Electronic Health Record Usage Patterns Across Surgical Subspecialties
Open accessAbstract
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.
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- Findingown data
Having 6 to 10 years of experience was an independent predictor of receiving more messages per day.
“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).”
Quote in the paperNot a substantial claim- Rests on an assumption: Epic Signal metrics accurately reflect clinicians' EHR usage.
- Rests on an assumption: Years of experience determined from the year of graduation from last residency or fellowship is a valid measure of years practicing as an attending.
- Findingown data
Overall, surgeons spent 57.5 minutes per day logged into 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.”
Quote in the paperNot a substantial claim- Rests on an assumption: Epic Signal metrics accurately reflect clinicians' EHR usage.
- Findingown data
Surgeons with 0 to 5 years of experience had significantly lower EHR efficiency than surgeons with 6 to 10 and >10 years of experience.
“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.”
- Also stated in: § Years of Experience · plain
“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.”
Quote in the paperSubstantial claim- Rests on an assumption: Epic Signal metrics accurately reflect clinicians' EHR usage.
- Rests on an assumption: Years of experience determined from the year of graduation from last residency or fellowship is a valid measure of years practicing as an attending.
- Also stated in: § Years of Experience · plain
- Findingown data
There were no significant differences in EHR metrics of specialty or workload outside scheduled time across years of experience.
“There were no significant differences in the EHR metrics of specialty or workload outside scheduled time across years of experience (►Table 3).”
Quote in the paperNot a substantial claim- The authors state this as an assumption: Time spent in the EHR outside working hours is a valid proxy for documentation burden.
- Rests on an assumption: Epic Signal metrics accurately reflect clinicians' EHR usage.
- Rests on an assumption: Years of experience determined from the year of graduation from last residency or fellowship is a valid measure of years practicing as an attending.
- Findingown data
All surgeons spent a median of 2.4 minutes in Notes per appointment.
“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.”
Quote in the paperNot a substantial claim- Rests on an assumption: Epic Signal metrics accurately reflect clinicians' EHR usage.
- Findingown data
Female surgeons had significantly higher EHR documentation burden than male surgeons.
“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.”
- Also stated in: § Gender · plain
“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.”
Quote in the paperSubstantial claim- The authors state this as an assumption: Time spent in the EHR outside working hours is a valid proxy for documentation burden.
- Rests on an assumption: Epic Signal metrics accurately reflect clinicians' EHR usage.
- Also stated in: § Gender · plain
- Findingown data
Surgeons with 0 to 5 years of experience had significantly lower EHR volume than surgeons with 6 to 10 and >10 years of experience.
“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.”
- Also stated in: § Years of Experience · plain
“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.”
Quote in the paperSubstantial claim- Rests on an assumption: Epic Signal metrics accurately reflect clinicians' EHR usage.
- Rests on an assumption: Years of experience determined from the year of graduation from last residency or fellowship is a valid measure of years practicing as an attending.
- Also stated in: § Years of Experience · plain
- Findingown data
All surgeons spent 0.4 minute per completed message.
“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.”
Quote in the paperNot a substantial claim- Rests on an assumption: Epic Signal metrics accurately reflect clinicians' EHR usage.
- Findingown data
All surgeons closed 81.5% of appointments the same day.
“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.”
Quote in the paperNot a substantial claim- Rests on an assumption: Epic Signal metrics accurately reflect clinicians' EHR usage.
- Findingown data
Having >10 years of experience was an independent predictor of spending less time per completed message.
“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.”
- Also stated in: § Multivariate Analysis · plain
“spending less time per completed message (OR À15.26, 95% CI À25.49, À5.04)”
Quote in the paperSubstantial claim- Rests on an assumption: Epic Signal metrics accurately reflect clinicians' EHR usage.
- Rests on an assumption: Years of experience determined from the year of graduation from last residency or fellowship is a valid measure of years practicing as an attending.
- Also stated in: § Multivariate Analysis · plain
- Findingown data
Female surgeons spent more time outside work hours than male surgeons.
“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.”
Quote in the paperSubstantial claim- The authors state this as an assumption: Time spent in the EHR outside working hours is a valid proxy for documentation burden.
- Rests on an assumption: Epic Signal metrics accurately reflect clinicians' EHR usage.
