HIE USA Frontline: hospitals Cimino 2014 Conceptual HIE USA Management Deas 2012 Conceptual HIE USA Management Del Fiol 2015 Conceptual HIE USA Frontline: prisons, hospitals Dimitropoulos 2009 Conceptual HIE USA Management Downs 2010 Conceptual HIE USA Management Feldman 2017 Conceptual HIE USA Management Frisse 2010 Conceptual HIE USA Frontline: patients, workers Frisse 2008 Conceptual HIE USA Frontline: organizations Frohlich 2007 Conceptual HIE USA Management Godlove 2015 Conceptual HIE USA Fronline: patients Greene 2016 Conceptual HIE USA Management Grossman 2008 Conceptual HIE USA Management Haarbrandt 2016 Conceptual DW USA Management Hu 2007 Conceptual DS USA Management Jones 2012 Conceptual DS USA Management Kuperman 2013 Conceptual HIE USA Management Langabeer 2016 Conceptual HIE USA Management Liu 2011 Conceptual HIE China Management McDonald 2009 Conceptual HIE USA Management McMurray 2015 Conceptual HIE USA Management Miller 2014 Conceptual HIE USA Frontline: hospitals Nelson 2016 Conceptual HIE USA Frontline: prisons, hospitals Politi 2014 Conceptual HIE n/a Management Ranade-Kharkar 2014 Conceptual HIE USA Management Shapiro 2016 Conceptual HIE USA Frontline: workers, organizations Shapiro 2006 Conceptual HIE USA Management Thorn 2013 Conceptual HIE USA Frontline: healthcare workers Thorn 2014 Conceptual HIE USA Frontline: healthcare workers Vest 2010 Conceptual HIE USA Management Williams 2012 Conceptual HIE USA Management Yaraghi 2014 Conceptual HIE USA Management Zafar 2007 Conceptual HIE USA Management Zaidan 2015 Conceptual HIE Malasia Management Primary studies Abramson 2012 Primary: quantitative EHR, HIE USA Frontline: hospitals Adjerid 2011 Primary: quantitative HIE USA Management: states Adler-Milstein 2011 Primary: quantitative HIE USA Frontline: organizations Adler-Milstein 2013 Primary: quantitative HIE USA Management: organizations Adler-Milstein 2016 Primary: quantitative HIE USA Management Alexander 2015 Primary: qualitative HIE USA Frontline: healthcare workers Alexander 2016 Primary: qualitative HIE USA Frontline: healthcare workers Ancker 2012 Primary: quantitative HIE USA Frontline: consumers Bahous 2016 Primary: quantitative HIE Israel Frontline: hospitals Bailey 201 Primary: quantitative HIE USA Frontline: hospitals Ben-Assuli 2013 Primary: quantitative HIE USA Frontline: hospitals Boockvar 2017 Primary: quantitative HIE USA Frontline: hospitals Butler 2014 Primary: qualitative HIE USA Frontline: prisons, communities Campion 2012 Primary: quantitative HIE USA Frontline: healthcare workers Campion 2013 Primary: quantitative HIE USA Frontline: communities Campion 2013 Primary: quantitative HIE USA Frontline: clinics, hospitals Campion 2014 Primary: quantitative CDE USA Frontline: organizations Carr 2014 Primary: quantitative HIE USA Frontline: hospitals Carr 2016 Primary: quantitative HIE USA Frontline: hospitals Cochran 2015 Primary: qualitative HIE USA Frontline: clinics, communities Cross 2016 Primary: qualitative DM USA Management: organizations Dalan 2010 Primary: qualitative DM USA Research Dimitropoulos 2011 Primary: quantitative HIE USA Frontline: consumers Dixon 2011 Primary: quantitative HIE USA Frontline: laboratories Dixon 2013 Primary: quantitative HIE USA Frontline: hospitals Downing 2017 Primary: quantitative HIE USA Management: policies Dullabh 2013 Primary: qualitative HIE USA Management: organizations Elysee 2017 Primary: quantitative HIE, IO USA Frontline: hospitals Foldy 2007 Primary: quantitative HIE USA Management: organizations Fontaine 2010 Primary: qualitative HIE USA Frontline: primary healthcare French 