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PreprintCited by 4Electronic Health Records SystemsData Quality and ManagementMedical Coding and Health Information

Data Quality in Health Research: A Systematic Literature Review

Open access
Bernardi, Filipe Andrade, Alves, Domingos, Crepaldi, Nathalia Yukie, Yamada, Diego Bettiol, Lima, Vinícius Costa, Lopes Rijo, Rui Pedro Charters·2022·medRxiv
DOI 10.1101/2022.05.31.22275804
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Abstract

Decision-making and strategies to improve service delivery need to be supported by reliable health data to generate consistent evidence on health status, so the data quality management process must ensure the reliability of the data collected. Thus, through an integrative literature review, the main objective of this work is to identify and evaluate digital health technology interventions designed to support the conduct of health research based on data quality. After analyzing and extracting the results of interest, 33 articles were included in the review. This transdisciplinarity may be reaching the threshold of significant growth and thus forcing the need for a metamorphosis of the area from focusing on the measurement and evaluation of data quality, today focused on content, to a direction focused on use and context In general, the main barriers reported in relation to the theme of research in the area of health data quality cite circumstances regarding a) use, b) systems and c) health services.. The resources presented can help guide medical decisions that do not only involve medical professionals, and indirectly contribute to avoiding decisions based on low-quality information that can put patients’ lives at risk

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  • Poor data quality in business, healthcare, and public administration leads to significant social and economic consequences.

    “Poor data quality in these domains leads to significant social and economic consequences. [1-4].”

    From Data Quality in the Age of AI: A Review of Governance, Ethics, and the FAIR Principles (2025) · cites this paper as [1]

    The Introduction says low-quality health data negatively affect research and health decision-making, and the Conclusion says decisions based on low-quality information can put patients’ lives at risk (page numbers unavailable). The paper does not establish comparable consequences in business or public administration, or identify economic consequences of poor data quality. (LLM, conf=0.85)

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  • Rigorous validation and governance policies combined with automated tools and audits ensure consistent data quality.

    “Ultimately, rigorous validation and governance policies [1] combined with automated tools and audits [9] ensure consistent data quality.”

    From Data Quality in the Age of AI: A Review of Governance, Ethics, and the FAIR Principles (2025) · cites this paper as [1]

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Research works printed but not linked: 36

