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DOI: 10.1055/a-2194-1061
Electronic Health Record Usage Patterns Across Surgical Subspecialties
Autoren
Funding None.
Abstract
Objectives This study aimed to utilize metrics from physician action logs to analyze surgeon clinical, volume, electronic health record (EHR) efficiency, EHR proficiency, and workload outside scheduled time as impacted by physician characteristics such as years of experience, gender, subspecialty, academic title, and administrative title.
Methods We selected 30 metrics from Epic Signal, an analytic tool in Epic that extracts metrics related to clinician documentation. Metrics measuring appointments, messages, and scheduled hours per day were used as a correlate for volume. EHR efficiency, and proficiency were measured by scores built into Epic Signal. Metrics measuring time spent in the EHR outside working hours were used as a correlate for documentation burden. We analyzed these metrics among surgeons at our institution across 4 months and correlated them with physician characteristics.
Results Analysis of 133 surgeons showed that, when stratified by gender, female surgeons had significantly higher EHR metrics for time per day, time per appointment, and documentation burden, and significantly lower EHR metrics for efficiency when compared to male surgeons. When stratified by experience, surgeons with 0 to 5 years of experience had significantly lower EHR metrics for volume, time per day, efficiency, and proficiency when compared to surgeons with 6 to 10 and more than 10 years of experience. On multivariate analysis, having over 10 years of experience was an independent predictor of more appointments per day, greater proficiency, and spending less time per completed message. Female gender was an independent predictor of spending more time in notes per appointment and time spent in the EHR outside working hours.
Conclusion The burden associated with volume, proficiency, efficiency, and workload outside scheduled time related to EHR use varies by gender and years of experience in our cohort of surgeons. Evaluation of physician action logs could help identify those at higher risk of burnout due to burdensome medical documentation.
Background and Significance
Since the American Recovery and Reinvestment Act, physicians' use of electronic health records (EHR) has risen from 9% in 2008 to 96% in 2017, primarily due to its use as a requirement for clinicians who seek to receive Medicaid and Medicare reimbursement.[1] [2] Although EHR implementation has led to quicker and more efficient information sharing between institutions and vastly improved continuity of patient care, there are unique challenges that physicians face in having to care for patients while also fulfilling required electronic documentation. Several recent studies have associated EHR use with an increase in clinician frustration and burnout with many clinicians citing EHR use as a major contributor to their feelings of burnout.[3] [4] [5] [6] [7] [8] [9] [10]
Across general surgery and the surgical subspecialities, quantitative evidence demonstrating the struggles of EHR documentation is lacking. Few studies have described the time spent on the EHR in individual surgical specialties, such as general surgery, vascular surgery, orthopaedic surgery, and ophthalmology. Despite this, none have broadly analyzed all surgical fields and attempted to describe predictors of time spent on EHR.[11] [12] [13] [14] [15] Moreover, there is evidence highlighting that women and less experienced clinicians spend more time on the EHR, possibly correlating with more feelings of burnout.[16] However, these studies were limited to the ambulatory setting and lacked an analysis on clinician characteristics, such as age, gender, and experience.
Objectives
The objective of the current study is to identify and evaluate the impact of surgeon-level characteristics on volume, efficiency, proficiency, and burden as it pertains to EHR usage by surgeons across various surgical specialties. Given that current literature indicates that certain surgeon-level characteristics, such as being female and having fewer years of experience, are associated with higher EHR burden,[16] we hypothesize that males and those with more years of experience will handle higher volumes more efficiently and proficiently, resulting in lower EHR burden.
Methods
Data on surgeon EHR usage were collected using Epic Signal, a software that can extract a variety of clinician outpatient documentation over a specified period in the EHR system Epic.[17] [18] [19] Institutional review board approval was obtained prior to study commencement. Clinician data from each month between February and June of 2022 were collected and included those from the fields of general surgery, cardiothoracic surgery, neurosurgery, orthopaedic surgery, otolaryngology, plastic and reconstructive surgery, urology, vascular surgery, and pediatric surgery. Those from the department of transplant surgery, ophthalmology, and obstetrics and gynecology were excluded due to their unique work schedules. Surgeons not working full-time over the course of 5 months were excluded, along with those holding the most senior administrative positions in their respective departments.
Surgeon demographic characteristics were collected from the Montefiore physician directory, Albert Einstein College of Medicine faculty, and their public profile on Doximity. Demographic information collected included years of experience, gender, specialty, academic title, and administrative title. Surgeon experience was evaluated as the number of years practicing as an attending, which was determined from the surgeon's year of graduation from their last residency or fellowship. Metrics including appointments per day, scheduled hours per day, messages per day, time in system, time in notes per appointment, time per completed message, proficiency score, time on unscheduled days, and time outside of 7 a.m. to 7 p.m. were collected for each of the 5 months in the study and averaged for each surgeon. A detailed breakdown of surgeon electronic medical record (EMR) metrics is provided in [Table 1].
