Gender Differences in Electronic Health Record Usage Among Surgeons

Key Points Question Are there gender differences in electronic health record (EHR) usage among surgeons? Findings In this cross-sectional study of 224 attending surgeons, female surgeons spent more time logged into the EHR system outside of 7 am to 7 pm, spent more time in the EHR outside of scheduled clinic hours, spent more time per note, wrote longer progress and documentation notes, had fewer appointments, and wrote fewer medical records compared with their male colleagues. Meaning This study’s results suggest the need for a comprehensive understanding of gender differences in EHR usage among surgeons and the associated implications.


Introduction
1][12] Disparities in EHR use may represent uncompensated labor that is not accounted for in the already substantial gender pay gap. 2,13Given the mounting evidence of gender differences in EHR usage and the association between EHR-related tasks and burnout, 14 it is imperative to investigate the variations in the burden of EHR usage among female vs male surgeons.
6][17] However, these studies have largely focused on nonsurgical specialties, including primary care 15 and physicians in ambulatory care . 17Due to the different practice patterns and day-to-day tasks of surgeons compared with nonsurgeons and the increasing proportion of female surgeons, there is a need to further study differences in EHR usage between male and female attending surgeons.
In light of these knowledge gaps, we conducted a comprehensive investigation into gender differences in EHR usage among surgeons.The primary objective of this study was to analyze EHR usage patterns among surgeons during the specified study period.We hypothesized that female surgeons face an increased burden in EHR usage compared with their male colleagues.

Study Design
This retrospective cross-sectional study used data obtained from an EHR system, Epic Signal (Epic Systems), from January 1, 2022, through December 31, 2022.Inclusion criteria were surgeons affiliated with a single academic institution, Stanford University, who had completed residency training and who saw any patients in the outpatient setting during the study period.This study was conducted in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline.

JAMA Network Open | Equity, Diversity, and Inclusion
Gender Differences in Electronic Health Record Usage Among Surgeons

Ethical Considerations
As this study involved retrospective analysis of deidentified EHR data, it was exempted from the requirement for informed consent.Ethical approval for the study was obtained from the Stanford University institutional review board.

Data Collection and Variables
We collected variables defining surgeon characteristics (gender, specialty, academic position, time in practice since residency), patient characteristics (age, items on problem list), and EHR variables (time in system, appointments, documentation characteristics, level of service [defined by Current Procedural Terminology billing codes for consults, established patients, or new patients levels 1-5], turnaround time, messaging/calls, orders).The EHR system dataset included gender information obtained from the physician's personal information table and specialty data obtained from the department to which the physician belongs.Academic positions (affiliate, assistant professor, associate professor, professor, instructor) and time in practice were determined from the publicly available online Stanford Community Academic Profile (CAP) directory.Primary EHR variables were progress note length (inpatient note character length), documentation length (outpatient note character length), time spent in medical records, and time documenting patient encounters.The method of composition describes the percentage of notes written manually, using a SmartTool, transcription, or copy and paste.Time spent outside of scheduled clinic hours represents time spent logged into the EHR system 30 minutes or more before the first and after the last appointment scheduled, respectively, on clinic days (determined based on integration with the Cadence calendar system).Time spent outside 7AM to 7PM is specific to scheduled clinic days.The time in system metric encompasses both clinic and nonclinic days.Inactive time, defined as periods when the Hyperspace window is not in focus or exceeds 5 seconds of inactivity, is not captured in the EHR system data.

Statistical Analysis
We summarized EHR system data (ie, sum, average) across 1-month reporting periods for each clinician.The number of reporting periods varied depending on the time each surgeon worked over the year of data collection.To equally weigh each surgeon's data within the analysis despite varying time worked, all variables were averaged across reporting periods for each surgeon to yield a single number for each variable per surgeon.Continuous variables were summarized with median and IQR.
Median and IQR were used to report variables with nonnormal distributions which were assessed via the Kolmogorov-Smirnov test and visually with histograms (eFigure in Supplement 1).The Kruskal-Wallis test was used to compare nonnormal continuous variables between male and female surgeons.Categorical variables were summarized using proportion and frequency and compared using the χ 2 test.Histograms and scatterplots were used to visualize data.Outlying data points were removed from scatterplots using the Robust regression and OUTlier removal package in GraphPad Prism 8 (GraphPad Software) with Q of 0.1% (most conservative) to better visualize the distributions.
Outlier removal was not conducted for mean and median values with accompanying P values in data tables.
Multivariate linear regression was used with primary EHR usage variables as dependent variables (documentation length, progress note length, time in notes per note, total hospital medical records, time outside 7AM to 7PM, time outside scheduled clinic hours) and surgeon gender, time in practice since residency, surgeon specialty, surgeon academic appointment, average patient age, and average number of problem list items as independent variables.To address potential multicollinearity in our model, we implemented a variance inflation factor (VIF) cutoff of 4, which resulted in the removal of the variable academic appointment from the analysis.Several specialties were grouped together to address potential unstable numeric issues during model fitting, stemming from sparse cell counts present in several specialties.Specifically, for the regression analysis, surgeon specialties were grouped into the following categories: cardiothoracic surgery, neurosurgery, orthopedic surgery, plastic and hand surgery, general surgery (including general surgery, colon and rectal

