Clinical Research Informatics: Contributions from 2024
- 1Sorbonne University, Inserm, University Sorbonne Paris-Nord, Limics, F-75006 Paris France
- 2Ghent University, Faculty of Medicine and Health Sciences, Department of Public Health and Primary Care, Unit of Medical Informatics and Statistics, Ghent Belgium
Abstract
Objectives : To summarize key contributions to research in Clinical Research Informatics (CRI) and to select the best papers published in 2024.
Method : 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 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 three section editors and the editorial team was organized to finally conclude on the selected two best papers.
Results : Among the 1417 papers returned by the search, published in 2024, that were in the scope of the various areas of CRI, the full review process selected two best papers. The first best paper investigates how to identify and mitigate group biases—such as subgroup underrepresentation—in computational phenotyping. The second-best paper addresses group bias in clinical trials by examining how restrictive eligibility criteria and enrollment challenges affect patient representativeness at both the population and subgroup levels. This year's survey paper is a scoping review related to digital health and environmental sustainability, highlighting the urgent need to embed sustainable practices across the technology lifecycle and the critical regulatory gaps in addressing environmental impacts.
Conclusions : The literature relevant to CRI in 2024 highlights key themes including bias and representativeness, quality and interoperability, and advances in phenotyping as well as real-world evidence extraction and generation.
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BibTeX
@article{tannier2026clinical,
title = {{Clinical Research Informatics: Contributions from 2024}},
author = {Xavier Tannier and Dipak Kalra and Pascal Coorevits},
journal = {Yearbook of Medical Informatics},
year = {2026},
month = nov,
doi = {10.1055/s-0046-1816614},
url = {https://doi.org/10.1055/s-0046-1816614},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Clinical Research Informatics: Contributions from 2024
AU - Xavier Tannier
AU - Dipak Kalra
AU - Pascal Coorevits
PY - 2026
DA - 2026/11/03
JO - Yearbook of Medical Informatics
DO - 10.1055/s-0046-1816614
UR - https://doi.org/10.1055/s-0046-1816614
AB - Objectives : To summarize key contributions to research in Clinical Research Informatics (CRI) and to select the best papers published in 2024. Method : 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 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 three section editors and the editorial team was organized to finally conclude on the selected two best papers. Results : Among the 1417 papers returned by the search, published in 2024, that were in the scope of the various areas of CRI, the full review process selected two best papers. The first best paper investigates how to identify and mitigate group biases—such as subgroup underrepresentation—in computational phenotyping. The second-best paper addresses group bias in clinical trials by examining how restrictive eligibility criteria and enrollment challenges affect patient representativeness at both the population and subgroup levels. This year's survey paper is a scoping review related to digital health and environmental sustainability, highlighting the urgent need to embed sustainable practices across the technology lifecycle and the critical regulatory gaps in addressing environmental impacts. Conclusions : The literature relevant to CRI in 2024 highlights key themes including bias and representativeness, quality and interoperability, and advances in phenotyping as well as real-world evidence extraction and generation.
SN - 0943-4747
SN - 2364-0502
ER -