Skip to main content

Advertisement

SpringerLink
Log in

Rebooting the Electronic Health Record

  • Implementation Science & Operations Management
  • Published:
Journal of Medical Systems Aims and scope Submit manuscript

Abstract

Justifications for the widespread adoption and integration of an electronic health record (EHR) have long leaned on the purported benefits of the technology. However, the performance of the EHR has been underwhelming relative to the promises of immediate access to relevant patient information, clinical decision supports, computerized ordering, and transferable patient data. In this narrative review, we provide an overview of the historical problems and limitations of the EHR, detail the core principles that define agile processes that may overcome the barriers faced by the current EHR, and re-imagine what an integrated, seamless EHR that serves its users and patients might look like. Moving forward, the EHR should be redesigned using a middle-out framework and empowering dual-type champions to maintain the sustainable diffusion of future innovations.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Abbreviations

EHR:

electronic health record.

HITECH:

Health Information Technology for Economic and Clinical Health.

FHIR:

Fast Healthcare Interoperability Resources.

HL-7:

Health Level Seven International.

ML:

machine learning.

References

  1. Berner ES, Detmer DE, Simborg D. Will the wave finally break? A brief view of the adoption of electronic medical records in the United States. J Am Med Inform Assoc. 2005;12(1):3–7.

    Article  Google Scholar 

  2. Hersh WR. The Electronic Medical Record: Promises and Problems. J. Assoc. Inf. Sci. Technol. 1995;46(10):772–776.

    Google Scholar 

  3. Evans RS. Electronic Health Records: Then, Now, and in the Future. Yearb Med Inform. 2016;Suppl 1(Suppl 1):S48-S61.

    Google Scholar 

  4. Palabindala V, Pamarthy A, Jonnalagadda NR. Adoption of electronic health records and barriers. J Community Hosp Intern Med Perspect. 2016;6(5):32643.

    Article  Google Scholar 

  5. Mazzucato M. The Entrepreneurial State: Debunking Public vs. Private Sector Myths. London, UK: Anthem Press; 2013.

    Google Scholar 

  6. Barnett GO. The computer-based clinical record: where do we stand? Ann Intern Med. 1993;119(10):1046.

    Article  CAS  Google Scholar 

  7. McDonald CJ, Tierney WM. Computer-stored medical records. Their future role in medical practice. JAMA. 1988;259(23):3433–3440.

    Article  CAS  Google Scholar 

  8. Teufel RJ 2nd, Kazley AS, Ebeling MD, Basco WT Jr. Hospital electronic medical record use and cost of inpatient pediatric care. Acad Pediatr. 2012;12(5):429–435.

  9. Wright, R. NonZero: the logic of human destiny. New York City, NY: Pantheon Books; 2000.

    Google Scholar 

  10. Manchikanti L, Benyamin RM, Falco FJ, Hirsch JA. Metamorphosis of medicine in the United States: a carrot and stick policy of electronic medical records. Pain Physician. 2014;17(6):E671-E680.

    Google Scholar 

  11. McGrath RG, Venkataraman S, MacMillan IC. Measuring outcomes of corporate venturing: An alternative perspective. Academy of Management Proceedings. 1992;1992(1):85–89.

  12. Edmondson AC. Teamwork on the fly. Harvard Business Review. https://hbr.org/2012/04/teamwork-on-the-fly-2. Published February 7, 2020. Accessed December 27, 2021.

  13. Rigby DK, Sutherland J, Takeuchi H. Embracing Agile. Harvard Business Review. https://hbr.org/2016/05/embracing-agile. Published August 27, 2021. Accessed December 27, 2021.

  14. Mintzberg H. The Fall and Rise of Strategic Planning. Harvard Business Review. https://hbr.org/1994/01/the-fall-and-rise-of-strategic-planning. Published August 1, 2014. Accessed December 27, 2021.

  15. Noble D. Evolution beyond neo-Darwinism: a new conceptual framework [published correction appears in J Exp Biol. 2015 Apr 15;218(Pt 8):1273]. J Exp Biol. 2015;218(Pt 1):7–13.

  16. Ferver K, Burton B, Jesilow P. The use of claims data in healthcare research. The Open Public Health Journal. 2009;2(1):11–24.

    Article  Google Scholar 

  17. Tyree PT, Lind BK, Lafferty WE. Challenges of using medical insurance claims data for utilization analysis. Am J Med Qual. 2006;21(4):269–275.

    Article  Google Scholar 

  18. Abdel-Kader AK, Eisenkraft JB, Katz DJ. Overview and limitations of database research in anesthesiology: a narrative review. Anes Analg. April 2021; 132(4): 1012–1022.

    Article  Google Scholar 

  19. Saripalle R, Runyan C, Russell M. Using HL7 FHIR to achieve interoperability in patient health record. J Biomed Inform. 2019;94:103188.

    Article  Google Scholar 

  20. NCVHS, Report to the Secretary on Uniform Standards for Patient Medical Record Information, July 6, 2000, pp. 21–22. https://ncvhs.hhs.gov/wp-content/uploads/2014/05/hipaa000706.pdf. Accessed April 22, 2021.