JAMA Network Open | Equity, Diversity, and Inclusion
Gender Differences in Electronic Health Record Usage Among Surgeons
This included 171 812 progress notes, 252 883 documents, and a total of 2 181 819 minutes in the EHR in our analysis.The median (IQR) time in practice was 14.0 (7.8-24.3)years.Surgeons spanned 14 specialties, with the most common being general surgeons (n = 45 [20%]) and orthopedic surgeons The orthopedic surgery department had the highest proportion of male surgeons (84%), compared with general surgery which had a higher proportion of female surgeons (51%) (eTable 2 in Supplement 1).Male surgeons represented a higher proportion of full professors (38%), whereas female surgeons comprised a higher proportion of assistant professors (46%) (  1).
There was no significant difference in median (IQR) time spent in the EHR system per month (664.1 [301.0-1299.1]vs 635.0 [315.6-1192.0]minutes; P = .89);however, male surgeons had a higher   2).While there was no significant difference in fraction of notes written by surgeon vs other or using transcription or SmartTools, female surgeons wrote a higher fraction of the notes manually (17% vs 12%; P = .006)and a higher fraction using copy and paste (10% vs 5%; P = .04).Female surgeons had a higher percentage of level 5 consult visits (38.9% vs 17.8%; P = .02),a higher percentage of level 5 new patient visits (38.4% vs P = .001),a lower percentage of level 3 consult visits (15.2% vs 32.6%; P = .05),and a lower percentage of level 3 new patient visits (18.1% vs 32.7%; P = .001),as compared with their male colleagues.There were no significant differences between female and male surgeons in turnaround time, messages and/or calls, and appointments closed per month (eTable 3 in Supplement 1).
Multivariable linear regression analyses were performed to determine the relative association of gender with primary EHR variables (Table 3).After controlling for surgeon's years in practice, specialty, median patient age, and median number of problem list items, surgeon's male gender was Cardiothoracic surgery was associated with significantly shorter documentation length; orthopedic surgery, plastic and hand surgery, and podiatry were all associated with significantly shorter documentation length and progress note length as compared with general surgery (all P < .05)(Table 3).Cardiothoracic surgery was associated with more hospital medical records completed per month (P < .001),while orthopedic surgery, otolaryngology, plastic and hand surgery, and urology were associated with fewer completed hospital medical records per month than general surgery (all P < .02)(Table 3).Despite lower medical record completion, otolaryngology and urology were associated with significantly higher time outside 7AM to 7PM and scheduled clinic hours (all P < .01)(eTable 4 in Supplement 1).2000 4000 6000 8000 10 000 12 000 14 000 16 000 18 000 20 000 22 000 24 000 26 000 0 2000 4000 6000 8000 10 000 12 000 14 000 16 000 18 000 20 000 22 000 24 000

