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

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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)
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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.[1] 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.[2][3] 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 also help address problems related to using different variables and indicators for collecting, analyzing, and reporting health information across healthcare administration and management programs[6], 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.[7]

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.[3][8] 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.[9][10][11] 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.[8][12][13]

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.[14] 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[15][16][17]; 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.[18] 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.[12]

Methods

This scoping review was conducted according to the methods outlined by Arksey and O’Malley.[19] 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.[20]

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

References

  1. ↑ Liu, D.; Wang, X.; Pan, F. et al. (2010). "Harmonization of health data at national level: a pilot study in China". International Journal of Medical Informatics 79 (6): 450–8. doi:10.1016/j.ijmedinf.2010.03.002. PMID 20399139. 
  2. ↑ Nutley, T.; Reynolds, H.W. (2013). "Improving the use of health data for health system strengthening". Global Health Action 6: 20001. doi:10.3402/gha.v6i0.20001. PMC PMC3573178. PMID 23406921. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3573178. 
  3. ↑ 3.0 3.1 Lippeveld, T. (2001). "Routine health information systems: The glue of a unified health system". Keynote address at the Workshop on Issues and Innovation in Routine Health Information in Developing Countries. https://docplayer.net/3034875-Routine-health-information-systems-the-glue-of-a-unified-health-system.html. 
  4. ↑ World Health Organization (2007). "Everybody's Business: Strengthening Health Systems to Improve Health Outcomes" (PDF). World Health Organization. pp. 44. ISBN 9789241596077. https://www.who.int/healthsystems/strategy/everybodys_business.pdf?ua=1. 
  5. ↑ Health Metrics Network, World Health Organization (November 2012). "Country Health Information Systems Assessments: Overview and Lessons Learned - Working Paper 3" (PDF). World Health Organization. Archived from the original on 21 October 2014. https://web.archive.org/web/20141021202640/http://www.who.int/healthmetrics/resources/Working_Paper_3_HMN_Lessons_Learned.pdf. 
  6. ↑ Heywood, A.; Boone, D. (February 2015). "Guidelines for Data Management Standards in Routine Health Information Systems". Measure Evaluation. pp. 93. https://www.measureevaluation.org/resources/publications/ms-15-99. 
  7. ↑ Karuri, J.; Waiganjo, P.; Orwa, D. et al. (2014). "DHIS2: The Tool to Improve Health Data Demand and Use in Kenya". Journal of Health Informatics in Developing Countries 8 (1): 113. https://jhidc.org/index.php/jhidc/article/view/113. 
  8. ↑ 8.0 8.1 Harrison, M.I.; Koppel, R.; Bar-Lev, S. (2007). "Unintended consequences of information technologies in health care--an interactive sociotechnical analysis". JAMIA 14 (5): 542–9. doi:10.1197/jamia.M2384. PMC PMC1975796. PMID 17600093. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1975796. 
  9. ↑ van Olmen, J.; Criel, B.; Bhojani, U. et al. (2012). "The Health System Dynamics Framework: The introduction of an analytical model for health system analysis and its application to two case-studies". Health, Culture and Society 2 (1): 1–21. doi:10.5195/hcs.2012.71. 
  10. ↑ Sittig, D.F.; Singh, H. (2010). "A new sociotechnical model for studying health information technology in complex adaptive healthcare systems". Quality & Safety in Healthcare 19 (Suppl. 3): i68–74. doi:10.1136/qshc.2010.042085. PMC PMC3120130. PMID 20959322. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3120130. 
  11. ↑ Plsek, P. (27 January 2003). "Complexity and the Adoption of Innovation in Health Care" (PDF). Accelerating Quality Improvement in Health Care Strategies to Speed the Diffusion of Evidence-Based Innovations. https://www.nihcm.org/pdf/Plsek.pdf. 
  12. ↑ 12.0 12.1 Cresswell, K.M.; Sheikh, A. (2014). "Undertaking sociotechnical evaluations of health information technologies". Informatics in Primary Care 21 (2): 78–83. doi:10.14236/jhi.v21i2.54. PMID 24841408. 
  13. ↑ Cresswell, K.M.; Bates, D.W.; Sheikh, A. (2017). "Ten key considerations for the successful optimization of large-scale health information technology". JAMIA 24 (1): 182–7. doi:10.1093/jamia/ocw037. PMID 27107441. 
  14. ↑ Fichtinger, A.; Rix, J.; Schäffler, U. et al. (2011). "Data Harmonisation Put into Practice by the HUMBOLDT Project". International Journal of Spatial Data Infrastructures Research 6: 234–60. doi:10.2902/1725-0463.2011.06.art11. 
  15. ↑ Akhlaq, A.; McKinstry, B.; Muhammad, K.B.; Sheikh, A. (2016). "Barriers and facilitators to health information exchange in low- and middle-income country settings: A systematic review". Health Policy and Planning 31 (9): 1310–25. doi:10.1093/heapol/czw056. PMID 27185528. 
  16. ↑ Boyd, J.H.; Randall, S.M.; Ferrante, A.M. et al. (2014). "Technical challenges of providing record linkage services for research". BMC Medical Informatics and Decision Making 14: 23. doi:10.1186/1472-6947-14-23. PMC PMC3996173. PMID 24678656. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3996173. 
  17. ↑ Hu, P.J.; Zeng, D.; Chen, H. et al. (2007). "System for infectious disease information sharing and analysis: Design and evaluation". IEEE Transactions on Information Technology in Biomedicine 11 (4): 483–92. doi:10.1109/titb.2007.893286. PMC PMC7186032. PMID 17674631. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7186032. 
  18. ↑ Aqil, A.; Lippeveld, T.; Hozumi, D. (2009). "PRISM framework: A paradigm shift for designing, strengthening and evaluating routine health information systems". Health Policy and Planning 24 (3): 217–28. doi:10.1093/heapol/czp010. PMC PMC2670976. PMID 19304786. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2670976. 
  19. ↑ Arksey, H.; O'Malley, L. (2005). "Scoping studies: Towards a methodological framework". International Journal of Social Research Methodology 8 (1): 19–32. doi:10.1080/1364557032000119616. 
  20. ↑ Schmidt, B.-M.; Colvin, C.J.; Hohlfeld, A. et al. (2009). "Defining and conceptualising data harmonisation: A scoping review protocol". Health Policy and Planning 7 (1): 226. doi:10.1186/s13643-018-0890-7. PMC PMC6284298. PMID 30522527. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6284298. 

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.

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