Journal of Digital Imaging

, Volume 29, Issue 6, pp 638–644

Toward Data-Driven Radiology Education—Early Experience Building Multi-Institutional Academic Trainee Interpretation Log Database (MATILDA)

  • Po-Hao Chen
  • Thomas W. Loehfelm
  • Aaron P. Kamer
  • Andrew B. Lemmon
  • Tessa S. Cook
  • Marc D. Kohli
Article

Abstract

The residency review committee of the Accreditation Council of Graduate Medical Education (ACGME) collects data on resident exam volume and sets minimum requirements. However, this data is not made readily available, and the ACGME does not share their tools or methodology. It is therefore difficult to assess the integrity of the data and determine if it truly reflects relevant aspects of the resident experience. This manuscript describes our experience creating a multi-institutional case log, incorporating data from three American diagnostic radiology residency programs. Each of the three sites independently established automated query pipelines from the various radiology information systems in their respective hospital groups, thereby creating a resident-specific database. Then, the three institutional resident case log databases were aggregated into a single centralized database schema. Three hundred thirty residents and 2,905,923 radiologic examinations over a 4-year span were catalogued using 11 ACGME categories. Our experience highlights big data challenges including internal data heterogeneity and external data discrepancies faced by informatics researchers.

Keywords

Radiology training Residency Case log Education Database Big data Analytics ACGME 

References

  1. 1.
    Williamson KB, Gunderman RB, Cohen MD, Frank MS: Learning Theory in Radiology Education1. Radiology 233(1):15–18, 2004CrossRefPubMedGoogle Scholar
  2. 2.
    Accreditation Council for Graduate Medical Education. Diagnostic Radiology Case Log Minimums [Internet]. [cited 2014 Jul 1]. Available from: https://www.acgme.org/acgmeweb/Portals/0/PFAssets/ProgramResources/420_DR_Case_Log_Minimums.pdf
  3. 3.
    Compliance Guidance: The Mammography Quality Standards Act Final Regulations: Preparing For MQSA Inspections [Internet]. U.S. Food and Drug Administration. 2001 [cited 2014 Jul 4]. Available from: http://www.fda.gov/MedicalDevices/DeviceRegulationandGuidance/GuidanceDocuments/ucm094420.htm
  4. 4.
    Budoff MJ, Cohen MC, Garcia MJ, Hodgson JM, Hundley WG, Lima JAC, et al: ACCF/AHA clinical competence statement on cardiac imaging with computed tomography and magnetic resonance. Circulation 112(4):598–617, 2005CrossRefPubMedGoogle Scholar
  5. 5.
    Nachiappan AC, Wynne DM, Katz DP, Willis MH, Bushong SC: A proposed medical physics curriculum: preparing for the 2013 ABR examination. J Am Coll Radiol JACR 8(1):53–57, 2011CrossRefPubMedGoogle Scholar
  6. 6.
    DeStigter KK, Mainiero MB, Janower ML, Resnik CS: Resident clinical duties while preparing for the ABR core examination: position statement of the Association of Program Directors in Radiology. J Am Coll Radiol JACR 9(11):832–834, 2012CrossRefPubMedGoogle Scholar
  7. 7.
    Bhargava P, Lackey AE, Dhand S, Moshiri M, Jambhekar K, Pandey T: Radiology education 2.0--on the cusp of change: part 1. Tablet computers, online curriculums, remote meeting tools and audience response systems. Acad Radiol 20(3):364–372, 2013CrossRefPubMedGoogle Scholar
  8. 8.
    R Development Core Team. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical ComputingGoogle Scholar
  9. 9.
    Baro E, Degoul S, Beuscart R, Chazard E: Toward a Literature-Driven Definition of Big Data in Healthcare. BioMed Res Int 2015:639021, 2015CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Sukumar SR, Natarajan R, Ferrell RK: Quality of Big Data in health care. Int J Health Care Qual Assur 28(6):621–634, 2015CrossRefPubMedGoogle Scholar
  11. 11.
    Vaitsis C, Nilsson G, Zary N: Big data in medical informatics: improving education through visual analytics. Stud Health Technol Inform 205:1163–1167, 2014PubMedGoogle Scholar
  12. 12.
    Caban JJ, Gotz D: Visual analytics in healthcare—opportunities and research challenges. J Am Med Inform Assoc JAMIA 22(2):260–262, 2015CrossRefPubMedGoogle Scholar
  13. 13.
    Alyass A, Turcotte M, Meyre D. From big data analysis to personalized medicine for all: challenges and opportunities. BMC Med Genomics [Internet]. 2015 Dec [cited 2015 Jul 26];8(1). Available from: http://www.biomedcentral.com/1755-8794/8/33
  14. 14.
    Fernández-Luque L, Bau T: Health and social media: perfect storm of information. Healthc Inform Res 21(2):67–73, 2015CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    Gow KW, Drake FT, Aarabi S, Waldhausen JH: The ACGME case log: general surgery resident experience in pediatric surgery. J Pediatr Surg 48(8):1643–1649, 2013CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    Salazar D, Schiff A, Mitchell E, Hopkinson W: Variability in Accreditation Council for Graduate Medical Education Resident Case Log System practices among orthopaedic surgery residents. J Bone Joint Surg Am 96(3), e22, 2014CrossRefPubMedGoogle Scholar
  17. 17.
    Chen PH, Chen YJ, Cook TS: Capricorn—A Web-Based Automatic Case Log and Volume Analytics for Diagnostic Radiology Residents. Acad Radiol 22(10):1242–1251, 2015CrossRefPubMedGoogle Scholar
  18. 18.
    Hendler J: Data Integration for Heterogenous Datasets. Big Data 2(4):205–215, 2014CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Society for Imaging Informatics in Medicine 2016

Authors and Affiliations

  • Po-Hao Chen
    • 1
  • Thomas W. Loehfelm
    • 2
    • 3
  • Aaron P. Kamer
    • 4
  • Andrew B. Lemmon
    • 2
  • Tessa S. Cook
    • 1
  • Marc D. Kohli
    • 3
  1. 1.Department of RadiologyHospital of the University of PennsylvaniaPhiladelphiaUSA
  2. 2.Department of RadiologyEmory University HospitalAtlantaUSA
  3. 3.Department of RadiologyStanford University School of MedicineStanfordUSA
  4. 4.Department of Radiology and Imaging SciencesIndiana University School of MedicineIndianapolisUSA

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