Discussion
In this study, we report gender differences in EHR usage between male and female surgeons at a large academic hospital.Specifically, we observed that despite seeing 74% of patients and completing 67% of medical records per month compared with their male counterparts, female surgeons spent an equivalent amount of time in the EHR system.Furthermore, female surgeons spent 28% more time in the system outside of scheduled clinic hours and 158% more time outside of 7AM to 7PM, despite seeing fewer patients and completing fewer hospital medical records.In addition, female surgeons spent more time writing individual notes compared with their male colleagues.Finally, after controlling for years in practice, specialty, average patient age, and average number of items in patient problem list in multivariate linear regression, female gender was significantly associated with fewer total hospital medical records completed per month and greater progress note and average documentation note length.
These findings are consistent with previous studies showing gender disparities in EHR usage among health care professionals.6][17] Our study adds to this body of evidence by demonstrating that these disparities exist within the surgical field as well.While prior studies shed light on the substantial time surgical residents dedicate to EHR usage, [18][19][20] they did not examine EHR use among attending surgeons or provide gender-specific stratification.While we found that female and male surgeons spent an equal amount of total time in the EHR system, female surgeons had on average 21 fewer appointments per month, suggesting that time spent per patient on EHR was greater for female vs male surgeons.Female surgeons also spent more time in the EHR system after hours, which may contribute to greater burnout. 21e reasons behind the differences in patient volume and medical record writing between male and female surgeons warrant further exploration.It is possible that variations in career choices and preferences may contribute to these disparities.For instance, women surgeons may opt for lighter schedules to balance personal and professional responsibilities, 22 leading to a lower patient load.
Additionally, the administrative burden associated with EHR usage may influence patient volume, as female surgeons spend substantial time on documentation tasks, leaving less time available for patient care.4][25] These practices could exacerbate the gender pay gap 2 and/or contribute to increased burnout in female surgeons. 3,26We also report differences in documentation length across various specialties, specifically with orthopedic surgeons, plastic and hand surgeons, and podiatrists writing shorter notes.This is not surprising, given these specialties are often focused on one specific problem that may be more straightforward than those in other surgical specialties.Other studies have reported similar differences in note length among surgical specialties, 27 which may stem from heterogeneous documentation standards, templates, or strategies (eg, transcription use), in addition to complexity of cases and patients. 15 our study, female surgeons wrote inpatient progress notes that were 40% longer and outpatient documentations that were 42% longer than their male colleagues.Furthermore, female surgeons manually documented their notes 42% more frequently compared with their male counterparts.Administrative burdens that disproportionately fall on female surgeons, such as the expectation of more comprehensive documentation, could potentially devalue the work performed by female surgeons.Furthermore, although we could not differentiate whether the clinical notes were cosigned by advanced practice practitioners (APPs), it is important to highlight that additional APP support frequently depends on productivity and clinic volume.If the presence of APPs is proven to enhance productivity, it might unintentionally contribute to a self-reinforcing cycle, potentially exacerbating existing gender disparities.
We found that female surgeons had 2 times the percentage of level 5 consult and new patient visits compared with their male colleagues.Level 5 billing is the highest level of complexity for new patient visits and is typically used when the patient's condition requires a comprehensive evaluation and management. 28,29Given the increased time spent by female surgeons in the EHR, it may be appropriate that female surgeons are billing more frequently for a higher level of service.However, we are unable to determine whether the increased relative value units from the higher level of service visits would counteract the effect of the fewer patients seen overall by female vs male surgeons when determining overall productivity and pay.Our findings are consistent with existing literature showing that female surgeons invest more time in individual patient encounters. 30This discovery further supports the widespread criticism that productivity metrics based solely on volume do not adequately capture the true extent of physician work. 31,32dressing these disparities requires a multifaceted approach.Policy interventions aimed at promoting equity in compensation could include adjusting payment structures to recognize the additional time and effort invested in EHR usage by female surgeons.Moreover, reassessing the documentation requirements and teaching efficient documentation practices may help alleviate the administrative burden on all surgeons while maintaining the necessary quality of care.Implementing targeted EHR concierge services specifically for women surgeons could alleviate documentationrelated stress and enhance overall efficiency.At our institution, EHR concierge services are currently offered equally, and voluntarily, to all physicians.These services include personalized needs assessment, professional observation, and individualized training, all of which have demonstrated substantial uptake and positive outcomes. 33Providing individual physician feedback on EHR use patterns may encourage clinicians to improve through the use of smart tools, templates, and preference lists to enhance efficiency. 17Future research should investigate the complex interplay between gender expectations, patient outcomes, and the time allocated to clinical encounters.
Recognizing and addressing the gender disparities in workload is crucial for promoting diversity, equity, and inclusion within the surgical field.

Figure 1 . 9 [
Figure 1.Scatterplots Demonstrate Individual Data Points and Median Values for Clinic Schedule and EHR Usage Variables Stratified by Gender

Figure 2 .
Figure 2. Distributions and Scatterplots of Progress Note and Documentation Length Stratified by Gender note, No. of characters Progress note characters by surgeon percentage A 0

Table 3 .
Multivariable Linear Regression Models for Documentation Length, Progress Note Length, and Total Hospital Medical Records Completed a General surgery reference category includes general surgery, colon and rectal surgery, transplant surgery, endocrine surgery, and trauma surgery.