Variation in Physicians’ Electronic Health Record Documentation and Potential Patient Harm from That Variation

  • Genna R. CohenEmail author
  • Charles P. Friedman
  • Andrew M. Ryan
  • Caroline R. Richardson
  • Julia Adler-Milstein
Original Research



Physician-to-physician variation in electronic health record (EHR) documentation not driven by patients’ clinical status could be harmful.


Measure variation in completion of common clinical documentation domains. Identify perceived causes and effects of variation and strategies to mitigate negative effects.


Sequential, explanatory, mixed methods using log data from a commercial EHR vendor and semi-structured interviews with outpatient primary care practices.


Quantitative: 170,332 encounters led by 809 physicians in 237 practices. Qualitative: 40 interviewees in 10 practices.

Main Measures

Interquartile range (IQR) of the proportion of encounters in which a physician completed documentation, for each documentation category. Multilevel linear regression measured the proportion of variation at the physician level.

Key Results

Five clinical documentation categories had substantial and statistically significant (p < 0.001) variation at the physician level after accounting for state, organization, and practice levels: (1) discussing results (IQR = 50.8%, proportion of variation explained by physician level = 78.1%); (2) assessment and diagnosis (IQR = 60.4%, physician-level variation = 76.0%); (3) problem list (IQR = 73.1%, physician-level variation = 70.1%); (4) review of systems (IQR = 62.3%, physician-level variation = 67.7%); and (5) social history (IQR = 53.3%, physician-level variation = 62.2%). Drivers of variation from interviews included user preferences and EHR designs with multiple places to record similar information. Variation was perceived to create documentation inefficiencies and risk patient harm due to missed or misinterpreted information. Mitigation strategies included targeted user training during EHR implementation and practice meetings focused on documentation standardization.


Physician-to-physician variation in EHR documentation impedes effective and safe use of EHRs, but there are potential strategies to mitigate negative consequences.


EHR documentation mixed methods primary care 


Funding Source

Funded by the Agency for Healthcare Research and Quality (1R36HS023719-01A1), the University of Michigan Rackham Predoctoral Fellowship, and the University of Michigan McNerney Award.

Compliance with Ethical Standards

Conflict of Interest

The authors declare that they do not have a conflict of interest.