  21. Centers for Medicare & Medicaid Services, Medicare and Medicaid Programs; Patient Protection and Affordable Care Act; Interoperability and Patient Access for Medicare Advantage Organization and Medicaid Managed Care Plans, State Medicaid Agencies, CHIP Agencies and CHIP Managed Care Entities, Issuers of Qualified Health Plans on the Federally-Facilitated Exchanges, and Health Care Providers, https://www.federalregister.gov/documents/2020/05/01/2020-05050/medicare-and-medicaid-programs-patient-protection-and-affordable-care-act-interoperability-and. Accessed April 22, 2021.

  22. Tajirian T, Stergiopoulos V, Strudwick G, Sequeira L, Sanches M, Kemp J, Ramamoorthi K, Zhang T, Jankowicz D, The Influence of Electronic Health Record Use on Physician Burnout: Cross-Sectional Survey. J Med Internet Res. 2020;22(7):e19274.

    Article  Google Scholar 

  23. Robertson SL, Robinson MD, Reid A. Electronic Health Record Effects on Work-Life Balance and Burnout Within the I3 Population Collaborative. J Grad Med Educ. 2017;9(4):479–484.

    Article  Google Scholar 

  24. Domingos P. The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World. NY, USA: Basic Books; 2015.

    Google Scholar 

  25. Catmull E, Wallace E. Creativity, Inc: Overcoming the Unseen Forces That Stand in the Way of True Inspiration. London, UK: Transworld Publishers Limited; 2014.

    Google Scholar 

  26. Schrittwieser J, Antonoglou I, Hubert T, et al. Mastering Atari, Go, chess and shogi by planning with a learned model. Nature. 2020; 588(7839):604–609.

    Article  CAS  Google Scholar 

  27. Silver D, Hassabis D. AlphaGo Zero: Starting from scratch. Deepmind. https://deepmind.com/blog/article/alphago-zero-starting-scratch. Published October 18, 2017. Accessed January 27, 2022.

  28. Using Care Select Imaging Integrated Clinical Decision Support Tool. ACR Support. https://acrsupport.acr.org/support/solutions/articles/11000072065-using-care-select-imaging-integrated-clinical-decision-support-tool. Accessed April 25, 2022.

  29. Nemati S, Holder A, Razmi F, Stanley MD, Clifford GD, Buchman TG. An interpretable machine learning model for accurate prediction of sepsis in the ICU. Critical Care Medicine. 2018;46(4):547–553.

    Article  Google Scholar 

  30. Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. arXiv; 2014. arXiv.1409.1556.

  31. Szegedy C, Wei Liu, Yangqing Jia, et al. Going deeper with convolutions. 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2015.

  32. van de Sande D, Van Genderen ME, Smit JM, et al. Developing, implementing and governing Artificial Intelligence in medicine: A step-by-step approach to prevent an artificial intelligence winter. BMJ Health & Care Informatics. 2022;29(1).

  33. Wang X, et al. ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases. arXiv; 2017. arXiv.1705.02315.

  34. Crawford K, Calo R. There is a blind spot in AI Research. Nature. 2016;538(7625):311–313.

    Article  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. University of Vermont Larner College of Medicine, 89 Beaumont Avenue, 05405-0068, Burlington, VT, U.S.

    Erik J. Zhang BA

  2. Department of Medicine, Division of Internal Medicine, University of Miami, Miami, FL, U.S.

    Heng Tan MD, PhD

  3. University of Arkansas for Medical Sciences, Fayetteville, AR, U.S.

    Joseph A. Sanford MD

  4. Department of Orthopaedics and Rehabilitation, University of Vermont Larner College of Medicine, Burlington, VT, U.S.

    James D. Michelson MD

  5. Department of Anesthesiology, University of Vermont Larner College of Medicine, Burlington, VT, U.S.

    Brian M. Waldschmidt MD

  6. Department of Anesthesiology, Department of Orthopaedics and Rehabilitation (by courtesy), Department of Surgery (by courtesy), University of Vermont Larner College of Medicine, Burlington, VT, U.S.

    Mitchell H. Tsai MD, MMM, FASA, FAACD

Authors
  1. Erik J. Zhang BA
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Heng Tan MD, PhD
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Joseph A. Sanford MD
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. James D. Michelson MD
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Brian M. Waldschmidt MD
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Mitchell H. Tsai MD, MMM, FASA, FAACD
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Erik J. Zhang, Heng Tan, Mitchell H. Tsai and Brian M. Waldschmidt have helped prepare the manuscript. Joseph A. Sanford has provided edits to the manuscript. James D. Michelson has contributed to the formulation of the manuscript and its editing.Attestation: Erik J. Zhang, Heng Tan, Joseph A. Sanford, James D. Michelson, Brian M. Waldschmidt and Mitchell H. Tsai approve the manuscript as submitted.

Corresponding author

Correspondence to Erik J. Zhang BA.

Ethics declarations

Conflict of interest

The authors declare no personal conflicts of interest and have received no outside support for the creation of this document.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, E.J., Tan, H., Sanford, J.A. et al. Rebooting the Electronic Health Record. J Med Syst 46, 48 (2022). https://doi.org/10.1007/s10916-022-01834-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10916-022-01834-y

Keywords

Search

Navigation