Abbreviation: ED, Emergency Department.
For all time-related metrics, time was measured starting from the moment the physician logged into the Epic system and began actively interacting with it, including any mouse activity, clicks, scrolling, and key presses. Periods of inactivity longer than 5 seconds, or instances when the physician logged out of the Epic system, resulted in pauses in the time measurement. Metrics were collected solely from surgeons' individual Epic accounts, and not from other health care providers who may have assisted in patient care. Proficiency, scaled from 0 to 10, reflects the frequency with which a physician utilizes Epic's integrated efficiency features such as chart search, preference lists, quick action tools, and speed buttons.
Descriptive statistics were generated for all metrics during the specified time period among all physicians, by gender and by years of experience. Years of experience was categorized as groups of 0 to 5, 6 to 10, and >10 years. Chi-square tests were used to compare categorical variables. Due to their skewed distributions, continuous metrics were analyzed using Mann–Whitney U tests for gender, and Kruskal–Wallis tests for years of experience.
Univariate analyses to analyze the association of select outcome metrics with gender, years of experience, pediatric specialty, associate professor tenure, and holding an administrative position were performed using linear regression. These metrics include appointments per day, scheduled hours per day, messages per day, time in system, time in notes per appointment, time per completed message, proficiency score, time on unscheduled days, and time outside of 7 a.m. to 7 p.m. Multivariate regression models that included all the aforementioned predictors were then generated for each outcome metric. Significance level of p-values for all tests was set at <0.05. All statistical analysis was performed using SPSS v28.0 (IBM Corp., Armonk, NY).
Results
A total of 158 surgeons were screened for eligibility, of which 133 (84.2%) met inclusion. Twenty-five surgeons were excluded for the following reasons: within the department of transplant surgery (9), not working full-time over the course of the 5 months (4), and holding the most senior administrative positions (12).
All Surgeons
The majority of surgeons were males (70.7%), in the specialty of Orthopaedic Surgery (27.8%), held the administrative title of assistant professor (68.0%) and had >10 years of experience (38.3%). All surgeons had a median of 18.6 appointments (13.2–26.5) per day, spent a median of 3.5 minutes (2.0–5.7 min) in in-basket per day, and 2.4 minutes (1.6–4.1 min) in Notes per appointment. In terms of efficiency, all surgeons closed 81.5% (49.7–94.8%) of appointments the same day and spent 0.4 minute (0.2–0.6 min) per completed message. All surgeons spent a median of 19.4 minutes (11.6–33.6 min) on unscheduled days with a median pajama time of 10.4 minutes (4.5–24.5 min; [Table 2]).
|
All providers |
Male |
Female |
p-Value |
|
|---|---|---|---|---|
|
n (%) |
133 |
94 (70.7) |
39 (29.3) |
– |
|
Experience, n (%) |
0.91 |
|||
|
0 to 5 years |
41 (30.8) |
28 (29.8) |
13 (33.3) |
– |
|
6 to 10 years |
41 (30.8) |
29 (30.9) |
12 (30.8) |
– |
|
>10 years |
51 (38.3) |
37 (39.4) |
14 (35.9) |
– |
|
Specialty, n (%) |
0.010 |
|||
|
Cardiothoracic surgery |
8 (6.0) |
7 (7.4) |
1 (2.6) |
– |
|
Neurosurgery |
10 (7.5) |
9 (9.6) |
1 (2.6) |
– |
|
Orthopaedic surgery |
37 (27.8) |
33 (35.1) |
4 (10.3) |
– |
|
Otolaryngology |
20 (15.0) |
10 (10.6) |
10 (25.6) |
– |
|