2016 Primary: quantitative HIE USA Management: organizations Fricton 2008 Primary: quantitative HIE USA Frontline: patients, workers Frisse 2012 Primary: quantitative HIE USA Frontline: organizations Furukawa 2013 Primary: quantitative HIE USA Frontline: hospitals Furukawa 2014 Primary: quantitative HIE USA Frontline: healthcare workers Gadd 2011 Primary: quantitative HIE USA Frontline: healthcare workers Garg 2014 Primary: quantitative HIE USA Frontline: hospitals Gill 2001 Primary: quantitative DL South Africa Frontline: patients, disease Grinspan 2013 Primary: quantitative HIE USA Frontline: patients Grinspan 2014 Primary: quantitative HIE USA Frontline: healthcare workers Grinspan 2015 Primary: quantitative HIE USA Frontline: patients Hassol 2014 Primary: quantitative HIE USA Frontline: healthcare workers Herwehe 2012 Primary: quantitative HIE USA Frontline: healthcare workers Hincapie 2011 Primary: qualitative HIE USA Frontline: healthcare workers Holman 2008 Primary: quantitative DL USA Frontline: organizations, research Hypponen 2014 Primary: quantitative HIE Finland Frontline: healthcare workers Ji 2017 Primary: quantitative HIE Korea Frontline: hospitals Johnson 2011 Primary: mixed HIE USA Frontline: hospitals Jung 2015 Primary: quantitative HIE USA Frontline: hospitals Kaelber 2013 Primary: quantitative HIE USA Frontline: hospitals Kierkegaard 2014 Primary: qualitative HIE USA Frontline: healthcare workers Kierkegaard 2014 Primary: qualitative HIE USA Management Kim 2012 Primary: qualitative HIE Korea Management Knaup 2006 Primary: quantitative DS Germany Frontline: hospitals Kralewski 2012 Primary: qualitative CIE USA Frontline: organizations, workers Laborde 2011 Primary: quantitative HIE USA Frontline: hospitals Lee 2012 Primary: quantitative HIE South Korea Frontline: healthcare workers Li 2011 Primary: quantitative CDE Japan & China Frontline: organizations Liu 2010 Primary: qualitative DH China Management Lobach 2007 Primary: quantitative HIE USA Management Maenpaa 2012 Primary: quantitative HIE Finland Frontline: hospitals Maiorana 2012 Primary: mixed HIE USA Frontline: workers, disease Marinez 2015 Primary: quantitative HIE USA Frontline: hospitals Massoudi 2016 Primary: qualitative HIE USA Frontline: organizations Masterbroek 2016 Primary: quantitative HIE Netherlands Frontline: healthcare workers Mastebroek 2017 Primary: qualitative HIE Netherlands Frontline: patients Mastebroek 2017 Primary: qualitative HIE Netherlands Frontline: patients Matsumoto 2017 Primary: qualitative HIE USA Frontline: workers, hospitals Medford-Davis 2017 Primary: quantitative HIE USA Frontline: pateients, hospitals Mello 2018 Primary: qualitative HIE USA Management: policies Merrill 2013 Primary: quantitative HIE USA Fronline: managers Messer 2012 Primary: mixed HIE USA Frontline: clinics, organizations Miller 2012 Primary: qualitative HIE USA Frontline: consumers, organizations Miller 2017 Primary: quantitative HIE USA Frontline: disease, workers Moore 2012 Primary: quantitative HIE USA Fronline: workers, hospitals Motulsky 2018 Primary: quantitative HIE Canada Fronline: workers Myers 2012 Primary: qualitative HIE USA Frontline: disease, workers Obeidat 2014 Primary: quantitative IE Jordan Frontline: hospitals O'Donnell 2011 Primary: quantitative HIE USA Frontline: workers Onyile 2013 Primary: quantitative HIE USA Frontline: patients Opoku-Agyeman 2016 Primary: quantitative HIE USA Frontline: hospitals Overhage 2017 Primary: quantitative HIE USA Management Ozkaynak 2013 Primary: qualitative HIE USA Frontline: hospitals, workers Park 2013 Primary: quantitative HIE South