Difference between revisions of "Journal:Definitions, components and processes of data harmonization in healthcare: A scoping review"
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'''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, [[Data analysis|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. | '''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, [[Data analysis|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 warehouse|data warehousing]]," "[[data sharing]], "[[Data integration|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. | '''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 warehouse|data warehousing]]," "[[data sharing]]," "[[Data integration|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. | '''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. | ||
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==Background== | ==Background== | ||
[[Data cleansing|Data harmonization]] (DH) in healthcare is a digital, technology-based innovation that can potentially help [[Health information technology|routine health information systems]] (RHISs) function at their best. It can help organize and integrate large databases containing routine [[Health information management|health information]].<ref name="LiuHarmon10">{{cite journal |title=Harmonization of health data at national level: a pilot study in China |journal=International Journal of Medical Informatics |author=Liu, D.; Wang, X.; Pan, F. et al. |volume=79 |issue=6 |pages=450–8 |year=2010 |doi=10.1016/j.ijmedinf.2010.03.002 |pmid=20399139}}</ref> 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.<ref name="NutleyImprov13">{{cite journal |title=Improving the use of health data for health system strengthening |journal=Global Health Action |author=Nutley, T.; Reynolds, H.W. |volume=6 |at=20001 |year=2013 |doi=10.3402/gha.v6i0.20001 |pmid=23406921 |pmc=PMC3573178}}</ref><ref name="LippeveldRoutine01">{{cite web |url=https://docplayer.net/3034875-Routine-health-information-systems-the-glue-of-a-unified-health-system.html |title=Routine health information systems: The glue of a unified health system |author=Lippeveld, T. |work=Keynote address at the Workshop on Issues and Innovation in Routine Health Information in Developing Countries |date=2001}}</ref> 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.<ref name="WHOEvery07">{{cite web |url=https://www.who.int/healthsystems/strategy/everybodys_business.pdf?ua=1 |format=PDF |title=Everybody's Business: Strengthening Health Systems to Improve Health Outcomes |author=World Health Organization |publisher=World Health Organization |pages=44 |date=2007 |isbn=9789241596077}}</ref><ref name="HMN-WHOCountry12">{{cite web |url=http://www.who.int/healthmetrics/resources/Working_Paper_3_HMN_Lessons_Learned.pdf |archiveurl=https://web.archive.org/web/20141021202640/http://www.who.int/healthmetrics/resources/Working_Paper_3_HMN_Lessons_Learned.pdf |format=PDF |title=Country Health Information Systems Assessments: Overview and Lessons Learned - Working Paper 3 |author=Health Metrics Network, World Health Organization |publisher=World Health Organization |date=November 2012 |archivedate=21 October 2014}}</ref> 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<ref name="HewoodGuide15">{{cite web |url=https://www.measureevaluation.org/resources/publications/ms-15-99 |title=Guidelines for Data Management Standards in Routine Health Information Systems |author=Heywood, A.; Boone, D. |work=Measure Evaluation |date=February 2015 |pages=93}}</ref>, 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 [[Health information exchange|exchanged]], as well as programmatic fragmentation across levels of the health system, which can result in the duplication and excessive production of data.<ref name="KaruriDHSI2_14">{{cite journal |title=DHIS2: The Tool to Improve Health Data Demand and Use in Kenya |journal=Journal of Health Informatics in Developing Countries |author=Karuri, J.; Waiganjo, P.; Orwa, D. et al. |volume=8 |issue=1 |at=113 |year=2014 |url=https://jhidc.org/index.php/jhidc/article/view/113}}</ref> | |||
Revision as of 17:21, 1 November 2020
| 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) |
 |
This article should be considered a work in progress and incomplete. Consider this article incomplete until this notice is removed. |
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]
References
- ↑ 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.
- ↑ 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.
- ↑ 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.
- ↑ 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.
- ↑ 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.
- ↑ 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.
- ↑ 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.
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.