Plastic/Reconstructive surgery |
7 (5.3) |
5 (5.3) |
2 (5.1) |
– |
|
Surgery |
29 (21.8) |
18 (19.1) |
11 (28.2) |
– |
|
Urology |
15 (11.3) |
7 (7.4) |
8 (20.5) |
– |
|
Vascular surgery |
7 (5.3) |
5 (5.3) |
2 (5.1) |
– |
|
Pediatric (across all specialties)[a] |
18 (13.5) |
9 (9.6) |
9 (23.1) |
0.038 |
|
Title, n (%) |
0.48 |
|||
|
Professor |
8 (6.3) |
7 (7.8) |
1 (2.6) |
– |
|
Associate professor |
33 (25.8) |
24 (26.7) |
9 (23.7) |
– |
|
Assistant professor |
87 (68.0) |
59 (65.6) |
28 (73.7) |
– |
|
Admin position[a] |
53 (39.8) |
41 (43.6) |
12 (30.8) |
0.17 |
|
Volume, median (IQR) |
||||
|
Appointments per day |
18.6 (13.2–26.5) |
19.5 (12.4–29.0) |
17.1 (13.3–22.5) |
0.19 |
|
Percentage of days with appointments |
27.0 (18.2–35.9) |
27.4 (18.5–35.9) |
26.7 (17.7–36.0) |
0.72 |
|
Scheduled hours per day |
5.7 (3.7–7.7) |
6.0 (3.4–7.9) |
5.5 (4.2–6.4) |
0.38 |
|
Aggregate messages per day |
11.5 (7.9–19.9) |
11.1 (7.5–20.3) |
14.0 (8.5–18.3) |
0.44 |
|
Time per day, median (IQR) |
||||
|
Time in system (min) |
57.5 (27.2–96.1) |
53.3 (24.4–90.8) |
73.6 (40.1–110.0) |
0.046 |
|
Time in notes (min) |
18.1 (7.6–37.0) |
16.1 (7.1–35.8) |
30.2 (10.3–39.8) |
0.067 |
|
Time in clinical review (min) |
9.7 (5.5–14.7) |
8.8 (5.4–13.9) |
11.6 (7.2–17.4) |
0.06 |
|
Time in schedule (min) |
7.9 (3.8–12.6) |
7.5 (3.7–12.7) |
9.4 (5.2–11.9) |
0.54 |
|
Time in orders (min) |
6.9 (2.5–13.4) |
5.7 (2.1–13.4) |
8.9 (4.0–15.0) |
0.13 |
|
Time in visit navigator (min) |
4.1 (2.1–7.3) |
3.6 (2.0–6.2) |
5.3 (2.7–7.9) |
0.05 |
|
Time in in-basket (min) |
3.5 (2.0–5.7) |
3.4 (1.3–5.3) |
4.5 (2.9–7.6) |
0.016 |
|
Percentage of time in system: ambulatory |
57.3 (41.2–66.6) |
56.1 (35.8–66.6) |
61.4 (52.1–66.9) |
0.26 |
|
Percentage of time in system: ED |
0.2 (0.0–1.0) |
0.1 (0.0–1.0) |
0.1 (0.0–0.8) |
0.91 |
|
Percentage of time in system: inpatient |
11.9 (5.9–21.8) |
12.6 (5.9–24.2) |
7.4 (5.9–16.5) |
0.11 |
|
Time per appointment, median (IQR) |
||||
|
Time in notes (min) |
2.4 (1.6–4.1) |
2.1 (1.4–3.7) |
3.2 (2.0–4.8) |
0.009 |
|
Time in clinical review (min) |
1.4 (0.7–2.3) |
1.4 (0.6–2.1) |
1.7 (1.0–2.7) |
0.084 |
|
Time in in-basket (min) |
0.5 (0.2–1.0) |
0.4 (0.2–1.0) |
0.7 (0.5–1.2) |
0.01 |
|
Time in orders (min) |
0.8 (0.4–1.4) |
0.7 (0.4–1.2) |
1.2 (0.5–2.1) |
0.01 |
|
Efficiency, median (IQR) |
||||
|
Percentage of appointments closed same day |
81.5 (49.7–94.8) |
82.3 (46.9–96.3) |
77.5 (50.0–87.4) |
0.56 |
|
Time in notes per note (min) |
1.8 (1.2–3.1) |
1.8 (1.1–3.2) |
2.2 (1.3–3.1) |
0.4 |
|
Time per completed message (min) |
0.4 (0.2–0.6) |
0.4 (0.3–0.6) |
0.5 (0.3–0.7) |
0.045 |
|
Documentation length per note (characters) |
2,278 (1,605–4,113) |
2,365 (1,491–4,295) |
2,148 (1,714–2,879) |
0.27 |
|
Progress note length (characters) |
4,495 (3,250–5,780) |
4,585 (2,855–5,982) |
4,400 (3,386–5,373) |
0.8 |
|
Orders with team contributions (%) |
7.3 (0.9–51.8) |
7.5 (0.6–54.6) |
7.3 (1.5–47.7) |
0.77 |
|
Proficiency, median (IQR) |
||||
|
Proficiency score |
3.4 (2.1–6.0) |
3.7 (1.9–6.5) |
3.2 (2.2–5.2) |
0.21 |
|
Workload outside scheduled time, median (IQR) |
||||
|
Pajama time (min) |
10.4 (4.5–24.5) |
8.6 (4.2–26.0) |
12.5 (6.5–23.4) |
0.56 |
|
Time on unscheduled days (min) |
19.4 (11.6–33.6) |
17.2 (10.2–31.4) |
23.4 (15.9–39.6) |
0.023 |
|
Time outside 7 a.m. to 7 p.m. (min) |
6.3 (2.2–15.8) |
5.5 (2.3–14.5) |
9.4 (2.1–21.7) |
0.18 |
|
Time outside scheduled hours (min) |
19.1 (8.4–28.8) |
17.9 (7.9–31.0) |
21.0 (12.2–25.5) |
0.55 |
Abbreviation: IQR, interquartile range.
a Pediatric and Admin position were tested separately.
