Data Quality in Health Research: Integrative Literature Review
BackgroundDecision-making and strategies to improve service delivery must be supported by reliable health data to generate consistent evidence on health status. The data quality management process must ensure the reliability of collected data. Consequently, various methodolog...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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JMIR Publications
2023-10-01
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| Series: | Journal of Medical Internet Research |
| Online Access: | https://www.jmir.org/2023/1/e41446 |
| _version_ | 1827778009868795904 |
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| author | Filipe Andrade Bernardi Domingos Alves Nathalia Crepaldi Diego Bettiol Yamada Vinícius Costa Lima Rui Rijo |
| author_facet | Filipe Andrade Bernardi Domingos Alves Nathalia Crepaldi Diego Bettiol Yamada Vinícius Costa Lima Rui Rijo |
| author_sort | Filipe Andrade Bernardi |
| collection | DOAJ |
| description |
BackgroundDecision-making and strategies to improve service delivery must be supported by reliable health data to generate consistent evidence on health status. The data quality management process must ensure the reliability of collected data. Consequently, various methodologies to improve the quality of services are applied in the health field. At the same time, scientific research is constantly evolving to improve data quality through better reproducibility and empowerment of researchers and offers patient groups tools for secured data sharing and privacy compliance.
ObjectiveThrough an integrative literature review, the aim of this work was to identify and evaluate digital health technology interventions designed to support the conducting of health research based on data quality.
MethodsA search was conducted in 6 electronic scientific databases in January 2022: PubMed, SCOPUS, Web of Science, Institute of Electrical and Electronics Engineers Digital Library, Cumulative Index of Nursing and Allied Health Literature, and Latin American and Caribbean Health Sciences Literature. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses checklist and flowchart were used to visualize the search strategy results in the databases.
ResultsAfter analyzing and extracting the outcomes of interest, 33 papers were included in the review. The studies covered the period of 2017-2021 and were conducted in 22 countries. Key findings revealed variability and a lack of consensus in assessing data quality domains and metrics. Data quality factors included the research environment, application time, and development steps. Strategies for improving data quality involved using business intelligence models, statistical analyses, data mining techniques, and qualitative approaches.
ConclusionsThe main barriers to health data quality are technical, motivational, economical, political, legal, ethical, organizational, human resources, and methodological. The data quality process and techniques, from precollection to gathering, postcollection, and analysis, are critical for the final result of a study or the quality of processes and decision-making in a health care organization. The findings highlight the need for standardized practices and collaborative efforts to enhance data quality in health research. Finally, context guides decisions regarding data quality strategies and techniques.
International Registered Report Identifier (IRRID)RR2-10.1101/2022.05.31.22275804 |
| first_indexed | 2024-03-11T14:27:05Z |
| format | Article |
| id | doaj.art-559cc8a2d8ac424e8bbb7575a56326fc |
| institution | Directory Open Access Journal |
| issn | 1438-8871 |
| language | English |
| last_indexed | 2024-03-11T14:27:05Z |
| publishDate | 2023-10-01 |
| publisher | JMIR Publications |
| record_format | Article |
| series | Journal of Medical Internet Research |
| spelling | doaj.art-559cc8a2d8ac424e8bbb7575a56326fc2023-10-31T14:30:39ZengJMIR PublicationsJournal of Medical Internet Research1438-88712023-10-0125e4144610.2196/41446Data Quality in Health Research: Integrative Literature ReviewFilipe Andrade Bernardihttps://orcid.org/0000-0002-9597-5470Domingos Alveshttps://orcid.org/0000-0002-0800-5872Nathalia Crepaldihttps://orcid.org/0000-0001-8011-868XDiego Bettiol Yamadahttps://orcid.org/0000-0001-6221-722XVinícius Costa Limahttps://orcid.org/0000-0002-2467-358XRui Rijohttps://orcid.org/0000-0002-9348-0474 BackgroundDecision-making and strategies to improve service delivery must be supported by reliable health data to generate consistent evidence on health status. The data quality management process must ensure the reliability of collected data. Consequently, various methodologies to improve the quality of services are applied in the health field. At the same time, scientific research is constantly evolving to improve data quality through better reproducibility and empowerment of researchers and offers patient groups tools for secured data sharing and privacy compliance. ObjectiveThrough an integrative literature review, the aim of this work was to identify and evaluate digital health technology interventions designed to support the conducting of health research based on data quality. MethodsA search was conducted in 6 electronic scientific databases in January 2022: PubMed, SCOPUS, Web of Science, Institute of Electrical and Electronics Engineers Digital Library, Cumulative Index of Nursing and Allied Health Literature, and Latin American and Caribbean Health Sciences Literature. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses checklist and flowchart were used to visualize the search strategy results in the databases. ResultsAfter analyzing and extracting the outcomes of interest, 33 papers were included in the review. The studies covered the period of 2017-2021 and were conducted in 22 countries. Key findings revealed variability and a lack of consensus in assessing data quality domains and metrics. Data quality factors included the research environment, application time, and development steps. Strategies for improving data quality involved using business intelligence models, statistical analyses, data mining techniques, and qualitative approaches. ConclusionsThe main barriers to health data quality are technical, motivational, economical, political, legal, ethical, organizational, human resources, and methodological. The data quality process and techniques, from precollection to gathering, postcollection, and analysis, are critical for the final result of a study or the quality of processes and decision-making in a health care organization. The findings highlight the need for standardized practices and collaborative efforts to enhance data quality in health research. Finally, context guides decisions regarding data quality strategies and techniques. International Registered Report Identifier (IRRID)RR2-10.1101/2022.05.31.22275804https://www.jmir.org/2023/1/e41446 |
| spellingShingle | Filipe Andrade Bernardi Domingos Alves Nathalia Crepaldi Diego Bettiol Yamada Vinícius Costa Lima Rui Rijo Data Quality in Health Research: Integrative Literature Review Journal of Medical Internet Research |
| title | Data Quality in Health Research: Integrative Literature Review |
| title_full | Data Quality in Health Research: Integrative Literature Review |
| title_fullStr | Data Quality in Health Research: Integrative Literature Review |
| title_full_unstemmed | Data Quality in Health Research: Integrative Literature Review |
| title_short | Data Quality in Health Research: Integrative Literature Review |
| title_sort | data quality in health research integrative literature review |
| url | https://www.jmir.org/2023/1/e41446 |
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