Korea Frontline: clinics, hospitals Park 2015 Primary: quantitative HIE South Korea Frontline: clinics, hospitals Patel 2011 Primary: quantitative HIE USA Frontline: clinics, hospitals Politi 2015 Primary: quantitative HIE Israel Frontline: hospitals Ramos 2014 Primary: qualitative HIE USA Frontline: patients Ramos 2016 Primary: mixed HIE USA Frontline: patients Reis 2016 Primary: quantitative HDE USA Frontline: hospitals Richardson 2014 Primary: qualitative HIE USA Frontline: organizations, workers Ross 2010 Primary: qualitative HIE USA Frontline: clinics Ross 2013 Primary: quantitative HIE USA Frontline: workers, clinics, hospitals Rudin 2009 Primary: qualitative HIE USA Frontline: healthcare workers Rundall 2016 Primary: qualitative HIE USA Frontline: policy makers, leaders Saef 2014 Primary: quantitative HIE USA Frontline: hospitals Santos 2017 Primary: quantitative HIE Brazil Frontline: clinics, hospitals Shade 2012 Primary: quantitative HIE USA Frontline: clinics, hospitals Shank 2012 Primary: quantitative HIE USA Frontline: healthcare workers Shapiro 2007 Primary: quantitative HIE USA Frontline: healthcare workers Shapiro 2013 Primary: quantitative HIE USA Frontline: hospitals Sicotte 2010 Primary: qualitative HIE Canada Frontline: workers, hospitals Sprivulis 2007 Primary: quantitative HIE Australia Frontline: workers, organizations Squire 2002 Primary: mixed HIE USA Frontline: healthcare workers Sridhar 2012 Primary: quantitative HIE USA Frontline: hospitals Thornewill 2011 Primary: mixed HIE USA Frontline: consumers, organizations Unertl 2012 Primary: qualitative HIE USA Frontline: clinics, hospitals Vest 2009 Primary: quantitative HIE n/a Frontline: workers, patients Vest 2010 Primary: qualitative HIE USA Frontline: hospitals Vest 2011 Primary: quantitative HIE USA Frontline: patients, hospitals Vest 2013 Primary: quantitative HIE USA Frontline: hospitals Vest 2013 Primary: qualitative HIE USA Management: policy makers Vest 2014 Primary: quantitative HIE USA Frontline: patients, hospitals Vest 2014 Primary: quantitative HIE USA Frontline: hospitals Vest 2015 Primary: qualitative HIE USA Frontline: consumers, organizations Vest 2017 Primary: qualitative HIE USA Frontline: consumers, organizations Vest 2017 Primary: quantitative HIE USA Frontline: consumers, organizations Vreeman 2008 Primary: quantitative HIE USA Frontline: laboratory, radiology Webn 2010 Primary: quantitative HIE USA Frontline: patients Winden 2014 Primary: quantitative HIE USA Frontline: clinical care Wright 2010 Primary: quantitative HIE USA Frontline: healthcare workers Yeager 2014 Primary: qualitative HIE USA Frontline: consumers Yeaman 2015 Primary: quantitative HIE USA Frontline: hospitals Zech 2015 Primary: quantitative HIE USA Frontline: patients, organizations Zech 2016 Primary: quantitative HIE USA Frontline: patients, organizations Zhu 2010 Primary: quantitative HIE USA Research Study protocol Dixon 2013 Protocol: mixed HIE USA Frontline: organizations Reviews Akhlaq 2016 Review HIE LMICs Management: countries Dixon 2010 Review HIE USA Research Esmaeilzadeh 2016 Review HIE n/a Management: policies Esmaeilzadeh 2017 Review HIE n/a Frontline: patients Fontaine 2010 Review HIE n/a Frontline: primary healthcare Hopf 2014 Review DL n/a Frontline: healthcare workers Kash 2017 Review HIE n/a Frontline: hospitals Mastebroek 2014 Review HIE USA Frontline: disease, workers Parker 2016 Review HIE USA Research Rahurkar 2015 Review HIE n/a Frontline: hospitals Rudin 2014 Review HIE USA Frontline: clinical care Sadoughi 2018 Review HIE n/a Management Vest 2012 Review HIE n/a Management