Gender
When stratified by gender, the majority of surgeons were males (70.7%). A significantly greater proportion of male surgeons were in the specialty of Orthopaedic Surgery (male 35.1% vs. female 10.3%, p = 0.010) when compared to other specialties. On the contrary, a significantly greater proportion of female surgeons were in the specialty of Otolaryngology (female 25.6% vs. male 10.6%, p = 0.010), Urology (female 28.2% vs. male 19.1%, p = 0.010), and Pediatrics (female 23.1% vs. male 9.6%, p = 0.038). There were no significant differences in years of experience or academic and administrative titles between genders.
Female surgeons had significantly higher EHR metrics for time per day, time per appointment, and documentation burden, and significantly lower EHR metric for efficiency when compared to male surgeons. More specifically, for time per day, female surgeons spent significantly more time in the system (female 73.6 min [40.1–110.0 min] vs. male 53.3 min [24.4–90.8 min], p = 0.046), time in visit navigator (female 5.3 min [2.7–7.9 min] vs. male 3.6 min [2.0–6.2 min], p = 0.05), and time in basket (female 4.5 min [2.9–7.6 min] vs. male 3.4 min [1.3–5.3 min], p = 0.016). For time per appointment, female surgeons spent significantly more time in notes (female 3.2 min [2.0–4.8 min] vs. male 2.1 min [1.4–3.7 min], p = 0.009), time in basket (female 0.7 min [0.5–1.2 min] vs. male 0.4 min [0.2–1.0 min], p = 0.01), and time in orders (female 1.2 min [0.5–2.1 min] vs. male 0.7 min [0.4–1.2 min], p = 0.01). Regarding workload outside scheduled time metrics, female surgeons spent significantly more time on unscheduled days (female 23.4 min [15.9–39.6 min] vs. male 17.2 min [10.2–31.4 min], p = 0.023). For efficiency, female surgeons spent significantly more time per completed message (female 0.5 min [0.3–0.7 min] vs. male 0.4 min [0.2–0.6 min], p = 0.045). There were no significant differences in the EHR metrics of volume or proficiency between genders ([Table 2]).
Years of Experience
When stratified by years of experience, there were similar number of surgeons in each group: 0 to 5 years (30.8%), 6 to 10 years (30.8%), >10 years (38.3%). Surgeons with 6 to 10 or >10 years of experience held significantly more admin positions (0–5 years 7.3% vs. 6–10 years 53.7% vs. >10 years 54.9%, p < 0.001), while surgeons with 0 to 5 years of experience held significantly more assistant professor positions (0–5 years 94.9% vs. 6–10 years 67.5% vs. >10 years 46.9%, p < 0.001).
Surgeons with 0 to 5 years of experience had significantly lower EHR metrics for volume, time per day, efficiency, and proficiency when compared to surgeons with 6 to 10 and >10 years of experience. More specifically, for volume, surgeons with 0 to 5 years of experience had significantly fewer appointments per day (0–5 years 14.7 [9.4–25.0] vs. 6–10 years 19.7 [15.1–24.7] vs. >10 years 19.3 [15.0–30.5], p = 0.017), fewer scheduled hours per day (0–5 years 4.4 [3.0–6.4] vs. 6–10 years 6.0 [4.3–7.7] vs. >10 years 5.9 [4.2–8.4], p = 0.041), and aggregate messages per day (0–5 years 8.9 [5.6–16.6] vs. 6–10 years 14.5 [9.6–21.1] vs. >10 years 11.3 [8.7–19.4], p = 0.015). For time per day, surgeons with 0 to 5 years of experience spent significantly less time in orders (0–5 years 3.4 min [0.9–10.5 min] vs. 6–10 years 7.6 min [3.7–13.6 min] vs. >10 years 8.9 min [3.4–15.0 min], p = 0.034), and time in visitor navigator (0–5 years 2.8 min [1.3–5.9 min] vs. 6–10 years 4.5 min [2.4–7.3 min] vs. >10 years 4.3 min [2.7–8.0], p = 0.023). For efficiency, surgeons with 0 to 5 years of experience spent significantly more time per note (0–5 years 2.4 min [1.5–4.4 min] vs. 6–10 years 1.4 min [1.1–2.7 min] vs. >10 years 1.8 min [1.2–2.9 min], p = 0.016) and time per completed message (0–5 years 0.6 min [0.3–0.8] vs. 6–10 years 0.4 min [0.2–0.6] vs. >10 years 0.3 min [0.2–0.5 min], p = 0.002). For proficiency, surgeons with 0 to 5 years of experience had a significantly lower proficiency score (0–5 years 2.9 [1.6–4.5] vs. 6–10 years 3.3 [2.2–4.5] vs. >10 years 5.2 [2.6–6.6], p = 0.035). There were no significant differences in the EHR metrics of specialty or workload outside scheduled time across years of experience ([Table 3]).
