Patient Registries for Clinical Research

  • Rachel L. RichessonEmail author
  • Leon Rozenblit
  • Kendra Vehik
  • James E. Tcheng
Part of the Health Informatics book series (HI)


Patient registries are fundamental to biomedical research. Registries provide consistent data for defined populations and can be used to support the study of the determinants and manifestations of disease and provide a picture of the natural history, outcomes of treatment, and experiences of individuals with a given condition or exposure. It is anticipated that electronic health record (EHR) systems will evolve to ubiquitously capture detailed clinical data that supports observational, and ultimately interventional, research. Emerging data representation and exchange standards can enable the interoperability required for automated transmission of clinical data into patient registries. This chapter describes informatics principles and approaches relevant to the design and implementation of patient registries, with emphasis on the ingestion of clinical data and the role of patient registries in research and learning health activities.


Registries Clinical research Secondary data use Observational research methods Data standards Interoperability Outcomes measurement Learning health systems 


  1. 1.
    AHRQ. In: Gliklich RE, Dreyer NA, editors. Registries for evaluating patient outcomes: a user’s guide. Rockville: Agency for Healthcare Research and Quality; 2010.Google Scholar
  2. 2.
    Travers K, et al. Characteristics and temporal trends in patient registries: focus on the life sciences industry, 1981–2012. Pharmacoepidemiol Drug Saf. 2015;24(4):389–98.CrossRefGoogle Scholar
  3. 3.
    Muilu J, Peltonen L, Litton JE. The federated database – a basis for biobank-based post-genome studies, integrating phenome and genome data from 600,000 twin pairs in Europe. Eur J Hum Genet. 2007;15(7):718–23.CrossRefGoogle Scholar
  4. 4.
    Nakamura Y. The BioBank Japan project. Clin Adv Hematol Oncol. 2007;5(9):696–7.PubMedGoogle Scholar
  5. 5.
    Ollier W, Sprosen T, Peakman T. UK Biobank: from concept to reality. Pharmacogenomics. 2005;6(6):639–46.CrossRefGoogle Scholar
  6. 6.
    Sandusky G, Dumaual C, Cheng L. Review paper: human tissues for discovery biomarker pharmaceutical research: the experience of the Indiana University Simon Cancer Center-Lilly Research Labs Tissue/Fluid BioBank. Vet Pathol. 2009;46(1):2–9.CrossRefGoogle Scholar
  7. 7.
    Horsley K. Florence Nightingale. J Mil Veterans’ Health. 2018;18(4):2–5.Google Scholar
  8. 8.
    Military Records. Civil war records: basic research sources. 2018 [cited 2018 July 1, 2018]. Available from:
  9. 9.
    Patient registries. In: DN, Gliklich RE, Leavy MB, editors. Registries for evaluating patient outcomes: a user’s guide [Internet]. 3rd ed. Rockville: Agency for Healthcare Research and Quality (US); 2014.Google Scholar
  10. 10.
    CMS. Centralized repository/RoPR. 2018a. [cited 2018 June 23]. Available from: RIncentivePrograms/CentralizedRepository-.html.
  11. 11.
    FDA. Guidance for industry and FDA staff. Procedures for handling post-approval studies imposed by PMA order. Rockville: U.S. Food and Drug Administration; 2007.Google Scholar
  12. 12.
    Hollak CE, et al. Limitations of drug registries to evaluate orphan medicinal products for the treatment of lysosomal storage disorders. Orphanet J Rare Dis. 2011;6:16.CrossRefGoogle Scholar
  13. 13.
    Clinical Trials Transformation Initiative (CTTI). CTTI recommendations: registry trials. 2017. [cited 2018 June 23]. Available from:
  14. 14.
    Stey AM, et al. Clinical registries and quality measurement in surgery: a systematic review. Surgery. 2015;157(2):381–95.CrossRefGoogle Scholar
  15. 15.
    CMS. Quality measures requirements. 2018b [cited 2018 June 23]. Available from:
  16. 16.
    Platt R, et al. Clinician engagement for continuous learning discussion paper. Washington, DC: National Academy of Medicine; 2017.Google Scholar
  17. 17.
    AHRQ. Bringing the patient voice to evidence generation: patient engagement in disease registries. (AHRQ Views. Blog posts from AHRQ leaders). 2018. [cited 2018 June 23]. Available from:
  18. 18.
    IOM. The learning healthcare system: workshop summary. Washington, DC: The National Academies Press; 2007.Google Scholar
  19. 19.
    ONC. Introduction to the interoperability standards advisory. 2018a. [cited 2018 June 23]. Available from:
  20. 20.
    Chute CG. Medical concept representation. In: Chen H, et al., editors. Medical informatics. Knowledge management and data mining in biomedicine. New York: Springer; 2005. p. 163–82.Google Scholar
