Clinical Research Informatics: Contributions from 2018

Summary

Objectives: To summarize key contributions to current research in the field of Clinical Research Informatics (CRI) and to select best papers published in 2018.

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 of the editorial team was organized to conclude on the selection of best papers.

Results: Among the 1,469 retrieved papers published in 2018 in the various areas of CRI, the full review process selected four best papers. The first best paper describes a simple algorithm detecting co-morbidities in Electronic Healthcare Records (EHRs) using a clinical data warehouse and a knowledge base. The authors of the second best paper present a federated algorithm for predicting heart failure hospital admissions based on patients' medical history described in their distributed EHRs. The third best paper reports the evaluation of an open source, interoperable, and scalable data quality assessment tool measuring completeness of data items, which can be run on different architectures (EHRs and Clinical Data Warehouses (CDWs) based on PCORnet or OMOP data models). The fourth best paper reports a data quality program conducted across 37 hospitals addressing data quality Issues through the whole data life cycle from patient to researcher.

Conclusions: Research efforts in the CRI field currently focus on consolidating promises of early Distributed Research Networks aimed at maximizing the potential of large-scale, harmonized data from diverse, quickly developing digital sources. Data quality assessment methods and tools as well as privacy-enhancing techniques are major concerns. It is also notable that, following examples in the US and Asia, ambitious regional or national plans in Europe are launched that aim at developing big data and new artificial intelligence technologies to contribute to the understanding of health and diseases in whole populations and whole health systems, and returning actionable feedback loops to improve existing models of research and care. The use of “real-world" data is continuously increasing but the ultimate role of this data in clinical research remains to be determined.

Keywords

IMIA Yearbook - clinical research informatics - biomedical research - clinical trials as topic - observational studies as topic - phenotype

• References

• 1 Hemingway H, Asselbergs FW, Danesh J, Dobson R, Maniadakis N, Maggioni A. , et al. Big data from electronic health records for early and late translational cardiovascular research: challenges and potential. Eur Heart J 2018; 39 (16) 1481-95
• 2 Ledieu T, Bouzille G, Thiessard F, Berquet K, Van Hille P, Renault E. , et al. Timeline representation of clinical data: usability and added value for pharmacovigilance. BMC Med Inform Decis Mak 2018; 18 (01) 86
• 3 Sylvestre E, Bouzille G, Chazard E, His-Mahier C, Riou C, Cuggia M. Combining information from a clinical data warehouse and a pharmaceutical database to generate a framework to detect comorbidities in electronic health records. BMC Med Inform Decis Mak 24 2018; 18 (01) 9
• 4 Garcelon N, Neuraz A, Salomon R, Bahi-Buisson N, Amiel J, Picard C. , et al. Next generation phenotyping using narrative reports in a rare disease clinical data warehouse. Orphanet J Rare Di. 31 2018; 13 (01) 85
• 5 Dietrich G, Krebs J, Fette G, Ertl M, Kaspar M, Stork S. , et al. Ad Hoc Information Extraction for Clinical Data Warehouses. Methods Inf Med 2018; 57 (01) e22-9
• 6 Brisimi TS, Chen R, Mela T, Olshevsky A, Paschalidis IC, Shi W. Federated learning of predictive models from federated Electronic Health Records. Int J Med Inf 2018; 112: 59-67
• 7 Paris N, Mendis M, Daniel C, Murphy S, Tannier X, Zweigenbaum P. i2b2 implemented over SMART-on-FHIR. AMIA Jt Summits Transl Sci Proc AMIA Jt Summits Transl Sci 2018; 2017: 369-78
• 8 Devine EB, Van Eaton E, Zadworny ME, Symons R, Devlin A, Yanez D. , et al. Automating Electronic Clinical Data Capture for Quality Improvement and Research: The CERTAIN Validation Project of Real World Evidence. EGEMS Wash DC 22 mai 2018; 6 (01) 8
• 9 Qualls LG, Phillips TA, Hammill BG, Topping J, Louzao DM, Brown JS. , et al. Evaluating Foundational Data Quality in the National Patient-Centered Clinical Research Network (PCORnet^¯). eGEMs [Internet]. [cité 9 déc 2018];6(1). Available at: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5983028/
• 10 Looten V, Kong Win Chang L, Neuraz A, Landau-Loriot MA, Vedie B, Paul JL. , et al. What can millions of laboratory test results tell us about the temporal aspect of data quality? Study of data spanning 17 years in a clinical data warehouse. Comput Methods Programs Biomed 2018 Dec 29
• 11 Daniel C, Serre P, Orlova N, Bréant S, Paris N, Griffon N. Initializing a hospital-wide data quality program. The AP-HP experience. Comput Methods Programs Biomed. 2018 Nov 9
• 12 Estiri H, Stephens KA, Klann JG, Murphy SN. Exploring completeness in clinical data research networks with DQe-c. J Am Med Inform Assoc 2018; 25 (01) 17-24
• 13 Veeningen M, Chatterjea S, Horvath AZ, Spindler G, Boersma E, van der Spek P. , et al. Enabling Analytics on Sensitive Medical Data with Secure Multi-Party Computation. Stud Health Technol Inform 2018; 247: 76-80
• 14 Semler SC, Wissing F, Heyder R. German Medical Informatics Initiative. Methods Inf Med 2018; 57 (S01): e50-6
• 15 Cuggia M, Combes S. The French Health Data Hub and the German Medical Informatics Initiative: Two National Projects to Promote Data Sharing in Healthcare. Yearb Med Inform 2019; 190-202