Of the 181 included studies, nine were not country-specific (these were global reviews), 151 were from the USA, and the rest were from other countries (specifically Australia, Brazil, Canada, China, Finland, Germany, Israel, Japan, Jordan, Korea, Malaysia, Netherlands, South Africa, and South Korea). In terms of the level of the health care system, 128 studies were on a DH intervention or activity that was concerned with the frontline level (health service providers), 48 studies were concerned with health system factors or policy-related activities at the managerial level, and five studies focused on DH interventions specifically for research purposes. Most studies (92%) used the term health information exchange (HIE), while the remaining studies (8%) used a variety of terms to describe various DH interventions and activities, specifically, record linkage, data mining, data linkage, data warehousing, data sharing, and data harmonization.

Definitions, components, and processes of data harmonization

In this subsection, we first discuss the alternative terms and definitions of DH, and then we summarize key components and processes of DH using studies sampled from the 61 studies identified for the second and third objectives. Table 2 presents identifying details of the 61 studies, including the type of study design, the intervention terms, the country, the level of the health care system, and the purpose of the study. These studies were concerned with the challenges and opportunities of DH, the barriers and facilitators of DH, the various factors affecting DH (such as technical and financial factors), the outcomes of DH (such as patient safety and quality of care), and the privacy and security issues of patient information.