Abbreviation: IQR, interquartile range.
Multivariate Analysis
On multivariate analysis, >10 years of experience was an independent predictor of more appointments per day (odds ratio [OR] 7.96, 95% confidence interval [CI] 2.30, 13.62) and scheduled hours per day (OR 1.74, 95% CI 0.38, 3.10), greater proficiency (OR 1.53, 95% CI 0.43, 2.63), and spending less time per completed message (OR −15.26, 95% CI −25.49, −5.04). Pediatric specialty was an independent predictor of fewer scheduled hours per day (OR −1.69, 95% CI −3.12, −0.26) and spending more time per completed message (OR 11.52, 95% CI 0.75, 22.29). Furthermore, 6 to 10 years of experience was an independent predictor of receiving more messages per day (OR 4.50, 95% CI 0.51, 8.49). Finally, female gender was an independent predictor of spending more time in notes per appointment (OR 1.07, 95% CI 0.18, 1.95) and time outside of 7 a.m. to 7 p.m. (OR 4.75, 95% CI 0.47, 9.04; [Table 4]).
Abbreviation: CI, confidence interval.
Discussion
The current study is the largest to analyze EHR use within general surgery and surgical subspecialties adjusted by gender, years of experience, and tenure. By using quantitative metrics representative for volume, efficiency, proficiency, and burden, we presented a tangible method to describe surgeons' experience with the EHR. Overall, surgeons spent 57.5 minutes per day logged into the EHR, 19.1 minutes per day in the EHR outside the scheduled hours, and 19.4 minutes per day in the EHR outside the scheduled days. After adjusting for surgeon gender, years of experience, tenure, and pediatric specialties, we found significant associations between female gender and time spent in notes per appointment, along with time spent outside the work hours. Additionally, having over 10 years of experience was significantly associated with higher metrics for volume, efficiency, and proficiency.
We found female surgeons spent just over 1 minute longer writing notes per appointment and additionally spent more time outside work hours despite having a similar patient volume as their male surgeon counterparts. Given that documentation burden is a contributor to burnout, burnout in our study may be more prevalent among female surgeons and is consistent with the numerous studies that have documented higher rates of burnout in female surgeons at the workplace.[20] [21] [22] [23] [24] [25] [26] Burnout in female surgeons is likely multifactorial. One study found that burnout among female surgeons may be related to frustrations with gender bias encountered in the workplace regarding being seen as incompetent by colleagues.[21] Another study found that female surgical residents reported more frequently feeling under strain, burned out from work, unhappy/depressed, less confident, and thought of themselves as worthless as they navigate residency. The study suggested that female surgical residents may be experiencing these more negative outcomes during residency due to lack of like-gendered leadership and mentorship.[22] Furthermore, another study observed that burnout among female general surgeons was associated with decreased professional fulfillment and lower sense of control over schedule.[23] Our current study adds to the body of the gender-specific surgeon burnout literature by suggesting that female surgeon burnout may also be attributed to spending more time in the EHR. This is an important area for further research, and future studies should focus on understanding the underlying causes to guide targeted interventions aimed at reducing this gender imbalance.
As surgeons move onto practicing as independent attendings, often in health care systems with different EHR, they must balance establishing a patient base and managing their care with learning how to operate efficiently and effectively in this new role. With the growing emphasis and subsequent time spent on documentation, the learning curve is only becoming steeper for new attending physicians. In our cohort, we observed significant differences in surgeon EHR metrics among those with more years of experience since completing training, even after controlling for gender, tenure, administrative duties, and specialty. Those with more than 5 years of experience saw a greater workload in the form of patient volume, scheduled hours per day, and aggregate messages received per day, with the number of messages declining after 10 years. With more experience, surgeons spent more of their day in the ambulatory setting and placing orders in the visit navigator, although they became quicker overall on a per-appointment basis. Surgeons also had significantly higher efficiency and proficiency metrics after 10 years of experience. Although not significant, this may be reflected in the overall decrease in burden metrics which is strongest at 10 years. These findings indicate that while the overall workload may increase, physicians are learning to become even more efficient to maintain a balanced lifestyle. We were unable to control for presence of physician extenders who may be helping with documentation and easing the burden for more senior surgeons.
