EHR Use in Inpatient Physicians: Patterns and Predictors

Abstract

Background

Electronic health record (EHR) systems have become integral to hospital-based care, with studies showing physicians spending significant time interfacing with these systems. While EHR interactions are necessary for patient care, understanding usage patterns can identify opportunities for system optimization and workflow improvement. Previous studies have focused on outpatient settings, making this study among the first to comprehensively analyze inpatient EHR interaction patterns.

Objectives

This study aims to characterize EHR utilization patterns among inpatient physicians and analyze how these patterns vary by physician characteristics, including gender, specialty, and years of experience. This analysis aims to identify opportunities for targeted EHR optimization and workflow enhancement strategies.

Methods

We analyzed nine key EHR interaction metrics from Epic Signal across 1,787 inpatient physicians during February 2024. Metrics included time spent in various EHR activities, patient volume, secure message usage, and specific feature utilization. Multivariate regression models were then generated for each outcome metric.

Results

Female physicians spent more time per patient in the EHR (21.74 vs. 15.62 minutes, p < 0.001) and utilized secure messaging features more frequently (messages sent: 0.82 vs. 0.06 per day, p < 0.001). Internal Medicine/Pediatrics demonstrated higher EHR interaction times across multiple metrics compared to Surgical Specialists, even after adjusting for patient load (51.93 vs. 8.37 minutes per day, p < 0.001). Years since graduation showed significant negative correlations with most EHR interaction metrics (r = −0.11 to −0.27, p < 0.001).

Conclusion

This analysis reveals significant variation in EHR utilization patterns across physician demographics and specialties. These findings can inform targeted interventions to optimize EHR workflows and support efficient system usage while maintaining documentation quality and patient care standards.

Keywords

documentation burden - electronic health records and systems - clinical documentation and communications - inpatient - system improvement

Protection of Human and Animal Subjects

This study was performed in compliance with the World Medical Association Declaration of Helsinki on Ethical Principles for Medical Research Involving Human Subjects and was reviewed by the Albert Einstein College of Medicine Institutional Review Board.

^* These authors contributed equally to this work.

