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Clinical Research Informatics
21 August 2020 (online)
Objectives: To summarize key contributions to current research in the field of Clinical Research Informatics (CRI) and to select best papers published in 2019.
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 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 517 papers, published in 2019, returned by the search, 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 use of a homomorphic encryption technique to enable federated analysis of real-world data while complying more easily with data protection requirements. The authors of the second best paper demonstrate the evidence value of federated data networks reporting a large real world data study related to the first line treatment for hypertension. The third best paper reports the migration of the US Food and Drug Administration (FDA) adverse event reporting system database to the OMOP common data model. This work opens the combined analysis of both spontaneous reporting system and electronic health record (EHR) data for pharmacovigilance.
Conclusions: The most significant research efforts in the CRI field are currently focusing on real world evidence generation and especially the reuse of EHR data. With the progress achieved this year in the areas of phenotyping, data integration, semantic interoperability, and data quality assessment, real world data is becoming more accessible and reusable. High quality data sets are key assets not only for large scale observational studies or for changing the way clinical trials are conducted but also for developing or evaluating artificial intelligence algorithms guiding clinical decision for more personalized care. And lastly, security and confidentiality, ethical and regulatory issues, and more generally speaking data governance are still active research areas this year.
- 1 Denaxas S, Gonzalez-Izquierdo A, Direk K, Fitzpatrick NK, Fatemifar G, Banerjee A. et al. UK phenomics platform for developing and validating electronic health record phenotypes: CALIBER. J Am Med Inform Assoc 2019; 26 (12) 1545-59
- 2 Suchard MA, Schuemie MJ, Krumholz HM, You SC, Chen R, Pratt N. et al. Comprehensive comparative effectiveness and safety of first-line antihypertensive drug classes: a systematic, multinational, large-scale analysis. Lancet 2019; 394 10211 1816-26
- 3 Wang C, Rosner GL. A Bayesian nonparametric causal inference model for synthesizing randomized clinical trial and real-world evidence. Stat Med 2019; 38 (14) 2573-88
- 4 Yu Y, Ruddy KJ, Hong N, Tsuji S, Wen A, Shah ND, Jiang G. ADEpedia-on-OHDSI: A next generation pharmacovigilance signal detection platform using the OHDSI common data model. J Biomed Inform 2019; 91: 103119
- 5 Glicksberg BS, Oskotsky B, Giangreco N, Thangaraj PM, Rudrapatna V, Datta D. et al. ROMOP: a light-weight R package for interfacing with OMOP-formatted electronic health record data. JAMIA Open 2019; 2 (01) 10-4
- 6 Prasser F, Kohlbacher O, Mansmann U, Bauer B, Kuhn KA. Data Integration for Future Medicine (DIFUTURE). Methods Inf Med 2018; 57 (S01): e57-e65
- 7 Paddock S, Abedtash H, Zummo J, Thomas S. Proof-of-concept study: Homomorphically encrypted data can support real-time learning in personalized cancer medicine. BMC Med Inform Decis Mak 2019; 19 (01) 255
- 8 Chen J, Chun D, Patel M, Chiang E, James J. The validity of synthetic clinical data: a validation study of a leading synthetic data generator (Synthea) using clinical quality measures. BMC Med Inform Decis Mak 2019; 19 (01) 44
- 9 Schüssler-Fiorenza Rose SM, Contrepois K, Moneghetti KJ, Zhou W, Mishra T, Mataraso S. et al. A longitudinal big data approach for precision health. Nat Med 2019; 25 (05) 792-804
- 10 Fang G, Annis IE, Elston-Lafata J, Cykert S. Applying machine learning to predict real-world individual treatment effects: insights from a virtual patient cohort. J Am Med Inform Assoc 2019; 26 (10): 977-88
- 11 Zhang Y, Cai T, Yu S, Cho K, Hong C, Sun J. et al. High-throughput phenotyping with electronic medical record data using a common semi-supervised approach (PheCAP). Nat Protoc 2019; 14 (12) 3426-44
- 12 Meystre SM, Heider PM, Kim Y, Aruch DB, Britten CD. Automatic trial eligibility surveillance based on unstructured clinical data. Int J Med Inf 2019; 129: 13-9
- 13 Miller HN, Gleason KT, Juraschek SP, Plante TB, Lewis-Land C, Woods B. et al. Electronic medical record-based cohort selection and direct-to-patient, targeted recruitment: early efficacy and lessons learned. J Am Med Inform Assoc 2019; 26 (11) 1209-17
- 14 Claerhout B, Kalra D, Mueller C, Singh G, Ammour N, Meloni L. et al Federated electronic health records research technology to support clinical trial protocol optimization: Evidence from EHR4CR and the InSite platform. J Biomed Inform 2019; 90: 103090
- 15 Carrigan G, Whipple S, Capra WB, Taylor MD, Brown JS, Lu M. et al. Using Electronic Health Records to Derive Control Arms for Early Phase Single-Arm Lung Cancer Trials: Proof-of-Concept in Randomized Controlled Trials. Clin Pharmacol Ther 2020; 107 (02) 369-77
- 16 Beier K, Schweda M, Schicktanz S. Taking patient involvement seriously: a critical ethical analysis of participatory approaches in data-intensive medical research. BMC Med Inform Decis Mak 2019; 19 (01) 90