PDF herunterladen
CC BY-NC-ND 4.0 · Appl Clin Inform 2023; 14(05): 944-950
DOI: 10.1055/a-2187-3243
State of the Art/Best Practice Paper

Identifying and Addressing Barriers to Implementing Core Electronic Health Record Use Metrics for Ambulatory Care: Virtual Consensus Conference Proceedings

Deborah R. Levy
1   Department of Veterans Affairs, VA Connecticut Healthcare System, West Haven, Connecticut, United States
2   Section of Biomedical Informatics and Data Sciences, Yale University School of Medicine, New Haven, Connecticut, United States
,
Amanda J. Moy
3   Department of Biomedical Informatics, Columbia University, New York, New York, United States
,
Nate Apathy
4   National Center for Human Factors in Healthcare, MedStar Health Research Institute, Washington, District of Columbia, United States
5   Center for Biomedical Informatics, Regenstrief Institute, Indianapolis, Iowa, United States
,
Julia Adler-Milstein
6   Department of Medicine, Center for Clinical Informatics and Improvement Research, University of California, San Francisco, California, United States
,
Lisa Rotenstein
7   Division of General Internal Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts, United States
8   Harvard Medical School, Boston, Massachusetts, United States
,
Bidisha Nath
9   Department of Emergency Medicine, Yale University School of Medicine, New Haven, Connecticut, United States
,
S. Trent Rosenbloom
10   Department of Biomedical Informatics, Vanderbilt University, Nashville, Tennessee, United States
,
Thomas Kannampallil
11   Department of Anesthesiology, Washington University School of Medicine, St. Louis, Missouri, United States
12   Institute for Informatics, Data Science, and Biostatistics (I2DB), Washington University School of Medicine, St. Louis, Missouri, United States
,
Rebecca G. Mishuris
7   Division of General Internal Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts, United States
8   Harvard Medical School, Boston, Massachusetts, United States
13   Digital, Mass General Brigham, Boston, Massachusetts, United States
,
Aram Alexanian
14   Novant Health, Charlotte, North Carolina, United States
,
Amber Sieja
15   Department of General Internal Medicine, University of Colorado School of Medicine, Aurora, Colorado, United States
,
Michelle R. Hribar
16   Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon, United States
,
Jigar S. Patel
17   Oracle Corporation, Kansas City, Missouri, United States
,
Christine A. Sinsky
18   American Medical Association, Chicago, Illinois, United States
,
Edward R. Melnick
2   Section of Biomedical Informatics and Data Sciences, Yale University School of Medicine, New Haven, Connecticut, United States
9   Department of Emergency Medicine, Yale University School of Medicine, New Haven, Connecticut, United States
› Institutsangaben Funding This work was supported by the American Medical Association Practice Transformation Initiatives (contract number 19449). D.R.L. is supported by the Department of Veterans Affairs, Veterans Health Administration, Office of Academic Affiliations, Office of Research and Development, with resources and the use of facilities at the VA Connecticut Healthcare System, West Haven, Connecticut (CIN-13-407). E.R.M. reports receiving grants from the National Institute on Drug Abuse and the Agency for Healthcare Research and Quality unrelated to this work.
› Weitere Informationen
Auch verfügbar auf
   Lizenzen und Reprints

Abstract

Precise, reliable, valid metrics that are cost-effective and require reasonable implementation time and effort are needed to drive electronic health record (EHR) improvements and decrease EHR burden. Differences exist between research and vendor definitions of metrics.

Process We convened three stakeholder groups (health system informatics leaders, EHR vendor representatives, and researchers) in a virtual workshop series to achieve consensus on barriers, solutions, and next steps to implementing the core EHR use metrics in ambulatory care.

Conclusion Actionable solutions identified to address core categories of EHR metric implementation challenges include: (1) maintaining broad stakeholder engagement, (2) reaching agreement on standardized measure definitions across vendors, (3) integrating clinician perspectives, and (4) addressing cognitive and EHR burden. Building upon the momentum of this workshop's outputs offers promise for overcoming barriers to implementing EHR use metrics.

Keywords

audit logs - metrics - electronic health records and systems - data validation and verification - ambulatory care/primary care - facilitators and barriers - use, administration, and maintenance

Note

The contents of this manuscript represent the view of the authors and do not necessarily reflect the position or policy of the U.S. Department of Veterans Affairs or the United States Government.


Protection of Human and Animal Subjects

No human subjects were involved in this work.