Limitations of our study include the duration of data collection for the study. With only 5 months of data, our study may not fully capture the overall experience that surgeons actually have with the EHR. There are also limitations regarding the metrics that we have used to represent volume, efficiency, proficiency, and burden. Appointments per day did not consider whether a physician had a full or half day at the clinic. Because scheduled time was determined by Epic Signal, we could not stratify our analysis based on scheduled time for clinic versus scheduled time for operating room or administrative tasks. Time per day spent in the EHR may not be representative of actual time because Epic Signal incorporates a 5-second time-out rule where the clock stops measuring if no cursor activity is detected after 5 seconds. Caution should be used when considering our metrics for efficiency and proficiency, as Epic Signal definitions for “Proficiency Score” has not been extensively used in other studies. Moreover, interpreting and validating proficiency scores generated by vendors can be challenging. Without universally accepted methodology for measuring proficiency, Epic's algorithm may not factor in various other time-saving methods implemented by physicians. The current state of research does not provide clear guidance on the practicality of using proficiency scores. Additionally, we used time spent working outside of normal working hours as a proxy for burden, but there are many other contributors to burden, such as lack of autonomy, lack of staff, and personal characteristics. The gold standard for burnout is the quantitative burnout survey, which we did not include in our study. Additionally, we did not have any other EHR usage metrics in our system other than Signal to compare and validate our findings.
In summary, our study has important implications for using EHR-generated reports to quantify EHR use by general surgery and subspecialty surgical attendings as impacted by individual characteristics. By classifying proficiency and efficiency and defining the amount of time that surgeons spent on the EHR during and after workhours, we hope to raise awareness of the burden placed by the EHR and highlight certain groups of surgeons who may experience a disproportionate amount of documentation burden. Globally, health systems face challenges in improving productivity while reducing surgeon burnout. Our data demonstrate that EHR-provided data can be used to assess volume, productivity, and surrogate markers of burnout with the goal of improving both. These data can then provide the evidence for funding and implementation of programs and interventions to help decrease the electronic documentation load. Further studies are warranted to further validate this method to identify areas of interest and improvement.
Conclusion
Surgeons spend a considerable amount of time using the EHR. Studying the data collected through EHRs can be used to understand how its use is linked to burnout and job satisfaction, and to develop programs that can improve efficiency and reduce the workload associated with documentation. It is particularly important to investigate the experiences of surgeons who may be less proficient, efficient, and experience more documentation burden, as this can help to identify any obstacles that may be hindering their efficient use of the EHR.
Clinical Relevance Statement
The findings of this research add to the growing body of evidence that EHR-generated reports can serve as indicators of EHR workload that are influenced by both external factors and specific traits of physicians. By identifying the elements that affect surgeon engagement and usage, health care organizations can effectively track the effects of organizational changes and improve productivity while reducing surgeon fatigue. Additional research is needed to explore the relationship between these metrics and how to effectively utilize them.
Multiple Choice Questions
-
Which of the following has been found in the literature to be correlated with increased physician burnout?
-
a) Increased levels of autonomy
-
b) Improved support and funding in the work environment
-
c) Male physicians
-
d) More time spent outside work hours on documentation
Correct answer: The correct answer is option (d). Studies have found that more time spent outside work hours on documentation, lack of autonomy, and reduced support and funding are correlated with increased reported levels of physician burnout. There are also several studies that report that female physicians have increased rates of burnout.
-
-
Which of the following surgeon characteristics was associated with significantly more time spent in the electronic health record outside of 7 a.m. to 7 p.m. on multivariate analysis?
-
a) Having greater than 10 years of experience
-
b) Being an associate professor
-
c) Being a female
-
d) Being from a pediatric specialty
Correct answer: The correct answer is option (c). Results in our multivariate analysis demonstrated that female gender was an independent predictor of spending more time in notes per appointment and time outside of 7 a.m. to 7 p.m. in the EHR.
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Conflict of Interest
None declared.
Acknowledgments
We would like to thank Dr. Ferdinand J. Chan for helping with acquisition of data.
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.
All subjects were de-identified prior to conducting this study.
Authors' Contributions
All authors made substantial contributions to conception and design, acquisition of data and/or analysis/interpretation of data, and drafting and critical revision of the article for important intellectual content. All listed authors approved the version to be published.
* These authors contributed equally to this work.
