Automating Documentation: A Critical Perspective into the Role of Artificial Intelligence in Clinical Documentation

  • Matt WillisEmail author
  • Mohammad Hossein Jarrahi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11420)


The current conversation around automation and artificial intelligence technologies creates a future vision where humans may not possibly compete against intelligent machines, and that everything that can be automated through deep learning, machine learning, and other AI technologies will be automated. In this article, we focus on general practitioner documentation of the patients’ clinical encounters, and explore how these work practices lend themselves to automation by AI. While these work practices may appear perfect to automate, we reveal potential negative consequences to automating these tasks, and illustrate how AI may render important aspect of this work invisible and remove critical thinking. We conclude by highlighting the specific features of clinical documentation work that could leverage the benefits of human-AI symbiosis.


Automation Artificial Intelligence Healthcare Clinical documentation 


  1. 1.
    Sola, D., Borioli, G.S., Quaglia, R.: Predicting GPs’ engagement with artificial intelligence. Br. J. Healthc. Manag. 24, 134–140 (2018)CrossRefGoogle Scholar
  2. 2.
    Bansler, J.P., Havn, E.C., Schmidt, K., Mønsted, T., Petersen, H.H., Svendsen, J.H.: Cooperative epistemic work in medical practice: an analysis of physicians’ clinical notes. Comput. Support. Coop. Work 25, 503–546 (2016)CrossRefGoogle Scholar
  3. 3.
    Lin, S.Y., Shanafelt, T.D., Asch, S.M.: Reimagining clinical documentation with artificial intelligence. Mayo Clin. Proc. 93, 563–565 (2018)CrossRefGoogle Scholar
  4. 4.
    Verghese, A., Shah, N.H., Harrington, R.A.: What this computer needs is a physician: humanism and artificial intelligence. JAMA 319, 19 (2018)CrossRefGoogle Scholar
  5. 5.
    Baird, B., Charles, A., Honeyman, M., Maguire, D., Das, P.: Understanding Pressures in General Practice, London, UK (2016)Google Scholar
  6. 6.
    Hopson, C.: The sate of the NHS provider sector (2016)Google Scholar
  7. 7.
    Byrne, L., Bottomley, J., Turk, A.: British Medical Association Survey of GPs in England, London (2016)Google Scholar
  8. 8.
    Gibson, J., et al.: Eighth National GP Worklife Survey. Manchester (2016)Google Scholar
  9. 9.
    Zuboff, S.: In the Age of the Smart Machine: The Future of Work and Power. Basic Books, New York (1988)Google Scholar
  10. 10.
    Smith, K., Smith, V., Krugman, M., Oman, K.: Evaluating the impact of computerized clinical documentation. Comput. Inform. Nurs. 23, 132–138 (2005)CrossRefGoogle Scholar
  11. 11.
    Ammenwerth, E., Spötl, H.-P.: The time needed for clinical documentation versus direct patient care. A work-sampling analysis of physicians’ activities. Methods Inf. Med. 48, 84–91 (2009)CrossRefGoogle Scholar
  12. 12.
    Erickson, S.M., Rockwern, B., Koltov, M., McLean, R.M.: Medical practice and quality committee of the American College of Physicians: putting patients first by reducing administrative tasks in health care: a position paper of the American College of Physicians. Ann. Intern. Med. 166, 659 (2017)CrossRefGoogle Scholar
  13. 13.
    Rule, A., et al.: Validating free-text order entry for a note-centric EHR. In: AMIA Annual Symposium Proceedings, vol. 2015, pp. 1103–1110 (2015)Google Scholar
  14. 14.
    Klann, J.G., Szolovits, P.: An intelligent listening framework for capturing encounter notes from a doctor-patient dialog. BMC Med. Inform. Decis. Mak. 9(Suppl 1), S3 (2009)CrossRefGoogle Scholar
  15. 15.
    Wald, H.S., Borkan, J.M., Taylor, J.S., Anthony, D., Reis, S.P.: Fostering and evaluating reflective capacity in medical education: developing the REFLECT rubric for assessing reflective writing. Acad. Med. 87, 41–50 (2012)CrossRefGoogle Scholar
  16. 16.
    Savolainen, R.: Everyday Information Practices: A Social Phenomenological Perspective. Scarecrow Press (2008)Google Scholar
  17. 17.
    Jarrahi, M.H., Thomson, L.: The interplay between information practices and information context: the case of mobile knowledge workers. J. Assoc. Inf. Sci. Technol. 68, 1073–1089 (2017)CrossRefGoogle Scholar
  18. 18.
    Jarrahi, M.: Artificial intelligence and the future of work: human-AI symbiosis in organizational decision making. Bus. Horiz. 61, 577–586 (2018)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Oxford Internet InstituteUniversity of OxfordOxfordUK
  2. 2.University of North CarolinaChapel HillUSA

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