Table 2. Characteristics of sampled studies (n = 61)   CDE = clinical data exchange, CIE = clinical information exchange, DH = data harmonization, DL = data linkage, DS = data sharing, DW = data warehouse, EHR = electronic health record, HDE = health data exchange, HIE = health information exchange, IE = information exchange, IO = interoperability, LMICs = low-to-middle-income countries, RL = record linkage Study name Date Type of study Intervention term Country Level of the healthcare system Purpose of the study Akhlaq 2016 Review HIE LMICs Management: countries Barriers and facilitators of HIE Boyd 2014 Conceptual RL Australia Research Functions of record linkage Burris 2017 Commentary HIE USA Frontline: hospitals Benefits of HIE Campion 2012 Primary: quantitative HIE USA Frontline: healthcare workers Push and pull of HIE Cimino 2014 Conceptual HIE USA Management Debates around consumer-mediated HIE Dalan 2010 Primary: qualitative DM USA Research Possibilities for clinical data mining and research Dimitropoulos 2009 Conceptual HIE USA Management Privacy and security of interoperable HIE Dixon 2010 Review HIE USA Research Costs, effort and value of HIE Downing 2017 Primary: quantitative HIE USA Management: policies Relationship between HIE and organisational HIE policy decisions Downs 2010 Conceptual HIE USA Management Improving laboratory services through HIE Dullabh 2013 Primary: qualitative HIE USA Management: organizations Experience of HIE implementation Elysee 2017 Primary: quantitative HIE, IO USA Frontline: hospitals Relationship between HIE, interoperability and medication reconciliation Esmaeilzadeh 2016 Review HIE n/a Management: policies HIE assimilation and patterns for policy Esmaeilzadeh 2017 Review HIE n/a Frontline: patients Patients’ perceptions of HIE Fontaine 2010 Review HIE n/a Frontline: primary healthcare HIE for primary healthcare practices Fontaine 2010 Primary: qualitative HIE USA Frontline: primary healthcare Barriers and facilitators of HIE in primary care practices Frisse 2010 Conceptual HIE USA Frontline: patients, workers Impact of HIE on patient-provider relationships Gadd 2011 Primary: quantitative HIE USA Frontline: healthcare workers Users’ perspectives on the usability of a regional HIE Gill 2001 Primary: quantitative DL South Africa Frontline: patients, disease Linkage of non-communicable diseases data Greene 2016 Conceptual HIE USA Management Technical and financial aspects of HIE Grossman 2008 Conceptual HIE USA Management Barriers to stakeholder participation in HIE Haarbrandt 2016 Conceptual DW USA Management Approaches for a clinical data warehouse Herwehe 2012 Primary: quantitative HIE USA Frontline: healthcare workers Implementation of an electronic medical record and HIE Hincapie 2011 Primary: qualitative HIE USA Frontline: healthcare workers Physicians’ opinions of HIE Hopf 2014 Review DL n/a Frontline: healthcare workers Healthcare professionals’ views of linking routinely collected data Hu 2007 Conceptual DS USA Management Challenges in implementing an infectious disease information sharing and analysis system Hypponen 2014 Primary: quantitative HIE Finland Frontline: healthcare workers User experiences with different regional HIE Ji 2017 Primary: quantitative HIE Korea Frontline: hospitals Technology and policy changes for HIE Jones 2012 Conceptual DS USA Management An overview of electronic data sharing Kash 2017 Review HIE n/a Frontline: hospitals Hospital readmission reduction and the role of HIE Kierkegaard 2014 Primary: qualitative HIE USA Frontline: healthcare workers Applications of HIE information to public health practice Kierkegaard 2014 Primary: qualitative HIE USA Management Health practitioners’ needs and HIE Kuperman 2013 Conceptual HIE USA Management Potential unintended consequences of HIE Liu 2010 Primary: qualitative DH China Management Defining data elements for HIE Maiorana 2012 Primary: mixed HIE USA Frontline: workers, disease Trust, confidentiality, and acceptability of sharing HIV data for HIE Massoudi 2016 Primary: qualitative HIE USA Frontline: organizations HIE for clinical quality measures Mastebroek 2014 Review HIE USA Frontline: disease, workers HIE in general care practice for people with disabilities Mastebroek 2016 Primary: quantitative HIE Netherlands Frontline: healthcare workers Priority setting and feasibility of HIE Masterbroek 2017 Primary: qualitative HIE Netherlands Frontline: patients Experiences of people with intellectual disabilities in HIE Matsumoto 2017 Primary: qualitative HIE USA Frontline: workers, hospitals HIE in managing hospital services Parker 2016 Review HIE USA Research The use of HIE in supporting clinical research Politi 2014 Conceptual HIE n/a Management Use patterns of HIE Rahurkar 2015 Review HIE n/a Frontline: hospitals Impact of HIE on cost, use and quality of care Ramos 2016 Primary: mixed HIE USA Frontline: patients HIE consent process in an HIV clinic Ranade-Kharkar 2014 Conceptual HIE USA Management Improving data quality integrity through HIE Ross 2010 Primary: qualitative HIE USA Frontline: clinics Motivators, barriers, and potential facilitators of adoption of HIE Rudin 2014 Review HIE USA Frontline: clinical care Use and effect of HIE on clinical care Rundall 2016 Primary: qualitative HIE USA Frontline: policy makers, leaders Information-sharing needs and HIE Sadoughi 2018 Review HIE n/a Management Quality and cost-effectiveness, and the rates of HIE adoption and participation Santos 2017 Primary: quantitative HIE Brazil Frontline: clinics, hospitals HIE for continuity of maternal and neonatal care Shade 2012 Primary: quantitative HIE USA Frontline: clinics, hospitals HIE for quality and continuity of HIV care Shapiro 2016 Conceptual HIE USA Frontline: workers, organizations HIE in emergency medicine Shapiro 2006 Conceptual HIE USA Management Approaches to patient HIE and their impact on emergency medicine Vest 2010 Conceptual HIE USA Management Challenges and strategies for HIE Vest 2012 Review HIE n/a Management National and international approaches of health information exchange Vest 2015 Primary: qualitative HIE USA Frontline: consumers, organizations HIE to change cost and utilisation outcomes Williams 2012 Conceptual HIE USA Management Strategies to advance HIE Yaraghi 2014 Conceptual HIE USA Management Professional and geographical network effects on HIE growth Yeager 2014 Primary: qualitative HIE USA Frontline: consumers Factors related to HIE participation and use Zaidan 2015 Conceptual HIE Malasia Management Security framework for nationwide HIE Zhu 2010 Primary: quantitative HIE USA Research Facilitating clinical research through HIE

Abbreviations

CDE = clinical data exchange

CIE = clinical information exchange

DH = data harmonization

DL = data linkage

DS = data sharing

DW = data warehouse

EHR = electronic health record

HDE = health data exchange

HIE = health information exchange

IE = information exchange

IO = interoperability

LMICs = low-to-middle-income countries

RL = record linkage

Notes

This presentation is faithful to the original, with only a few minor changes to presentation. In some cases important information was missing from the references, and that information was added. The "Study objectives" section in the original only states two objectives, leading to confusion when encountering references to three objectives; the intended three objectives were inferred from the rest of the text and restated for this version. The original tables didn't include an abbreviation key; the meaning of the abbreviations was inferred from the rest of the text (and the cited papers) and added for this version. Additionally, the author has been contacted asking for clarification.