  1. Harrison K, Rahimi N, Carolina Danovaro-Holliday M. Factors limiting data quality in the expanded programme on immunization in low and middle-income countries: A scoping review. Vaccine. 2020 Jun;38(30):4652-63.
  2. Peng C, Goswami P. Meaningful Integration of Data from Heterogeneous Health Services and Home Environment Based on Ontology. Sensors (Basel). 2019;19(8):1747.
  3. Kahn MG, Callahan TJ, Barnard J, Bauck AE, Brown J, Davidson BN, et al. A Harmonized Data Quality Assessment Terminology and Framework for the Secondary Use of Electronic Health Record Data. EGEMS (Wash DC). 2016 Sep 11;4(1):18.
  4. Ndabarora E, Chipps JA, Uys L. Systematic review of health data quality management and best practices at community and district levels in LMIC. Inf Dev. 2013 Jun 27;30(2):103-20.
  5. Benchoufi M, Ravaud P. Blockchain technology for improving clinical research quality. Trials [Internet]. 2017 Jul 19 [cited 2019 Jul 6];18(1). Available from: https://trialsjournal.biomedcentral.com/articles/10.1186/s13063-017-2035-z · DOI
  6. Pellizzon R de F. Pesquisa na área da saúde: 1. Base de dados DeCS (Descritores em Ciências da Saúde). Acta Cir Bras. 2004 Apr;19(2):153-63.
  7. Romano L. Using Medical Subject Headings (MeSH) in Cataloging. Tech Serv Q. 2018 Jan 29;35(2):217-9.
  8. Juddoo S, George C, Duquenoy P, Windridge D. Data Governance in the Health Industry: Investigating Data Quality Dimensions within a Big Data Context. Appl Syst Innov. 2018 Nov 1;1(4):43.
  9. Kumar M, Gotz D, Nutley T, Smith JB. Research gaps in routine health information system design barriers to data quality and use in low-and middle-income countries: A literature review. Int J Health Plann Manage. 2017 Aug 2;33(1):e1-9.
  10. Kodra Y, Posada de la Paz M, Coi A, Santoro M, Bianchi F, Ahmed F, et al. Data Quality in Rare Diseases Registries. Adv Exp Med Biol. 2017;1031:149-64.
  11. Feder SL. Data Quality in Electronic Health Records Research: Quality Domains and Assessment Methods. West J Nurs Res. 2017 Jan 24;40(5):753-66.
  12. Johnson S, Speedie S, Simon G, Kumar V, Westra B. Quantifying the Effect of Data Quality on the Validity of an eMeasure. Appl Clin Inform. 2017;08(04):1012-21.
  13. Malmasi S, Hosomura N, Chang L-S, Brown CJ, Skentzos S, Turchin A. Extracting Healthcare Quality Information from Unstructured Data. AMIA Annu Symp Proc [Internet]. 2018 Apr 16;2017:1243-52. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5977624/pdf/2732196.pdf
  14. Houston L, Probst Y, Yu P, Martin A. Exploring Data Quality Management within Clinical Trials. Appl Clin Inform. 2018 Jan;09(01):072-81.
  15. Pezoulas VC, Kourou KD, Kalatzis F, Exarchos TP, Venetsanopoulou A, Zampeli E, et al. Medical data quality assessment: On the development of an automated framework for medical data curation. Comput Biol Med [Internet]. 2019 Apr 1 [cited 2021 Nov 20];107:270-83. Available from: https://www.sciencedirect.com/science/article/abs/pii/S0010482519300733
  16. Mashoufi M, Ayatollahi H, Khorasani-Zavareh D. A Review of Data Quality Assessment in Emergency Medical Services. Open Med Inform J [Internet]. 2018 May 31 [cited 2020 Mar 14];12:19-32. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5997849/
  17. Bian J, Lyu T, Loiacono A, Viramontes TM, Lipori G, Guo Y, et al. Assessing the practice of data quality evaluation in a national clinical data research network through a systematic scoping review in the era of real-world data. J Am Med Inform Assoc. 2020 Nov 9;27(12):1999-2010.
  18. D'Amore J, Li C, McCrary L, Niloff J, Sittig D, McCoy A, et al. Using Clinical Data Standards to Measure Quality: A New Approach. Appl Clin Inform. 2018 Apr;09(02):422- 31.
  19. Sirgo G, Esteban F, Gómez J, Moreno G, Rodríguez A, Blanch L, et al. Validation of the ICU-DaMa tool for automatically extracting variables for minimum dataset and quality indicators: The importance of data quality assessment. Int J Med Inform. 2018 Apr;112:166- 72.
  20. Tian Q, Liu M, Min L, An J, Lu X, Duan H. An automated data verification approach for improving data quality in a clinical registry. Comput Methods Programs Biomed. 2019 Nov;181:104840.
