Abstract

Background

As patient-initiated messaging rises, identifying variation in message volume and its relationship to clinician workload is essential.

Objective

To describe the association between variation in message volume over time and time spent on the electronic health record (EHR) outside of scheduled hours.

Design

Retrospective cohort study.

Participants

Primary care clinicians at Cleveland Clinic Health System.

Main Measures

We categorized clinicians according to their number of quarterly incoming medical advice messages (i.e., message volume) between January 2019 and December 2021 using group-based trajectory modeling. We assessed change in quarterly messages and outpatient visits between October–December 2019 (Q4) and October–December 2021 (Q12). The primary outcome was time outside of scheduled hours spent on the EHR. We used mixed effects logistic regression to describe the association between incoming portal messages and time spent on the EHR by clinician messaging group and at the clinician level.

Key Results

Among the 150 clinicians, 31% were in the low-volume group (206 messages per quarter per clinician), 47% were in the moderate-volume group (505 messages), and 22% were in the high-volume group (840 messages). Mean quarterly messages increased from 340 to 695 (p < 0.001) between Q4 and Q12; mean quarterly outpatient visits fell from 711 to 575 (p = 0.005). While time spent on the EHR outside of scheduled hours increased modestly for all clinicians, this did not significantly differ by message group. Across all clinicians, each additional 10 messages was associated with an average of 12 min per quarter of additional time spent on the EHR (p < 0.001).

Conclusions

Message volume increased substantially over the study period and varied by group. While messages were associated with additional time spent on the EHR outside of scheduled hours, there was no significant difference in time spent on the EHR between the high and low message volume groups.

References

  1. Individuals’ Access and Use of Patient Portals and Smartphone Health Apps, 2020 | HealthIT.gov. https://www.healthit.gov/data/data-briefs/individuals-access-and-use-patient-portals-and-smartphone-health-apps-2020. Accessed 22 February 2023.

  2. Kruse CS, Bolton K, Freriks G. The Effect of Patient Portals on Quality Outcomes and Its Implications to Meaningful Use: A Systematic Review. J Med Internet Res. 2015;17(2):e44. https://doi.org/10.2196/jmir.3171.

  3. The Complexities of Physician Supply and Demand: Projections from 2016 to 2030. Association of American Medical Colleges.

  4. North F, Luhman KE, Mallmann EA, et al. A Retrospective Analysis of Provider-to-Patient Secure Messages: How Much Are They Increasing, Who Is Doing the Work, and Is the Work Happening After Hours? JMIR Med Inform. 2020;8(7):e16521. https://doi.org/10.2196/16521.

  5. Rule A, Shafer CM, Micek MA, Baltus JJ, Sinsky CA, Arndt BG. Gender Differences in Primary Care Physicians’ Electronic Health Record Use over Time: an Observational Study. J Gen Intern Med. Published online December 6, 2022. https://doi.org/10.1007/s11606-022-07837-2.

  6. 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.

  7. Lieu TA, Freed GL. Unbounded—Parent-Physician Communication in the Era of Portal Messaging. JAMA Pediatr. 2019;173(9):811. https://doi.org/10.1001/jamapediatrics.2019.2309.

  8. Rittenberg E, Liebman JB, Rexrode KM. Primary Care Physician Gender and Electronic Health Record Workload. J Gen Intern Med. 2022;37(13):3295-3301. https://doi.org/10.1007/s11606-021-07298-z.

  9. Rotenstein LS, Fong AS, Jeffery MM, et al. Gender Differences in Time Spent on Documentation and the Electronic Health Record in a Large Ambulatory Network. JAMA Network Open. 2022;5(3):e223935. https://doi.org/10.1001/jamanetworkopen.2022.3935.

  10. Hoonakker PLT, Carayon P, Cartmill RS. The impact of secure messaging on workflow in primary care: Results of a multiple-case, multiple-method study. Int J Med Inform. 2017;100:63-76. https://doi.org/10.1016/j.ijmedinf.2017.01.004.

  11. Crotty BH, Tamrat Y, Mostaghimi A, Safran C, Landon BE. Patient-to-physician messaging: volume nearly tripled as more patients joined system, but per capita rate plateaued. Health Aff (Millwood). 2014;33(10):1817-1822. https://doi.org/10.1377/hlthaff.2013.1145.

  12. Holmgren AJ, Downing NL, Tang M, Sharp C, Longhurst C, Huckman RS. Assessing the impact of the COVID-19 pandemic on clinician ambulatory electronic health record use. J Am Med Inform Assoc. 2022;29(3):453-460. https://doi.org/10.1093/jamia/ocab268.

  13. Nath B, Williams B, Jeffery MM, et al. Trends in Electronic Health Record Inbox Messaging During the COVID-19 Pandemic in an Ambulatory Practice Network in New England. JAMA Netw Open. 2021;4(10):e2131490. https://doi.org/10.1001/jamanetworkopen.2021.31490.