Publication History

Received: 19 January 2025

Accepted: 17 May 2025

Article published online:
03 October 2025

• References

• 1 Zlabek JA, Wickus JW, Mathiason MA. Early cost and safety benefits of an inpatient electronic health record. J Am Med Inform Assoc 2011; 18 (02) 169-172
• 2 Han JE, Rabinovich M, Abraham P. et al. Effect of electronic health record implementation in critical care on survival and medication errors. Am J Med Sci 2016; 351 (06) 576-581
• 3 Lin SC, Jha AK, Adler-Milstein J. Electronic health records associated with lower hospital mortality after systems have time to mature. Health Aff (Millwood) 2018; 37 (07) 1128-1135
• 4 HealthIT.gov. National trends in hospital and physician adoption of electronic health records. Accessed September 18, 2024 at: https://www.healthit.gov/data/quickstats/national-trends-hospital-and-physician-adoption-electronic-health-records
• 5 Moy AJ, Schwartz JM, Chen R. et al. Measurement of clinical documentation burden among physicians and nurses using electronic health records: a scoping review. J Am Med Inform Assoc 2021; 28 (05) 998-1008
• 6 Tajirian T, Stergiopoulos V, Strudwick G. et al. The influence of electronic health record use on physician burnout: Cross-sectional survey. J Med Internet Res 2020; 22 (07) e19274
• 7 Shanafelt TD, Dyrbye LN, Sinsky C. et al. Relationship between clerical burden and characteristics of the electronic environment with physician burnout and professional satisfaction. Mayo Clin Proc 2016; 91 (07) 836-848
• 8 Tai-Seale M, Dillon EC, Yang Y. et al. Physicians' well-being linked to in-basket messages generated by algorithms in electronic health records. Health Aff (Millwood) 2019; 38 (07) 1073-1078
• 9 Arndt BG, Beasley JW, Watkinson MD. et al. Tethered to the EHR: Primary care physician workload assessment using EHR event log data and time-motion observations. Ann Fam Med 2017; 15 (05) 419-426
• 10 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
• 11 Gupta K, Murray SG, Sarkar U, Mourad M, Adler-Milstein J. Differences in ambulatory EHR use patterns for male vs. female physicians. NEJM Catalyst. 2019 . Accessed September 18, 2024 at: https://catalyst.nejm.org/ambulatory-ehr-patterns-physician-gender/
• 12 Malacon K, Touponse G, Yoseph E. et al. Gender differences in electronic health record usage among surgeons. JAMA Netw Open 2024; 7 (07) e2421717
• 13 Tang K, Labagnara K, Babar M. et al. Electronic health record usage patterns across surgical subspecialties. Appl Clin Inform 2024; 15 (01) 34-44
• 14 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
• 15 Rittenberg E, Liebman JB, Rexrode KM. Primary care physician gender and electronic health record workload. J Gen Intern Med 2022; 37 (13) 3295-3301
• 16 Ganguli I, Sheridan B, Gray J, Chernew M, Rosenthal MB, Neprash H. Physician work hours and the gender pay gap – evidence from primary care. N Engl J Med 2020; 383 (14) 1349-1357
• 17 Lanham HJ, Leykum LK, McDaniel Jr RR. Same organization, same electronic health records (EHRs) system, different use: exploring the linkage between practice member communication patterns and EHR use patterns in an ambulatory care setting. J Am Med Inform Assoc 2012; 19 (03) 382-391
• 18 Cho H, Nguyen OT, Weaver M. et al. Electronic health record system use and documentation burden of acute and critical care nurse clinicians: a mixed-methods study. J Am Med Inform Assoc 2024; 31 (11) 2540-2549
• 19 Pozdnyakova A, Laiteerapong N, Volerman A. et al. Impact of medical scribes on physician and patient satisfaction in primary care. J Gen Intern Med 2018; 33 (07) 1109-1115
• 20 Frintner MP, Kaelber DC, Kirkendall ES, Lourie EM, Somberg CA, Lehmann CU. The effect of electronic health record burden on pediatricians' work-life balance and career satisfaction. Appl Clin Inform 2021; 12 (03) 697-707
• 21 Goodson JD, Shahbazi S, Rao K, Song Z. Differences in the complexity of office visits by physician specialty: NAMCS 2013–2016. J Gen Intern Med 2020; 35 (06) 1715-1720
• 22 Verma G, Ivanov A, Benn F. et al. Analyses of electronic health records utilization in a large community hospital. PLoS ONE 2020; 15 (07) e0233004
• 23 Oxford MA, McLaughlin CM, McLaughlin CJ, Johnson TS, Roberts JM. Replacing the scalpel with a computer mouse. Ann Plast Surg 2024; 92 (4S, suppl 2): S271-S274
• 24 Ryan MS, Shih SC, Winther CH, Wang JJ. Does it get easier to use an EHR? Report from an urban regional extension center. J Gen Intern Med 2014; 29 (10) 1341-1348
• 25 Sieck CJ, Pearl N, Bright TJ, Yen P-Y. A qualitative study of physician perspectives on adaptation to electronic health records. BMC Med Inform Decis Mak 2020; 20 (01) 25
• 26 Apathy NC, Holmgren AJ, Cross DA. Physician EHR time and visit volume following adoption of team-based documentation support. JAMA Intern Med 2024; 184 (10) 1212-1221
• 27 Rotenstein LS, Holmgren AJ, Horn DM. et al. System-level factors and time spent on electronic health records by primary care physicians. JAMA Netw Open 2023; 6 (11) e2344713
• 28 Overhage JM, Qeadan F, Choi EHE, Vos D, Kroth PJ. Explaining variability in electronic health record effort in primary care ambulatory encounters. Appl Clin Inform 2024; 15 (02) 212-219
• 29 Vainiomäki S, Heponiemi T, Vänskä J, Hyppönen H. Tailoring ehrs for specific working environments improves work well-being of physicians. Int J Environ Res Public Health 2020; 17 (13) 4715
• 30 Kruse CS, Mileski M, Dray G, Johnson Z, Shaw C, Shirodkar H. Physician burnout and the electronic health record leading up to and during the first year of Covid-19: Systematic review. J Med Internet Res 2022; 24 (03) e36200
• 31 Sieja A, Markley K, Pell J. et al. Optimization sprints: Improving clinician satisfaction and teamwork by rapidly reducing electronic health record burden. Mayo Clin Proc 2019; 94 (05) 793-802
• 32 Smith L, Kirk W, Bennett MM, Youens K, Ramm J. From headache to handled: Advanced in-basket management system in primary care clinics reduces provider workload burden and self-reported burnout. Appl Clin Inform 2024; 15 (05) 869-876
• 33 Lourie EM, Utidjian LH, Ricci MF, Webster L, Young C, Grenfell SM. Reducing electronic health record-related burnout in providers through a personalized efficiency improvement program. J Am Med Inform Assoc 2021; 28 (05) 931-937
• 34 Rinne ST, Brunner J, Mohr DC, Bearak A-C, Anderson E. Practices supporting electronic health record transitions: Lessons from four US healthcare systems. J Gen Intern Med 2023; 38 (Suppl. 04) 1015-1022
• 35 Shah S, Bedgood M, Devon-Sand A. et al. Effect of an electronic health record-based intervention on documentation practices. Appl Clin Inform 2024; 15 (04) 771-777
• 36 Kang C, Sarkar IN. Interventions to reduce electronic health record-related burnout: A systematic review. Appl Clin Inform 2024; 15 (01) 10-25
• 37 Jeppson J. What can Epic's Signal data tell us about EHR satisfaction and burnout?. KLAS Research. 2020 . Accessed September 18, 2024 at: https://klasresearch.com/archcollaborative/report/what-can-epics-signal-data-tell-us-about-ehr-satisfaction-and-burnout/324#:~:text=Signal%20Data%20Not%20a%20Reliable,increases%20but%20only%20to%2015%25
• 38 Murad MH, Vaa Stelling BE, West CP. et al. Measuring documentation burden in healthcare. J Gen Intern Med 2024; 39 (14) 2837-2848