Contents |
Authors:
Igor Rekunenko, ORCID: https://orcid.org/0000-0002-1558-629X Dr.Sc., Professor, Sumy State University, Ukraine Anton Boiko, ORCID: https://orcid.org/0000-0002-1784-9364 Dr.Sc., Professor, Sumy State University, Ukraine Olha Kramarenko, ORCID: https://orcid.org/0000-0002-2616-0147 Sumy State University, Ukraine Bhola Khan, ORCID: https://orcid.org/0000-0002-4479-2762 Dr.Sc., Professor, Yobe State University, Nigeria
Pages: 36-43
Language: English
DOI: https://doi.org/10.21272/hem.2022.2-04
Received: 22.05.2022
Accepted: 22.06.2022
Published: 30.06.2022
Download: |
Views: |
Downloads: |
|
|
|
Abstract
Today, in times of outbreaks of epidemics such as the Zika virus and COVID-19, health systems around the globe face an urgent need to respond quickly to overcome their spread and prevent the re-infection of humanity. Among the solutions was the World Health Organization’s call for immediate, rapid, and, most importantly, open dissemination of medical research data. Although data sharing benefits science and society, there is a range of ethical, legal, cultural, financial, and technical barriers to the dissemination and reuse of medical research data today. Therefore, the quality of data, namely the possibility of their easy search, availability, compatibility, and reusability, is considered relevant in developing data infrastructure in healthcare. Consequently, there is an urgent need to develop an appropriate research data management system in the healthcare system. The aim is to study the efficiency of data management in the healthcare system. This goal is proposed to be achieved in the following sequence: to consider the essence of the concept of research data management; to conduct a bibliometric study of the concept of data management in the medical fields of knowledge, to analyze the frequency of publications on the issue of data management of medical research, indexed by the Scopus database. The object of research is the healthcare sector. The subject is the determinants of the impact of proper management of medical research data on the healthcare sector. The terminological maps of term relationships were constructed using the VOSviewer visualization tool. The study found that 8% of all data management research was conducted in the medical fields of knowledge. The study found an upward trend in the number of health data management publications with the highest growth rate in 2019 and 2020. Analysis of terminology clusters revealed that the keywords «Big Data», «Machine Learning», «Data Collection», «Data Quality», «Data Sharing», «Data Reuse», «COVID-19», and «Blockchain» have the highest number of mentions in publications and strong connection with other publications. Thus, we justified the importance of developing a coherent program and strategic plans for managing research data in the health care system. The results of the study can be used to support decision-making on future opportunities to effectively influence the development of access to medical research data, as well as to ensure the improvement of the quality and confidentiality of research data in the health system.
Keywords: research data management, open data, data management, data quality, medical data, bibliometric analysis.
JEL Classification: I18, I23.
Cite as: Rekunenko, I., Boiko, A., Kramarenko, O., & Khan, B. (2022). Data Management in Healthcare Research as a Guarantee of its Quality. Health Economics and Management Review, 3(2), 36-43. https://doi.org/10.21272/hem.2022.2-04
This work is licensed under a Creative Commons Attribution 4.0 International License
References
- Dobrova, V., Popov, O., Zupanets, I., & Tkachenko, K. (2021). Shaping of the evidence-based substitution conceptual framework of the original medicines to generic counterparts in Ukraine. ScienceRise: Pharmaceutical Science, 4(32), 67-77. [Google Scholar] [CrossRef]
- Dobrova, V., Zupanets, K., & Ratushna, K. (2014). Analysis and study of the data quality loss risk in clinical trials. Clinical pharmacy, 18(1), 4-10. [Google Scholar]
- Heeney, C., Hawkins, N., de Vries, J., Boddington, P., & Kaye, J. (2011). Assessing the privacy risks of data sharing in genomics. Public health genomics, 14(1), 17-25. [Google Scholar] [CrossRef].
- Houston, L., Yu, P., Martin, A., & Probst, Y. (2020). Heterogeneity in clinical research data quality monitoring: a national survey. Journal of Biomedical Informatics, 108, 103491. [Google Scholar] [CrossRef]
- IBM (n.d.). Data Management Software & Solutions. Retrieved from [Link]
- Malin, B., & Sweeney, L. (2004). How (not) to protect genomic data privacy in a distributed network: using trail re-identification to evaluate and design anonymity protection systems. Journal of biomedical informatics, 37(3), 179-192. [Google Scholar] [CrossRef]
- Marco, D. P. (n.d.). Foundations of Enterprise Data Management. Retrieved from [Link]
- Queralt-Rosinach, N., Kaliyaperumal, R., Bernabé, C. H., Long, Q., Joosten, S. A., van der Wijk, H. J., … & Roos, M. (2022). Applying the FAIR principles to data in a hospital: challenges and opportunities in a pandemic. Journal of Biomedical Semantics, 13(1), 1-19. [Google Scholar] [CrossRef]
- Stuart, D., Baynes, G., Hrynaszkiewicz, I., Allin, K., Penny, D., Lucraft, M., & Astell, M. (2018). Whitepaper: Practical challenges for researchers in data sharing. LONDON: Figshare. [CrossRef]
- The Data Governance Institute. (n.d.). Definitions of Data Governance. Retrieved from [Link]
- Wellcome. (2020). Sharing Research Data and Findings Relevant to the Novel Coronavirus (COVID-19) Outbreak. Retrieved from [Link].
- Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J., Appleton, G., Axton, M., Baak, A., … & Mons, B. (2016). The FAIR Guiding Principles for scientific data management and stewardship. Scientific data, 3(1), 1-9. [Google Scholar] [CrossRef]
- Xafis, V., & Labude, M. K. (2019). Openness in big data and data repositories. Asian Bioethics Review, 11(3), 255-273. [Google Scholar] [CrossRef]
- Xafis, V., Schaefer, G. O., Labude, M. K., Brassington, I., Ballantyne, A., Lim, H. Y., … & Tai, E. S. (2019). An ethics framework for big data in health and research. Asian Bioethics Review, 11(3), 227-254. [Google Scholar] [CrossRef]
- Zastrow, M. (2020). Open science takes on the coronavirus pandemic. Nature, 581(7806), 109-110. [Google Scholar]
|