Yearb Med Inform 2013; 22(01): 178-184
DOI: 10.1055/s-0038-1638853
Original Article
Georg Thieme Verlag KG Stuttgart

Clinical Research Informatics: Survey of Recent Advances and Trends in a Maturing Field

P. J. Embi
1   The Ohio State University, Columbus, OH, USA
› Author Affiliations
Further Information

Correpsondence to:

Peter J. Embi, MD, MS, FACP, FACMI
The Ohio State University
3190 Graves Hall
333 W. 10th Ave
Columbus, OH 43210, USA
Phone: +1 614 292 4778   
Fax: +1 614 688 6600   

Publication History

Publication Date:
05 March 2018 (online)

 

Summary

Objectives: To provide a survey of the field of clinical research informatics (CRI), focusing in particular on significant developments over the past 3 years and the insights they provide about the progress and state of the field.

Methods: An iterative “scoping review” of the literature published in scientific journals and conference proceedings that are relevant to CRI, from late 2009 to early 2013.

Results: 212 articles were identified, and 64 were selected to illustrate recent advances in the field. Based on those, six categories of CRI activity were identified: Data and Knowledge Management, Discovery and Standards; Clinical Data Re-Use for Research; Researcher Support and Resources; Participant Recruitment; Patients/Consumers and CRI; Policy, Regulatory and Fiscal Matters.

Conclusions: This survey demonstrates that the field of CRI has matured and is now well established. The ongoing work is essential to overcome many of the challenges the clinical research enterprise is facing and more work is needed. Even as work continues to establish necessary infrastructure, methods, and best practices, CRI researchers should strive for more rigorous study designs to evaluate the impacts of the work in the field. There is little doubt that the field is poised for rapid growth, and that the CRI literature will continue to reflect that growth in years to come.


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Correpsondence to:

Peter J. Embi, MD, MS, FACP, FACMI
The Ohio State University
3190 Graves Hall
333 W. 10th Ave
Columbus, OH 43210, USA
Phone: +1 614 292 4778   
Fax: +1 614 688 6600   