  14. Lieu TA, Altschuler A, Weiner JZ, et al. Primary Care Physicians’ Experiences With and Strategies for Managing Electronic Messages. JAMA Netw Open. 2019;2(12):e1918287. https://doi.org/10.1001/jamanetworkopen.2019.18287.

  15. Rotenstein LS, Holmgren AJ, Downing NL, Bates DW. Differences in Total and After-hours Electronic Health Record Time Across Ambulatory Specialties. JAMA Intern Med. 2021;181(6):863-865. doi:https://doi.org/10.1001/jamainternmed.2021.0256.

  16. 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(3):264-268. https://doi.org/10.1370/afm.2960.

  17. D’Hoore W, Bouckaert A, Tilquin C. Practical considerations on the use of the Charlson comorbidity index with administrative data bases. J Clin Epidemiol. 1996;49(12):1429-1433. https://doi.org/10.1016/s0895-4356(96)00271-5.

  18. Heisey-Grove DM, Carretta HJ. Disparities in Secure Messaging Uptake Between Patients and Physicians: Longitudinal Analysis of Two National Cross-Sectional Surveys. J Med Internet Res. 2020;22(5):e12611. https://doi.org/10.2196/12611

  19. Nagin DS. Group-based trajectory modeling: an overview. Ann Nutr Metab. 2014;65(2-3):205-210. https://doi.org/10.1159/000360229.

  20. Rotenstein L, Jay Holmgren A. COVID exacerbated the gender disparity in physician electronic health record inbox burden. J Am Med Inform Assoc. Published online July 20, 2023:ocad141. https://doi.org/10.1093/jamia/ocad141.

  21. Palen TE, Ross C, Powers JD, Xu S. Association of Online Patient Access to Clinicians and Medical Records With Use of Clinical Services. JAMA. 2012;308(19):2012. https://doi.org/10.1001/jama.2012.14126.

  22. Yamin CK, Emani S, Williams DH, et al. The Digital Divide in Adoption and Use of a Personal Health Record. Arch Intern Med. 2011;171(6). https://doi.org/10.1001/archinternmed.2011.34.

  23. Ancker JS, Hafeez B, Kaushal R. Socioeconomic disparities in adoption of personal health records over time. Am J Manag Care. 2016;22(8):539-540.

  24. Mackert M, Mabry-Flynn A, Champlin S, Donovan EE, Pounders K. Health Literacy and Health Information Technology Adoption: The Potential for a New Digital Divide. J Med Internet Res. 2016;18(10):e264. https://doi.org/10.2196/jmir.6349.

  25. Dudas RA, Pumilia JN, Crocetti M. Pediatric caregiver attitudes and technologic readiness toward electronic follow-up communication in an urban community emergency department. Telemed J E Health. 2013;19(6):493-496. https://doi.org/10.1089/tmj.2012.0166.

  26. Stillman M. Death by Patient Portal. JAMA. 2023;330(3):223-224. https://doi.org/10.1001/jama.2023.11629.

  27. Margolius D, Siff J, Teng K, Einstadter D, Gunzler D, Bolen S. Primary Care Physician Factors Associated with Inbox Message Volume. J Am Board Fam Med. 2020;33(3):460-462. https://doi.org/10.3122/jabfm.2020.03.190360.

  28. Anthony DL, Campos-Castillo C, Lim PS. Who Isn’t Using Patient Portals And Why? Evidence And Implications From A National Sample Of US Adults. Health Aff (Millwood). 2018;37(12):1948-1954. https://doi.org/10.1377/hlthaff.2018.05117.

Topic

JGIM

Author Descriptions

Center for Value-Based Care Research, Cleveland Clinic, Cleveland, OH, USA

Kathryn A. Martinez PhD, MPH, Michael B. Rothberg MD, MPH & Elizabeth R. Pfoh PhD, MPH

Department of Quantitative Health Sciences, Cleveland Clinic, Cleveland, OH, USA

Rebecca Schulte MPH

Department of Pediatrics, Cleveland Clinic, Cleveland, OH, USA

Maria Charmaine Tang MD, FAAP

Share

Related Articles

April 1, 2024

Navigating Uncertainty in Clinical Practice: A Structured Approach

Badar Patel MD EdM, Galina Gheihman MD, Joel T. Katz MD, Arabella Simpkin Begin BMBCh MMSc DPhil & Sonja R. Solomon MD

Abstract The practice of clinical medicine is imbued with uncertainty. The ways…

Learn More
April 1, 2024

The Early HOSPITAL Score to Predict 30-Day Readmission Soon After Hospitalization: a Prospective Multicenter Study

Philippe Mathys MD, Lukas Bütikofer PhD, Daniel Genné MD, Jörg D. Leuppi MD, PhD, Marco Mancinetti MD, MME, Gregor John MD, Drahomir Aujesky MD, MSc & Jacques D. Donzé MD, MSc

Abstract Background The simplified HOSPITAL score is an easy-to-use prediction model to…

Learn More