Publikationsverlauf

Eingereicht: 19. Juli 2023

Angenommen: 30. September 2023

Accepted Manuscript online:
06. Oktober 2023

Artikel online veröffentlicht:
29. November 2023

© 2023. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/)

Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany

 

  • References

  • 1 Sinsky CA, Rule A, Cohen G. et al. Metrics for assessing physician activity using electronic health record log data. J Am Med Inform Assoc 2020; 27 (04) 639-643
    CrossrefPubMedSuche in Google Scholar
  • 2 Rule A, Melnick ER, Apathy NC. Using event logs to observe interactions with electronic health records: an updated scoping review shows increasing use of vendor-derived measures. J Am Med Inform Assoc 2022; 30 (01) 144-154
    CrossrefPubMedSuche in Google Scholar
  • 3 Levy DR, Sloss EASA, Chartash D. et al. Reflections on the Documentation Burden Reduction AMIA Plenary Session through the Lens of 25 × 5. Appl Clin Inform 2023; 14 (01) 11-15
    Thieme ConnectPubMedSuche in Google Scholar
  • 4 Melnick ER, Ong SY, Fong A. et al. Characterizing physician EHR use with vendor derived data: a feasibility study and cross-sectional analysis. J Am Med Inform Assoc 2021; 28 (07) 1383-1392
    CrossrefPubMedSuche in Google Scholar
  • 5 Melnick ER, Sinsky CA, Krumholz HM. Implementing measurement science for electronic health record use. JAMA 2021; 325 (21) 2149-2150
    CrossrefPubMedSuche in Google Scholar
  • 6 Kannampallil T, Adler-Milstein J. Using electronic health record audit log data for research: insights from early efforts. J Am Med Inform Assoc 2022; 30 (01) 167-171
    CrossrefPubMedSuche in Google Scholar
  • 7 Tran B, Lenhart A, Ross R, Dorr DA. Burnout and EHR use among academic primary care physicians with varied clinical workloads. AMIA Jt Summits Transl Sci Proc 2019; 2019: 136-144
    PubMedSuche in Google Scholar
  • 8 Arndt BG, Micek MA, Rule A, Shafer CM, Baltus JJ, Sinsky CA. Refining vendor-defined measures to accurately quantify EHR workload outside time scheduled with patients. Ann Fam Med 2023; 21 (03) 264-268
    CrossrefPubMedSuche in Google Scholar
  • 9 Melnick ER, Nielson JA, Finnell JT. et al. Delphi consensus on the feasibility of translating the ACEP clinical policies into computerized clinical decision support. Ann Emerg Med 2010; 56 (04) 317-320
    CrossrefPubMedSuche in Google Scholar
  • 10 Nasa P, Jain R, Juneja D. Delphi methodology in healthcare research: how to decide its appropriateness. World J Methodol 2021; 11 (04) 116-129
    CrossrefPubMedSuche in Google Scholar
  • 11 Rossetti SC, Rosenbloom S, Levy DR. et al. 25 × 5 symposium drives ongoing efforts to reduce documentation burden on U.S. clinicians: final summary report 2021 . Accessed December 5, 2021 at: https://www.dbmi.columbia.edu/wp-content/uploads/2021/12/25×5-Summary-Report.pdf or https://brand.amia.org/m/dbde97860f393e1/original/25×5-Summary-Report.pdf
    PubMed
  • 12 Hobensack M, Levy DR, Cato K. et al. 25 × 5 Symposium to reduce documentation burden: report-out and call for action. Appl Clin Inform 2022; 13 (02) 439-446
    Thieme ConnectPubMedSuche in Google Scholar
  • 13 Baxter SL, Apathy NC, Cross DA, Sinsky C, Hribar MR. Measures of electronic health record use in outpatient settings across vendors. J Am Med Inform Assoc 2021; 28 (05) 955-959
    CrossrefPubMedSuche in Google Scholar
  • 14 Overhage JM, McCallie Jr D. Physician time spent using the electronic health record during outpatient encounters: a descriptive study. Ann Intern Med 2020; 172 (03) 169-174
    CrossrefPubMedSuche in Google Scholar
  • 15 Holmgren AJ, Downing NL, Bates DW. et al. Assessment of electronic health record use between US and non-US health systems. JAMA Intern Med 2021; 181 (02) 251-259
    CrossrefPubMedSuche in Google Scholar
  • 16 Moy AJ, Aaron L, Cato KD. et al. Characterizing multitasking and workflow fragmentation in electronic health records among emergency department clinicians: using time-motion data to understand documentation burden. Appl Clin Inform 2021; 12 (05) 1002-1013
    Thieme ConnectPubMedSuche in Google Scholar
  • 17 Chen Y, Adler-Milstein J, Sinsky CA. Measuring and maximizing undivided attention in the context of electronic health records. Appl Clin Inform 2022; 13 (04) 774-777
    Thieme ConnectPubMedSuche in Google Scholar
  • 18 Senathirajah Y, Kaufman DR, Cato KD, Borycki EM, Fawcett JA, Kushniruk AW. Characterizing and visualizing display and task fragmentation in the electronic health record: mixed methods design. JMIR Human Factors 2020; 7 (04) e18484
    CrossrefPubMedSuche in Google Scholar