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References
- 1 American Recovery and Reinvestment Act of 2009. 111th Congress (2009–2010). Accessed January 3, 2023 at: https://www.congress.gov/bill/111th-congress/house-bill/1
- 2 Hecht J. The future of electronic health records. Nature 2019; 573 (7775) S114-S116
- 3 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
- 4 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 (04) 479-484
- 5 Downing NL, Bates DW, Longhurst CA. Physician burnout in the electronic health record era: are we ignoring the real cause?. Ann Intern Med 2018; 169 (01) 50-51
- 6 Collier R. Electronic health records contributing to physician burnout. CMAJ 2017; 189 (45) E1405-E1406
- 7 Seu M, Cho BH, Pigott R. et al. Trends and perceptions of electronic health record usage among plastic surgeons. Plast Reconstr Surg Glob Open 2020; 8 (04) e2709
- 8 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
- 9 Moy AJ, Hobensack M, Marshall K. et al. Understanding the perceived role of electronic health records and workflow fragmentation on clinician documentation burden in emergency departments. J Am Med Inform Assoc 2023; 30 (05) 797-808
- 10 Budd J. Burnout related to electronic health record use in primary care. J Prim Care Community Health 2023 ;14:21501319231166921
- 11 Tan HJ, Chung AE, Gotz D. et al. Electronic health record use and perceptions among urologic surgeons. Appl Clin Inform 2023; 14 (02) 279-289
- 12 Aziz F, Talhelm L, Keefer J, Krawiec C. Vascular surgery residents spend one fifth of their time on electronic health records after duty hours. J Vasc Surg 2019; 69 (05) 1574-1579
- 13 Kesler K, Wynn M, Pugely AJ. Time and clerical burden posed by the current electronic health record for orthopaedic surgeons. J Am Acad Orthop Surg 2022; 30 (01) e34-e43
- 14 Read-Brown S, Hribar MR, Reznick LG. et al. Time requirements for electronic health record use in an academic ophthalmology center. JAMA Ophthalmol 2017; 135 (11) 1250-1257
- 15 Cox ML, Risoli Jr T, Peskoe SB, Turner DA, Migaly J. Quantified electronic health record (EHR) use by academic surgeons. Surgery 2021; 169 (06) 1386-1392
- 16 Gupta K, Murray SG, Sarkar U. et al. Differences in ambulatory EHR use patterns for male vs. female physicians. NEJM Catal 2019 ;5(6)
- 17 Beiser M, Lu V, Paul S. et al. Electronic health record usage patterns: assessing telemedicine's impact on the provider experience during the COVID-19 pandemic. Telemed J E Health 2021; 27 (08) 934-938
- 18 Jeppson K. What can Epic's signal data tell us about EHR satisfaction and burnout. KLAS Impact Report; 2020 . Accessed December 26, 2022 at: https://klasresearch.com/archcollaborative/report/what-can-epics-signal-data-tell-us-about-ehr-satisfaction-and-burnout/324
- 19 Ruan E, Beiser M, Lu V. et al. Physician electronic health record usage as affected by the COVID-19 pandemic. Appl Clin Inform 2022; 13 (04) 785-793
- 20 Dyrbye LN, Shanafelt TD, Balch CM, Satele D, Sloan J, Freischlag J. Relationship between work-home conflicts and burnout among American surgeons: a comparison by sex. Arch Surg 2011; 146 (02) 211-217
- 21 Lu PW, Columbus AB, Fields AC, Melnitchouk N, Cho NL. Gender differences in surgeon burnout and barriers to career satisfaction: a qualitative exploration. J Surg Res 2020; 247: 28-33
- 22 Dahlke AR, Johnson JK, Greenberg CC. et al. Gender differences in utilization of duty-hour regulations, aspects of burnout, and psychological well-being among general surgery residents in the United States. Ann Surg 2018; 268 (02) 204-211
- 23 Mete M, Dickman J, Rowe S. et al. Beyond burnout: understanding the well-being gender gap in general surgery by examining professional fulfillment and control over schedule. Am J Surg 2022; 223 (04) 609-614
- 24 Galaiya R, Kinross J, Arulampalam T. Factors associated with burnout syndrome in surgeons: a systematic review. Ann R Coll Surg Engl 2020; 102 (06) 401-407
- 25 Hu YY, Ellis RJ, Hewitt DB. et al. Discrimination, abuse, harassment, and burnout in surgical residency training. N Engl J Med 2019; 381 (18) 1741-1752
- 26 Benjamin T, Gulati A, Zebolsky AL. et al. Assessing the prevalence of burnout among female microvascular head and neck surgeons. Facial Plast Surg Aesthet Med 2023; 25 (04) 298-303
Address for correspondence
Publikationsverlauf
Eingereicht: 07. August 2023
Angenommen: 17. Oktober 2023
Accepted Manuscript online:
18. Oktober 2023
Artikel online veröffentlicht:
10. Januar 2024
© 2024. Thieme. All rights reserved.
Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany
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References
- 1 American Recovery and Reinvestment Act of 2009. 111th Congress (2009–2010). Accessed January 3, 2023 at: https://www.congress.gov/bill/111th-congress/house-bill/1
- 2 Hecht J. The future of electronic health records. Nature 2019; 573 (7775) S114-S116
- 3 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
- 4 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 (04) 479-484
- 5 Downing NL, Bates DW, Longhurst CA. Physician burnout in the electronic health record era: are we ignoring the real cause?. Ann Intern Med 2018; 169 (01) 50-51
- 6 Collier R. Electronic health records contributing to physician burnout. CMAJ 2017; 189 (45) E1405-E1406
- 7 Seu M, Cho BH, Pigott R. et al. Trends and perceptions of electronic health record usage among plastic surgeons. Plast Reconstr Surg Glob Open 2020; 8 (04) e2709
- 8 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
- 9 Moy AJ, Hobensack M, Marshall K. et al. Understanding the perceived role of electronic health records and workflow fragmentation on clinician documentation burden in emergency departments. J Am Med Inform Assoc 2023; 30 (05) 797-808
- 10 Budd J. Burnout related to electronic health record use in primary care. J Prim Care Community Health 2023 ;14:21501319231166921
- 11 Tan HJ, Chung AE, Gotz D. et al. Electronic health record use and perceptions among urologic surgeons. Appl Clin Inform 2023; 14 (02) 279-289
- 12 Aziz F, Talhelm L, Keefer J, Krawiec C. Vascular surgery residents spend one fifth of their time on electronic health records after duty hours. J Vasc Surg 2019; 69 (05) 1574-1579
- 13 Kesler K, Wynn M, Pugely AJ. Time and clerical burden posed by the current electronic health record for orthopaedic surgeons. J Am Acad Orthop Surg 2022; 30 (01) e34-e43
- 14 Read-Brown S, Hribar MR, Reznick LG. et al. Time requirements for electronic health record use in an academic ophthalmology center. JAMA Ophthalmol 2017; 135 (11) 1250-1257
- 15 Cox ML, Risoli Jr T, Peskoe SB, Turner DA, Migaly J. Quantified electronic health record (EHR) use by academic surgeons. Surgery 2021; 169 (06) 1386-1392
- 16 Gupta K, Murray SG, Sarkar U. et al. Differences in ambulatory EHR use patterns for male vs. female physicians. NEJM Catal 2019 ;5(6)
- 17 Beiser M, Lu V, Paul S. et al. Electronic health record usage patterns: assessing telemedicine's impact on the provider experience during the COVID-19 pandemic. Telemed J E Health 2021; 27 (08) 934-938
- 18 Jeppson K. What can Epic's signal data tell us about EHR satisfaction and burnout. KLAS Impact Report; 2020 . Accessed December 26, 2022 at: https://klasresearch.com/archcollaborative/report/what-can-epics-signal-data-tell-us-about-ehr-satisfaction-and-burnout/324
- 19 Ruan E, Beiser M, Lu V. et al. Physician electronic health record usage as affected by the COVID-19 pandemic. Appl Clin Inform 2022; 13 (04) 785-793
- 20 Dyrbye LN, Shanafelt TD, Balch CM, Satele D, Sloan J, Freischlag J. Relationship between work-home conflicts and burnout among American surgeons: a comparison by sex. Arch Surg 2011; 146 (02) 211-217
- 21 Lu PW, Columbus AB, Fields AC, Melnitchouk N, Cho NL. Gender differences in surgeon burnout and barriers to career satisfaction: a qualitative exploration. J Surg Res 2020; 247: 28-33
- 22 Dahlke AR, Johnson JK, Greenberg CC. et al. Gender differences in utilization of duty-hour regulations, aspects of burnout, and psychological well-being among general surgery residents in the United States. Ann Surg 2018; 268 (02) 204-211
- 23 Mete M, Dickman J, Rowe S. et al. Beyond burnout: understanding the well-being gender gap in general surgery by examining professional fulfillment and control over schedule. Am J Surg 2022; 223 (04) 609-614
- 24 Galaiya R, Kinross J, Arulampalam T. Factors associated with burnout syndrome in surgeons: a systematic review. Ann R Coll Surg Engl 2020; 102 (06) 401-407
- 25 Hu YY, Ellis RJ, Hewitt DB. et al. Discrimination, abuse, harassment, and burnout in surgical residency training. N Engl J Med 2019; 381 (18) 1741-1752
- 26 Benjamin T, Gulati A, Zebolsky AL. et al. Assessing the prevalence of burnout among female microvascular head and neck surgeons. Facial Plast Surg Aesthet Med 2023; 25 (04) 298-303