  • References

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  • 3 Zerhouni EA. Translational and clinical science-time for a new vision. N Engl J Me. 2005; Oct 13 353 (15) 1621-3.
  • 4 Zerhouni EA. Clinical research at a crossroads: the NIH roadmap. J Investig Med 2006; May 1 54 (4) 171-3.
  • 5 Payne PR, Johnson SB, Starren JB, Tilson HH, Dowdy D. Breaking the translational barriers: the value of integrating biomedical informatics and translational research. J Investig Med 2005; May 53 (4) 192-200.
  • 6 Sung NS, Crowley Jr. WF, Genel M, Salber P, Sandy L, Sherwood LM. et al. Central challenges facing the national clinical research enterprise. JAMA 2003; Mar 12 289 (10) 1278-87.
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  • 11 Oster S, Langella S, Hastings S, Ervin D, Madduri R, Phillips J. et al. caGrid 1.0: An Enterprise Grid Infrastructure for Biomedical Research. J Am Med Inform Assoc 2008; Jan 1 15 (2) 138-49.
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  • 22 Anderson N, Abend A, Mandel A, Geraghty E, Gabriel D, Wynden R. et al. Implementation of a deidentified federated data network for population-based cohort discovery. J Am Med Inform Assoc. 2011 Aug 26.
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  • 25 Sim I, Carini S, Tu S, Wynden R, Pollock BH, Mollah SA. et al. The human studies database project: federating human studies design data using the ontology of clinical research. AMIA Summits Transl Sci Proc 2010; Jan 1 2010: 51-5.
  • 26 Richesson RL, Krischer J. Data standards in clinical research: gaps, overlaps, challenges and future directions. J Am Med Inform Assoc 2007; 14 (6) 687-96.
  • 27 Kush RD, Helton E, Rockhold FW, Hardison CD. Electronic health records, medical research, and the Tower of Babel. N Engl J Med 2008; 358 (16) 1738-40.
  • 28 Holzer K, Gall W. Utilizing IHE-based electronic health record systems for secondary use. Methods Inf Med 2011; 50 (4) 319.
  • 29 Fridsma DB, Evans J, Hastak S, Mead CN. The BRIDG project: a technical report. J Am Med Inform Assoc 2008; Mar-Apr 15 (2) 130-7.
  • 30 Breil B, Kenneweg J, Fritz F, Bruland P, Doods D, Trinczek B. et al. Multilingual medical data models in ODM format-a novel form-based approach to semantic interoperability between routine health-care and clinical research. Appl Clin Inf 2012; 3: 276-89.
  • 31 Weng C, Tu SW, Sim I, Richesson R. Formal representations of eligibility criteria: A literature review. J Biomed Inform 2010; 43 (3) 451.
  • 32 Tenenbaum JD, Whetzel PL, Anderson K, Borromeo CD, Dinov ID, Gabriel D. et al. The Biomedical Resource Ontology (BRO) to enable resource discovery in clinical and translational research. J Biomed Inform 2011; Mar 44 (1) 137-45.
  • 33 Kong YM, Dahlke C, Xiang Q, Qian Y, Karp D, Scheuermann RH. Toward an ontology-based framework for clinical research databases. Journal ofbiomedical informatics. 2011; Mar 44 (1) 48-58.
  • 34 Szalma S, Koka V, Khasanova T, Perakslis ED. Effective knowledge management in translational medicine. J Transl Med. 2010 Jan 1 8. 68.
  • 35 Richesson RL, Nadkarni P. Data standards for clinical research data collection forms: current status and challenges. J Am Med Inform Assoc 2011; May 12 18 (3) 341-6.
  • 36 Shaw M, Detwiler LT, Noy N, Brinkley J, Suciu D. vSPARQL: a view definition language for the semantic web. J Biomed Inform 2011; Mar 44 (1) 102-17.
  • 37 Wynden R, Weiner M, Sim I, Gabriel D, Casale M, Carini S. et al. Ontology mapping and data discovery for the translational investigator. AMIA Summits Transl Sci Proc 2010; 2010: 66-70.
  • 38 Rea S, Pathak J, Savova G, Oniki TA, Westberg L, Beebe CE. et al. Building a robust, scalable and standards-driven infrastructure for secondary use of EHR data: The SHARPn project. J Biomed Inform 2012; 45 (4) 763-71.
  • 39 Behrman RE, Benner JS, Brown JS, McClellan M, Woodcock J, Platt R. Developing the Sentinel System-a national resource for evidence development. N Engl J Med 2011; 364 (6) 498-9.
  • 40 El Fadly A, Rance B, Lucas N, Mead C, Chatellier G, Lastic P-Y. et al. Integrating clinical research with the healthcare enterprise: from the RE-USE project to the EHR4CR platform. J Biomed Inform 2011; 44: S94-S102.
  • 41 Carroll RJ, Thompson WK, Eyler AE, Mandelin AM, Cai T, Zink RM. et al. Portability of an algorithm to identify rheumatoid arthritis in electronic health records. J Am Med Inform Assoc. 2012 Mar 28.
  • 42 Denny JC, Ritchie MD, Basford MA, Pulley JM, Bastarache L, Brown-Gentry K. et al. PheWAS: demonstrating the feasibility of a phenome-wide scan to discover gene-disease associations. Bioin-formatics 2010; May 1 26 (9) 1205-10.
  • 43 Ritchie MD, Denny JC, Crawford DC, Ramirez AH, Weiner JB, Pulley JM. et al. Robust replication of genotype-phenotype associations across multiple diseases in an electronic medical record. Am J Hum Genet 2010; Apr 9 86 (4) 560-72.
  • 44 Kho AN, Pacheco JA, Peissig PL, Rasmussen L, Newton KM, Weston N. et al. Electronic medical records for genetic research: results of the eMERGE consortium. Sci Transl Med. 2011 Apr 20 3. (79) 79re1.
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