Clinical Research Informatics: Contributions from 2017
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.
- 1 Kim H, Bell E, Kim J, Sitapati A, Ramsdell J, Farcas C. , et al. iCONCUR: informed consent for clinical data and bio-sample use for research. J Am Med Inform Assoc 2017; 24 (02) 380-387
- 2 Jackson RG, Patel R, Jayatilleke N, Kolliakou A, Ball M, Gorrell G. , et al. Natural language processing to extract symptoms of severe mental illness from clinical text: the Clinical Record Interactive Search Comprehensive Data Extraction (CRIS-CODE) project. BMJ Open 2017; 7 (01) e012012
- 3 Lucini FR, , S Fogliatto F, C da Silveira GJ, L Neyeloff J, Anzanello MJ. , de S Kuchenbecker R, , et al. Text mining approach to predict hospital admissions using early medical records from the emergency department. Int J Med Inf 2017; 100: 1-8
- 4 Marella WM, Sparnon E, Finley E. Screening Electronic Health Record-Related Patient Safety Reports Using Machine Learning. J Patient Saf 2017; 13 (01) 31-36
- 5 Girardeau Y, Doods J, Zapletal E, Chatellier G, Daniel C, Burgun A. , et al. Leveraging the EH-R4CR platform to support patient inclusion in academic studies: challenges and lessons learned. BMC Med Res Methodol 2017; 17 (01) 36
- 6 Alonso-Calvo R, Paraiso-Medina S, Perez-Rey D, Alonso-Oset E, van Stiphout R, Yu S. , et al. A semantic interoperability approach to support integration of gene expression and clinical data in breast cancer. Comput Biol Med 2017; 87: 179-186
- 7 Knowledge Base workgroup of the Observational Health Data Sciences and Informatics (OHDSI) collaborative. Large-scale adverse effects related to treatment evidence standardization (LAERTES): an open scalable system for linking pharmacovigi-lance evidence sources with clinical data. J Biomed Semant 2017; 8 (01) 11
- 8 Springate DA, Parisi R, Olier I, Reeves D, Kontopantelis E. rEHR: An R package for manipulating and analysing Electronic Health Record data. PloS One 2017; 12 (02) e0171784
- 9 Caron A, Chazard E, Muller J, Perichon R, Ferret L, Koutkias V. , et al. IT-CARES: an interactive tool for case-crossover analyses of electronic medical records for patient safety. J Am Med Inform Assoc 2017; 24 (02) 323-330
- 10 Jannot A-S, Zapletal E, Avillach P, Mamzer M-F, Burgun A, Degoulet P. The Georges Pompidou University Hospital Clinical Data Warehouse: A 8-years follow-up experience. Int J Med Inform 2017; 102: 21-28
- 11 Hemingway H, Feder GS, Fitzpatrick NK, Denaxas S, Shah AD, Timmis AD. Using nationwide ‘big data’ from linked electronic health records to help improve outcomes in cardiovascular diseases: 33 studies using methods from epidemiology, informatics, economics and social science in the ClinicAl disease research using Linked Bespoke studies and Electronic health Records (CALIBER) programme [Internet]. Southampton (UK): NIHR Journals Library; 2017 [cité 10 juin 2018]. (Programme Grants for Applied Research). Accessible at: http://www.ncbi.nlm.nih.gov/books/NBK414778/
- 12 Dernoncourt F, Lee JY, Uzuner O, Szolovits P. De-identification of patient notes with recurrent neural networks. J Am Med Inform Assoc 2017; 24 (03) 596-606
- 13 McIntosh LD, Juehne A, Vitale CRH, Liu X, Alcoser R, Lukas JC. , et al. Repeat: a framework to assess empirical reproducibility in biomedical research. BMC Med Res Methodol 2017; 17 (01) 143
- 14 Harron K, Hagger-Johnson G, Gilbert R, Goldstein H. Utilising identifier error variation in linkage of large administrative data sources. BMC Med Res Methodol 2017; 17 (01) 23
- 15 Huang J, Duan R, Hubbard RA, Wu Y, Moore JH, Xu H. , et al. PIE: A prior knowledge guided integrated likelihood estimation method for bias reduction in association studies using electronic health records data. J Am Med Inform Assoc 2017 Dec 1.