  21. 21.
    ONC. 2015 edition certification companion guide. 2015 edition common clinical data set – 45 CFR 170.102. 2018b. [cited 2018 June 23]. Available from:
  22. 22.
    NLM. The NIH common data element (CDE) resource portal. 2013. [cited 2013 March 6]. Available from:
  23. 23.
    CMS. Data element library. 2018. [cited 2018 June 23]. Available from:
  24. 24.
    Sood HS, et al. Has the time come for a unique patient identifier for the U.S.? NEJM Catalyst. 2018.Google Scholar
  25. 25.
    Dusetzina SB, Tyree S, Meyer AM, et al. Linking data for health services research: a framework and instructional guide [Internet]. In:An overview of record linkage methods. Rockville: Agency for Healthcare Research and Quality (US); 2014.Google Scholar
  26. 26.
  27. 27.
    Drozda JP Jr, et al. Constructing the informatics and information technology foundations of a medical device evaluation system: a report from the FDA unique device identifier demonstration. J Am Med Inform Assoc: JAMIA. 2018;25(2):111–20.CrossRefGoogle Scholar
  28. 28.
    Campbell WS, et al. An alternative database approach for management of SNOMED CT and improved patient data queries. J Biomed Inform. 2015;57:350–7.CrossRefGoogle Scholar
  29. 29.
    PheKB. 2012. [cited 2013 May 24]. Vanderbilt University. Available from:
  30. 30.
    NLM. NLM Value Set Authority Center (VSAC). 2015. Feb 11, 2015 [cited 2015 March 11]. Available from:
  31. 31.
    PheMA. PheMA wiki: phenotype execution modeling architecture project. 2015. [cited 2015 September 28]. Available from:
  32. 32.
    Richesson RL, et al. Electronic health records based phenotyping in next-generation clinical trials: a perspective from the NIH health care systems collaboratory. J Am Med Inform Assoc. 2013;20(e2):e226–31.CrossRefGoogle Scholar
  33. 33.
    Richesson RL, Smerek MM, Blake Cameron C. A framework to support the sharing and reuse of computable phenotype definitions across health care delivery and clinical research applications. EGEMS (Washington, DC). 2016;4(3):1232.Google Scholar
  34. 34.
    Gliklich RE, et al. Registry of patient registries outcome measures framework: information model report. Methods research report, Prepared by L&M Policy Research, LLC, under Contract No. 290-2014-00004-C. Rockville: Agency for Healthcare Research and Quality (US); 2018.Google Scholar
  35. 35.
    Cochi SL, et al. Congenital rubella syndrome in the United States, 1970–1985. On the verge of elimination. Am J Epidemiol. 1989;129(2):349–61.CrossRefGoogle Scholar
  36. 36.
    Tilling K. Capture-recapture methods – useful or misleading? Int J Epidemiol. 2001;30(1):12–4.CrossRefGoogle Scholar
  37. 37.
    Rothman K, Greenland S. Modern epidemiology. 2nd ed. Hagerstown: Lippincott Williams and Wilkins; 1998.Google Scholar
  38. 38.
    AHRQ. In: Gliklich RE, Dreyer NA, editors. Registries for evaluating patient outcomes: a user’s guide. Rockville: Agency for Healthcare Research and Quality; 2007.Google Scholar
  39. 39.
    Sanborn TA, et al. ACC/AHA/SCAI 2014 health policy statement on structured reporting for the cardiac catheterization laboratory: a report of the American College of Cardiology Clinical Quality Committee. J Am Coll Cardiol. 2014;63(23):2591–623.CrossRefGoogle Scholar
  40. 40.
    Wickham H. Tidy data. 2014., 2014;59(10):23.Google Scholar
  41. 41.
    Blumenthal S. The use of clinical registries in the United States: a landscape survey. eGEMs (Generating evidence & methods to improve patient outcomes). 2017;5(1):26.Google Scholar
  42. 42.
    Chute CG, Huff SM. The pluripotent rendering of clinical data for precision medicine. Stud Health Technol Inform. 2017;245:337–40. Available from:
  43. 43.
    ONC. Common clinical data set. 2015. [cited 2018 June 25]. Available from:
  44. 44.
    S4S. Sync for science (S4S). Helping patients share EHR data with researchers. 2018. [cited 2018 June 25]. Available from:
  45. 45.
    Sankar PL, Parker LS. The precision medicine initiative’s all of us research program: an agenda for research on its ethical, legal, and social issues. Genet Med: Off J Am Coll Med Genet. 2017;19(7):743–50.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing 2019

Authors and Affiliations

  • Rachel L. Richesson
    • 1
    Email author
  • Leon Rozenblit
    • 2
  • Kendra Vehik
    • 3
  • James E. Tcheng
    • 4
  1. 1.Duke University School of NursingDurhamUSA
  2. 2.Prometheus Research, LLCNew HavenUSA
  3. 3.University of South Florida, Health Informatics InstituteTampaUSA
  4. 4.Duke University School of MedicineDurhamUSA

Personalised recommendations