- Findingown data
There were no significant differences in EHR metrics of volume or proficiency between genders.
“There were no significant differences in the EHR metrics of volume or proficiency between genders (►Table 2).”
Quote in the paperNot a substantial claim- Rests on an assumption: Epic Signal metrics accurately reflect clinicians' EHR usage.
- Findingown data
Female surgeons had significantly higher EHR time per day than male surgeons.
“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.”
- Also stated in: § Gender · plain
“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.”
Quote in the paperSubstantial claim- Rests on an assumption: Epic Signal metrics accurately reflect clinicians' EHR usage.
- Also stated in: § Gender · plain
- Findingown data
Overall, surgeons spent 19.1 minutes per day in the EHR outside scheduled hours.
“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.”
Quote in the paperNot a substantial claim- The authors state this as an assumption: Time spent in the EHR outside working hours is a valid proxy for documentation burden.
- Rests on an assumption: Epic Signal metrics accurately reflect clinicians' EHR usage.
- Findingown data
All surgeons had a median of 18.6 appointments per day.
“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.”
Quote in the paperNot a substantial claim- Rests on an assumption: Epic Signal metrics accurately reflect clinicians' EHR usage.
- Findingown data
Surgeons with 0 to 5 years of experience had significantly lower EHR time per day than surgeons with 6 to 10 and >10 years of experience.
“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.”
- Also stated in: § Years of Experience · plain
“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.”
Quote in the paperSubstantial claim- Rests on an assumption: Epic Signal metrics accurately reflect clinicians' EHR usage.
- Rests on an assumption: Years of experience determined from the year of graduation from last residency or fellowship is a valid measure of years practicing as an attending.
- Also stated in: § Years of Experience · plain
- Findingown data
Pediatric specialty was an independent predictor of spending more time per completed message.
“Pediatric specialty was an independent predictor of fewer scheduled hours per day (OR À1.69, 95% CI À3.12, À0.26) and spending more time per completed message (OR 11.52, 95% CI 0.75, 22.29).”
Quote in the paperSubstantial claim- Rests on an assumption: Epic Signal metrics accurately reflect clinicians' EHR usage.
- Findingown data
Female surgeons had similar patient volume as their male surgeon counterparts.
“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.”
Quote in the paperSubstantial claim- Rests on an assumption: Epic Signal metrics accurately reflect clinicians' EHR usage.
- Findingown data
Pediatric specialty was an independent predictor of fewer scheduled hours per day.
“Pediatric specialty was an independent predictor of fewer scheduled hours per day (OR À1.69, 95% CI À3.12, À0.26) and spending more time per completed message (OR 11.52, 95% CI 0.75, 22.29).”
Quote in the paperSubstantial claim- Rests on an assumption: Epic Signal metrics accurately reflect clinicians' EHR usage.
- Findingown data
Female surgeons had significantly lower EHR efficiency than male surgeons.
“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.”
- Also stated in: § Gender · plain
“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.”
Quote in the paperSubstantial claim- Rests on an assumption: Epic Signal metrics accurately reflect clinicians' EHR usage.
- Also stated in: § Gender · plain
- Findingown data
Having >10 years of experience was an independent predictor of greater proficiency.
“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.”
- Also stated in: § Multivariate Analysis · plain
“greater proficiency (OR 1.53, 95% CI 0.43, 2.63)”
Quote in the paperSubstantial claim- Rests on an assumption: Epic Signal metrics accurately reflect clinicians' EHR usage.
- Rests on an assumption: The Epic Signal Proficiency Score validly measures EHR proficiency.
- Rests on an assumption: Years of experience determined from the year of graduation from last residency or fellowship is a valid measure of years practicing as an attending.
- Also stated in: § Multivariate Analysis · plain
- Findingown data
All surgeons spent a median of 19.4 minutes on unscheduled days in the EHR.
“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).”
- Also stated in: § Discussion · plain
“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.”
Quote in the paperNot a substantial claim- The authors state this as an assumption: Time spent in the EHR outside working hours is a valid proxy for documentation burden.
- Rests on an assumption: Epic Signal metrics accurately reflect clinicians' EHR usage.
- Also stated in: § Discussion · plain
- Findingown data
Having >10 years of experience was an independent predictor of more appointments per day.
