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DOI: 10.1055/s-0044-1800733
Clinical Research Informatics: Contributions from 2023

Summary
Objectives: To summarize key contributions to current research in the field of Clinical Research Informatics (CRI) and to select the best papers published in 2023.
Methods: A bibliographic search using a combination of MeSH descriptors and free-text terms on CRI was performed using PubMed, followed by a double-blind review in order to select a list of candidate best papers to be then peer-reviewed by external reviewers. After peer-review ranking, a consensus meeting between the two section editors and the editorial team was organized to finally conclude on the selected three best papers.
Results: Among the 1,119 papers returned by the search, published in 2023, that were in the scope of the various areas of CRI, the full review process selected three best papers. The first best paper describes the process undertaken in Germany, under the national Medical Informatics Initiative, to define and validate a provenance metadata framework to enable the interpretation including quality assessment of health data reused for research. The authors of the second-best paper present a methodology for the generation of computable phenotypes and the covariates associated with success rates in e-phenotype validation. The third-best presents a review of published and accessible tools that enable the assessment of health data quality through an automated process. This year's survey paper marks the tenth anniversary of the CRI section of the Yearbook by reviewing the dominant themes within CRI over the past decade and the major milestone innovations within this field.
Conclusions: The literature relevant to CRI in 2023 has largely been populated by publications that assess and enhance the reusability of health data for clinical research, in particular data quality assessment and metadata management.
Keywords
International Medical Informatics Association Yearbook - Clinical Research Informatics - Biomedical Research, Clinical Trials as Topic - Observational studies as Topic - Real-world data - Health Data Quality - PhenotypingPublikationsverlauf
Artikel online veröffentlicht:
08. April 2025
© 2024. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution License, permitting unrestricted use, distribution, and reproduction so long as the original work is properly cited. (https://creativecommons.org/licenses/by/4.0/)
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References
- 1 Gierend K, Waltemath D, Ganslandt T, Siegel F. Traceable Research Data Sharing in
a German Medical Data Integration Center With FAIR (Findability, Accessibility, Interoperability,
and Reusability)-Geared Provenance Implementation: Proof-of-Concept Study. JMIR Form
Res 2023;7:e50027. doi: 10.2196/50027.
- 2 Keloth VK, Banda JM, Gurley M, Heider PM, Kennedy G, Liu H, Liu F, et al. Representing
and utilizing clinical textual data for real world studies: An OHDSI approach. J Biomed
Inform. 2023 Jun;142:104343. doi: 10.1016/j.jbi.2023.104343.
- 3 Mystre S, Heider P, Cates A, Bastian G, Pittman T, Gentillin S, et al. Piloting an
automated clinical trial eligibility surveillance and provider alert system based
on artificial intelligence and standard data models. BMC Medical Research Methodology
2023;23:88. doi : 10.1186/s12874-023-01916-6.
- 4 Daniali M, Galer PD, Lewis-Smith D, Parthasarathy S, Kim E, Salvucci DD, et al. Enriching
Representation Learning Using 53 Million Patient Notes through Human Phenotype Ontology
Embedding. Artif Intell Med 2023;139:102523. doi :10.1016/j.artmed.2023.102523.
- 5 Hamidi B, Flume PA, Simpson KN, Alekseyenko AV. Not all phenotypes are created equal:
covariates of success in e-phenotype specification. J Am Med Inform Assoc. 2023 Jan
18;30(2):213-221. doi: 10.1093/jamia/ocac157.
- 6 Brandt PS, Kho A, Luo Y, Pacheco JA, Walunas TL, Hakonarson H et al. Characterizing
variability of electronic health record-driven phenotype definitions. J Am Med Inform
Assoc. 2023 Feb 16;30(3):427-437. doi: 10.1093/jamia/ocac235.
- 7 Ozonze O, Scott PJ, Hopgood AA. Automating Electronic Health Record Data Quality
Assessment. J Med Syst. 2023 Feb 13;47(1):23. doi: 10.1007/s10916-022-01892-2.
- 8 Tahar K, Martin T, Mou Y, Verbuecheln R, Graessner H, Krefting D. Rare Diseases in
Hospital Information Systems-An Interoperable Methodology for Distributed Data Quality
Assessments. Methods Inf Med. 2023 Sep;62(3-04):71-89. doi: 10.1055/a-2006-1018.
- 9 Lee S, Roh GH, Kim JY, Ho Lee Y, Woo H, Lee S. Effective data quality management
for electronic medical record data using SMART DATA. Int J Med Inform. 2023 Dec;180:105262.
doi: 10.1016/j.ijmedinf.2023.105262.
- 10 Salim MM, Park JH. Federated Learning-Based Secure Electronic Health Record Sharing
Scheme in Medical Informatics. IEEE J Biomed Health Inform. 2023 Feb;27(2):617-624.
doi: 10.1109/JBHI.2022.3174823.
- 11 Peters U, Turner B, Alvarez D, Murray M, Sharma A, Mohan S, Patel S. Considerations
for Embedding Inclusive Research Principles in the Design and Execution of Clinical
Trials. Ther Innov Regul Sci. 2023 Mar;57(2):186-195. doi: 10.1007/s43441-022-00464-3.