  21. van der Bij S, Khan N, ten Veen P, de Bakker DH, Verheij RA. Improving the quality of EHR recording in primary care: a data quality feedback tool. J Am Med Inform Assoc. 2016 Jun 6;24(1):81-7.
  22. Chang H-M, Huang E-W, Hou I-C, Liu H-Y, Li F-S, Chiou S-F. Using a Text Mining Approach to Explore the Recording Quality of a Nursing Record System. J Nurs Res. 2019 Jun;27(3):e27.
  23. Sarafidis M, Tarousi M, Anastasiou A, Pitoglou S, Lampoukas E, Spetsarias A, et al. Data Quality Challenges in a Learning Health System. Stud Health Technol Inform [Internet]. 2020 Jun 16;270:143-7. Available from: https://pubmed.ncbi.nlm.nih.gov/32570363/
  24. Scobie HM, Edelstein M, Nicol E, Morice A, Rahimi N, MacDonald NE, et al. Improving the quality and use of immunization and surveillance data: Summary report of the Working Group of the Strategic Advisory Group of Experts on Immunization. Vaccine. 2020 Oct;38(46):7183-97.
  25. Shankaranarayanan G, Blake R. From content to context: The evolution and growth of data quality research. ACM J Data Inf Qual. 2017 8(2):1-28
  26. Andrews R, Wynn M, Vallmuur K, ter Hofstede A, Bosley E, Elcock M, et al. Leveraging Data Quality to Better Prepare for Process Mining: An Approach Illustrated Through Analysing Road Trauma Pre-Hospital Retrieval and Transport Processes in Queensland. Int J Environ Res Public Health. 2019 Mar 29;16(7):1138.
  27. Keller S, et al. The evolution of data quality: Understanding the transdisciplinary origins of data quality concepts and approaches. Annual Review of Statistics and Its Application 4 (2017): 85-108.
  28. Ni K, Chu H, Zeng L, Li N, Zhao Y. Barriers and facilitators to data quality of electronic health records used for clinical research in China: a qualitative study. BMJ Open [Internet]. 2019 Jul [cited 2019 Nov 5];9(7):e029314. Available from: https://bmjopen.bmj.com/content/9/7/e029314
  29. Burnett SM, Wun J, Evance I, Davis KM, Smith G, Lussiana C, et al. Introduction and Evaluation of an Electronic Tool for Improved Data Quality and Data Use during Malaria Case Management Supportive Supervision. Am J Trop Med Hyg. 2019 Apr 3;100(4):889-98.
  30. Houston L, et al. Exploring data quality management within clinical trials. Applied clinical informatics 9.01 (2018): 072-081.
  31. Pozzar R, Hammer MJ, Underhill-Blazey M, Wright AA, Tulsky JA, Hong F, et al. Threats of Bots and Other Bad Actors to Data Quality Following Research Participant Recruitment Through Social Media: Cross-Sectional Questionnaire. J Med Internet Res. 2020 Oct 7;22(10):e23021.
  32. Harkener S, Stausberg J, Hagel C, Siddiqui R. Towards a Core Set of Indicators for Data Quality of Registries. Stud Health Technol Inform. 2019 Sep 3;267:10.3233/SHTI190803. · DOI
  33. Kodra Y, Weinbach J, Posada-de-la-Paz M, Coi A, Lemonnier S, van Enckevort D, et al. Recommendations for Improving the Quality of Rare Disease Registries. Int J Environ Res Public Health. 2018 Aug 3;15(8):1644.
  34. Morley J, Luciano F. How to design a governable digital health ecosystem. The 2020 Yearbook of the Digital Ethics Lab. Springer, Cham, 2021. 69-88.
  35. Lepri B, et al. Fair, transparent, and accountable algorithmic decision-making processes. Philosophy & Technology 31.4 (2018): 611-627.
  36. Glasgow R, et al. RE-AIM planning and evaluation framework: adapting to new science and practice with a 20-year review. Frontiers in public health 7 (2019): 64.

Cited by

Sorted by citations

Authors
Bernardi, F. A., Alves, D., Crepaldi, N. Y., Yamada, D. B., Lima, V. C., & Lopes Rijo, R. P. C.
Affiliations
Universidade de São Paulo (BR)
Corresponding author
Bernardi, Filipe Andrade
ORCID
Venue
medRxiv
Publisher
openRxiv
Published
2022-05-31
DOI
10.1101/2022.05.31.22275804
OpenAlex
W4281782073
Cited by
4
References
2
Citations by year
2025: 3 · 2023: 1
Type
posted content
Language
en
Open access
Licence
cc-by
Locations
medRxiv (acceptedVersion, cc-by)
Topics
  • Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Data Quality and Management
  • Health Sciences › Health Professions › Health Information Management › Medical Coding and Health Information
Keywords
data quality, health research, digital health technologies, data quality management, health services, clinical decision-making, health information use

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