CC BY-NC-ND 4.0 · Yearb Med Inform 2018; 27(01): 177-183
DOI: 10.1055/s-0038-1641220
Section 8: Clinical Research Informatics
Georg Thieme Verlag KG Stuttgart

Clinical Research Informatics: Contributions from 2017

Christel Daniel
1  AP-HP Direction of Information Systems, Paris, France
2  Sorbonne University, University Paris 13, Sorbonne Paris Cité, INSERM UMR_S 1142, LIMICS, Paris, France
Dipak Kalra
3  University of Gent, Belgium
Section Editors for the IMIA Yearbook Section on Clinical Research Informatics› Author Affiliations
Further Information

Publication History

Publication Date:
29 August 2018 (online)



Objectives: To summarize key contributions to current research in the field of Clinical Research Informatics (CRI) and to select best papers published in 2017.

Method: A bibliographic search using a combination of MeSH descriptors and free 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. A consensus meeting between the two section editors and the editorial team was organized to finally conclude on the selection of best papers.

Results: Among the 741 returned papers published in 2017 in the various areas of CRI, the full review process selected five best papers. The first best paper reports on the implementation of consent management considering patient preferences for the use of de-identified data of electronic health records for research. The second best paper describes an approach using natural language processing to extract symptoms of severe mental illness from clinical text. The authors of the third best paper describe the challenges and lessons learned when leveraging the EHR4CR platform to support patient inclusion in academic studies in the context of an important collaboration between private industry and public health institutions. The fourth best paper describes a method and an interactive tool for case-crossover analyses of electronic medical records for patient safety. The last best paper proposes a new method for bias reduction in association studies using electronic health records data.

Conclusions: Research in the CRI field continues to accelerate and to mature, leading to tools and platforms deployed at national or international scales with encouraging results. Beyond securing these new platforms for exploiting large-scale health data, another major challenge is the limitation of biases related to the use of “real-world” data. Controlling these biases is a prerequisite for the development of learning health systems.