  1. 1.
    Blumenthal D, Tavenner M. The meaningful use regulation for electronic health records. N Engl J Med 2010;363(6):501–504.CrossRefPubMedGoogle Scholar
  2. 2.
    Schoen C, Osborn R, Squires D, Doty MM. Access, affordability, and insurance complexity are often worse in the United States compared to ten other countries. Health Aff (Millwood). 2013;32(12):2205–2215.CrossRefPubMedGoogle Scholar
  3. 3.
    Hartman M, Martin AB, Benson J, Catlin A, National Health Expenditure Accounts Team. National health spending in 2011: overall growth remains low, but some payers and services show signs of acceleration. Health Aff (Millwood) 2013;32(1):87–99.CrossRefGoogle Scholar
  4. 4.
    Yadav S, Kazanji N, K C N, et al. Comparison of accuracy of physical examination findings in initial progress notes between paper charts and a newly implemented electronic health record. J Am Med Inform Assoc 2017;24(1):140–144.CrossRefPubMedGoogle Scholar
  5. 5.
    McDonald CJ, Callaghan FM, Weissman A, Goodwin RM, Mundkur M, Kuhn T. Use of internist’s free time by ambulatory care electronic medical record systems. JAMA Intern Med 2014;174(11):1860–1863.CrossRefPubMedGoogle Scholar
  6. 6.
    Meeks DW, Smith MW, Taylor L, Sittig DF, Scott JM, Singh H. An analysis of electronic health record–related patient safety concerns. J Am Med Inform Assoc 2014;21(6):1053–1059.CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Logan JR, Gorman PN, Middleton B. Measuring the quality of medical records: a method for comparing completeness and correctness of clinical encounter data. Proc AMIA Symp 2001:408–412.Google Scholar
  8. 8.
    Linder JA, Schnipper JL, Middleton B. Method of electronic health record documentation and quality of primary care. J Am Med Inform Assoc 2012;19(6):1019–1024.CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Edwards ST, Neri PM, Volk LA, Schiff GD, Bates DW. Association of note quality and quality of care: a cross-sectional study. BMJ Qual Saf 2014;23(5):406–413.CrossRefPubMedGoogle Scholar
  10. 10.
    Valikodath NG, Newman-Casey PA, Lee PP, Musch DC, Niziol LM, Woodward MA. Agreement of ocular symptom reporting between patient-reported outcomes and medical records. JAMA Opthalmol 2017;135(3):225–231.CrossRefGoogle Scholar
  11. 11.
    Wilcox A, Bowes WA, Thornton SN, Narus SP. Physician use of outpatient electronic health records to improve care. AMIA Ann Symp Proc 2008:809–813.Google Scholar
  12. 12.
    Ancker JS, Kern LM, Edwards A, et al. How is the electronic health record being used? Use of EHR data to assess physician-level variability in technology use. J Am Med Inform Assoc 2014;21(6):1001–1008.CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Bates DW, Bitton A. The future of health information technology in the patient-centered medical home. Health Aff (Millwood). 2010;29(4):614–621.CrossRefPubMedGoogle Scholar
  14. 14.
    Farmer MM, Rose DE, Rubenstein LV, et al. Challenges facing primary care practices aiming to implement patient-centered medical homes. J Gen Intern Med 2014;29(Suppl 2):555–562.CrossRefPubMedCentralGoogle Scholar
  15. 15.
    Fernandopulle R, Patel N. How the electronic health record did not measure up to the demands of our medical home practice. Health Aff (Millwood). 2010;29(4):622–628.CrossRefPubMedGoogle Scholar
  16. 16.
    Glickman ME, Rao SR, Schultz MR. False discovery rate control is a recommended alternative to Bonferroni-type adjustments in health studies. J Clin Epidemiol 2014;67(8):850–857.CrossRefGoogle Scholar
  17. 17.
    Hong CS, Atlas SJ, Chang Y, et al. Relationship between patient panel characteristics and primary care physician clinical performance rankings. JAMA. 2010;304(10):1107–1113.CrossRefPubMedGoogle Scholar
  18. 18.
    Stata Statistical Software: Release 13. College Station: StataCorp; 2013.Google Scholar
  19. 19.
    Richardson JE, Ash JS. A clinical decision support needs assessment of community-based physicians. J Am Med Inform Assoc 2011;18(Suppl 1):i28-i35.CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    Feldman MS. Strategies for Interpreting Qualitative Data. Thousand Oaks: SAGE Publications; 1995.CrossRefGoogle Scholar
  21. 21.
    Miles M, Huberman AM. Qualitative Data Analysis: an Expanded Sourcebook. Thousand Oaks: SAGE Publications; 1994:211–249.Google Scholar
  22. 22.
    Rubin HJ, Rubin IS. Qualitative Interviewing: the Art of Hearing Data. Thousand Oaks: SAGE Publications; 1995.Google Scholar
  23. 23.
    ATLAS.ti. Berlin, Germany: ATLAS.ti Scientific Software Development.Google Scholar
  24. 24.
    Marsh GW. Refining an emergent life-style-change theory through matrix analysis. ANS Adv Nurs Sci 1990;12(3):41–52.CrossRefPubMedGoogle Scholar
  25. 25.
    Tuite PJ, Krawczewski K. Parkinsonism: a review-of-systems approach to diagnosis. Semin Neurol 2007;27(02):113–122.CrossRefPubMedGoogle Scholar
  26. 26.
    Pollard SE, Neri PM, Wilcox AR, et al. How physicians document outpatient visit notes in an electronic health record. Int J Med Inform 2013;82(1):39–46.CrossRefPubMedGoogle Scholar
  27. 27.
    Rosenbloom ST, Denny JC, Xu H, Lorenzi N, Stead WW, Johnson KB. Data from clinical notes: a perspective on the tension between structure and flexible documentation. J Am Med Inform Assoc 2011;18(2):181–186.CrossRefPubMedPubMedCentralGoogle Scholar
  28. 28.
    Bossen C. Representations at work: a national standard for electronic health records. Proceedings of the 2006 20th Anniversary Conference on Computer Supported Cooperative Work. 2006: ACM.Google Scholar
  29. 29.
    Baron RJ, Fabens EL, Schiffman M, Wolf E. Electronic health records: just around the corner? Or over the cliff? Ann Intern Med 2005;143(3):222–226.CrossRefPubMedGoogle Scholar
  30. 30.
    Howard J, Clark EC, Friedman A, et al. Electronic health record impact on work burden in small, unaffiliated, community-based primary care practices. J Gen Intern Med 2013;28(1):107–113.CrossRefPubMedGoogle Scholar
  31. 31.
    Collins FS, Varmus H. A new initiative on precision medicine. N Engl J Med 2015;372(9):793–795.CrossRefPubMedPubMedCentralGoogle Scholar
  32. 32.
    Friedman CP, Wong AK, Blumenthal D. Achieving a nationwide learning health system. Sci Transl Med 2010;2(57):57cm29.CrossRefPubMedGoogle Scholar

Copyright information

© Society of General Internal Medicine 2019

Authors and Affiliations

  • Genna R. Cohen
    • 1
    Email author
  • Charles P. Friedman
    • 2
  • Andrew M. Ryan
    • 3
  • Caroline R. Richardson
    • 4
  • Julia Adler-Milstein
    • 5
  1. 1.MathematicaWashingtonUSA
  2. 2.Department of Learning Health SciencesUniversity of Michigan Medical SchoolAnn ArborUSA
  3. 3.Department of Health Management and PolicyUniversity of Michigan School of Public HealthAnn ArborUSA
  4. 4.Department of Family MedicineUniversity of Michigan Health SystemAnn ArborUSA
  5. 5.Center for Clinical Informatics and Improvement ResearchUniversity of California San Francisco Department of MedicineSan FranciscoUSA

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