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Yearb Med Inform 2016; 25(01): 219-223
DOI: 10.15265/IY-2016-044
IMIA and Schattauer GmbH
Georg Thieme Verlag KG Stuttgart

Clinical Research Informatics Contributions from 2015

C. Daniel
1   INSERM UMRS 1142, Paris, France
2   Direction of Information Systems, AP-HP, Paris, France
,
R. Choquet
1   INSERM UMRS 1142, Paris, France
3   BNDMR, Necker Hospital for Children, AP-HP, Paris, France
,
Section Editors for the IMIA Yearbook Section on Clinical Research Informatics › Author Affiliations
Further Information

Publication History

10 November 2016

Publication Date:
06 March 2018 (online)

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Summary

Objectives: To summarize key contributions to current research in the field of Clinical Research Informatics (CRI) and to select best papers published in 2015.

Method: A bibliographic search using a combination of MeSH and free terms search over PubMed on Clinical Research Informatics (CRI) was performed 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 finally organized to conclude on the selection of best papers.

Results: Among the 579 returned papers published in the past year in the various areas of Clinical Research Informatics (CRI) - i) methods supporting clinical research, ii) data sharing and interoperability, iii) re-use of healthcare data for research, iv) patient recruitment and engagement, v) data privacy, security and regulatory issues and vi) policy and perspectives - the full review process selected four best papers. The first selected paper evaluates the capability of the Clinical Data Interchange Standards Consortium (CDISC) Operational Data Model (ODM) to support the representation of case report forms (in both the design stage and with patient level data) during a complete clinical study lifecycle. The second selected paper describes a prototype for secondary use of electronic health records data captured in non-standardized text. The third selected paper presents a privacy preserving electronic health record linkage tool and the last selected paper describes how big data use in US relies on access to health information governed by varying and often misunderstood legal requirements and ethical considerations.

Conclusions: A major trend in the 2015 publications is the analysis of observational, “nonexperimental” information and the potential biases and confounding factors hidden in the data that will have to be carefully taken into account to validate new predictive models. In addiction, researchers have to understand complicated and sometimes contradictory legal requirements and to consider ethical obligations in order to balance privacy and promoting discovery.

Keywords

Medical informatics - clinical research informatics - biomedical research - patient selection - phenotyping
 

  • References

  • 1 Alonso-Calvo R, Perez-Rey D, Paraiso-Medina S, Claerhout B, Hennebert P, Bucur A. Enabling semantic interoperability in multi-centric clinical trials on breast cancer.. Comput Methods Programs Biomed 2015; Mar 118 (Suppl. 03) 322-9.
    CrossrefPubMedGoogle Scholar
  • 2 Bellazzi R, Dagliati A, Sacchi L, Segagni D. Big Data Technologies: New Opportunities for Diabetes Management.. J Diabetes Sci Technol 2015; Apr 24 9 (Suppl. 05) 1119-25.
    CrossrefPubMedGoogle Scholar
  • 3 Choquet R, Maaroufi M, de Carrara A, Messiaen C, Luigi E, Landais P. A methodology for a minimum data set for rare diseases to support national centers of excellence for healthcare and research.. J Am Med Inform Assoc 2015; Jan 22 (Suppl. 01) 76-85.
    CrossrefPubMedGoogle Scholar
  • 4 Dumitrescu L, Goodloe R, Bradford Y, Farber-Eger E, Boston J, Crawford DC. The effects of electronic medical record phenotyping details on genetic association studies: HDL-C as a case study.. BioData Min 2015; May 6 8: 15.
    CrossrefPubMedGoogle Scholar
  • 5 Gray EA, Thorpe JH. Comparative effectiveness research and big data: balancing potential with legal and ethical considerations.. J Comp Eff Res 2015; Jan 4 (Suppl. 01) 61-74.
    CrossrefPubMedGoogle Scholar
  • 6 Huser V, Sastry C, Breymaier M, Idriss A, Cimino JJ. Standardizing data exchange for clinical research protocols and case report forms: An assessment of the suitability of the Clinical Data Interchange Standards Consortium (CDISC) Operational Data Model (ODM).. J Biomed Inform 2015 Jul 15.
    PubMedGoogle Scholar
  • 7 Kaye J, Whitley EA, Lund D, Morrison M, Teare H, Melham K. Dynamic consent: a patient interface for twenty-first century research networks.. Eur J Hum Genet 2015; Feb 23 (Suppl. 02) 141-6.
    CrossrefPubMedGoogle Scholar
  • 8 Kho AN, Cashy JP, Jackson KL, Pah AR, Goel S, Boehnke J. et al. Design and implementation of a privacy preserving electronic health record linkage tool in Chicago.. J Am Med Inform Assoc 2015; Sep 22 (Suppl. 05) 1072-80.
    CrossrefPubMedGoogle Scholar
  • 9 Koutkias VG, Jaulent MC. Computational approaches for pharmacovigilance signal detection: toward integrated and semantically-enriched frameworks.. Drug Saf 2015; Mar 38 (Suppl. 03) 219-32.
    CrossrefPubMedGoogle Scholar
  • 10 Kreuzthaler M, Schulz S, Berghold A. Secondary use of electronic health records for building cohort studies through top-down information extraction.. J Biomed Inform 2015; Feb 53: 188-95.
    CrossrefPubMedGoogle Scholar
  • 11 Liang SF, Taweel A, Miles S, Kovalchuk Y, Spiridou A, Barratt B. et al. Semi automated transformation to OWL formatted files as an approach to data integration. A feasibility study using environmental, disease register and primary care clinical data.. Methods Inf Med 2015; 54 (Suppl. 01) 32-40.
    Article in Thieme ConnectPubMedGoogle Scholar
  • 12 Mate S, Köpcke F, Toddenroth D, Martin M, Prokosch HU, Bürkle T, Ganslandt T. Ontology-based data integration between clinical and research systems.. PLoS One 2015; Jan 14 10 (Suppl. 01) e0116656.
    CrossrefPubMedGoogle Scholar
  • 13 Pang C, Hendriksen D, Dijkstra M, van der Velde KJ, Kuiper J, Hillege HL. et al. BiobankConnect: software to rapidly connect data elements for pooled analysis across biobanks using ontological and lexical indexing.. J Am Med Inform Assoc 2015; Jan 22 (Suppl. 01) 65-75. .
    CrossrefPubMedGoogle Scholar
  • 14 Pivovarov R, Perotte AJ, Grave E, Angiolillo J, Wiggins CH, Elhadad N. Learning probabilistic phenotypes from heterogeneous EHR data.. J Biomed Inform 2015; Dec 58: 156-65.
    CrossrefPubMedGoogle Scholar
  • 15 Soto-Rey I, Trinczek B, Girardeau Y, Zapletal E, Ammour N, Doods J. et al. Efficiency and effectiveness evaluation of an automated multi-country patient count cohort system.. BMC Med Res Methodol 2015; May 1 15: 44.
    CrossrefPubMedGoogle Scholar
  • 16 Voss EA, Makadia R, Matcho A, Ma Q, Knoll C, Schuemie M. et al. Feasibility and utility of applications of the common data model to multiple, disparate observational health databases.. J Am Med Inform Assoc 2015; May 22 (Suppl. 03) 553-64.
    PubMedGoogle Scholar