“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.”
- Also stated in: § Multivariate Analysis · plain
“>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)”
Quote in the paperSubstantial claim- Rests on an assumption: Epic Signal metrics accurately reflect clinicians' EHR usage.
- Rests on an assumption: Years of experience determined from the year of graduation from last residency or fellowship is a valid measure of years practicing as an attending.
- Also stated in: § Multivariate Analysis · plain
- Findingown data
Surgeons with 0 to 5 years of experience had significantly lower EHR proficiency than surgeons with 6 to 10 and >10 years of experience.
“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.”
- Also stated in: § Years of Experience · plain
“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.”
Quote in the paperSubstantial claim- Rests on an assumption: Epic Signal metrics accurately reflect clinicians' EHR usage.
- Rests on an assumption: The Epic Signal Proficiency Score validly measures EHR proficiency.
- Rests on an assumption: Years of experience determined from the year of graduation from last residency or fellowship is a valid measure of years practicing as an attending.
- Also stated in: § Years of Experience · plain
- Findingown data
Female surgeons spent just over 1 minute longer writing notes per appointment than male surgeons.
“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.”
Quote in the paperSubstantial claim- Rests on an assumption: Epic Signal metrics accurately reflect clinicians' EHR usage.
- Findingown data
Female gender was an independent predictor of spending more time in notes per appointment.
“Female gender was an independent predictor of spending more time in notes per appointment and time spent in the EHR outside working hours.”
- Also stated in: § Multivariate Analysis · plain
“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).”
Quote in the paperSubstantial claim- Rests on an assumption: Epic Signal metrics accurately reflect clinicians' EHR usage.
- Also stated in: § Multivariate Analysis · plain
- Findingown data
Female surgeons had significantly higher EHR time per appointment than male surgeons.
“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.”
- Also stated in: § Gender · plain
“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.”
Quote in the paperSubstantial claim- Rests on an assumption: Epic Signal metrics accurately reflect clinicians' EHR usage.
- Also stated in: § Gender · plain
- Findingown data
Female gender was an independent predictor of spending more time in the EHR outside of 7 a.m. to 7 p.m.
“Female gender was an independent predictor of spending more time in notes per appointment and time spent in the EHR outside working hours.”
- Also stated in: § Multivariate Analysis · plain
“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).”
Quote in the paperSubstantial claim- The authors state this as an assumption: Time spent in the EHR outside working hours is a valid proxy for documentation burden.
- Rests on an assumption: Epic Signal metrics accurately reflect clinicians' EHR usage.
- Also stated in: § Multivariate Analysis · plain
- Findingown data
In multivariate analysis, having >10 years as attending was associated with a 1.53 higher proficiency score (95% CI 0.43 to 2.63, p=0.007).
“| >10 years as attending | 1.27 (0.34, 2.21) | 0.008 | 1.53 (0.43, 2.63) | 0.007 |”
Quote in the paperNot a substantial claim - Findingown data
Female surgeons had significantly more time in notes per appointment than male surgeons (median 3.2 vs 2.1 min, p=0.009).
“| Time in notes (min) | 2.4 (1.6-4.1) | 2.1 (1.4-3.7) | 3.2 (2.0-4.8) | 0.009 |”
Quote in the paperNot a substantial claim - Findingown data
In multivariate analysis, pediatric specialty was associated with 11.52 more minutes per completed message (95% CI 0.75 to 22.29, p=0.036).
“| Pediatric specialty | 11.43 (0.43, 22.44) | 0.042 | 11.52 (0.75, 22.29) | 0.036 |”
Quote in the paperNot a substantial claim - Findingown data
In multivariate analysis, having >10 years as attending was associated with 1.74 more scheduled hours per day (95% CI 0.38 to 3.10, p=0.013).
“| >10 years as attending | 1.38 (0.20, 2.56) | 0.022 | 1.74 (0.38, 3.10) | 0.013 |”
Quote in the paperNot a substantial claim - Findingown data
Among surgeons, those with 0 to 5 years of experience spent significantly less time in orders per day than those with 6-10 or >10 years (median 3.4 vs 7.6 vs 8.9 min, p=0.034).
“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 [3.4-15.0min], p ¼ 0.034)”
Quote in the paperSubstantial claim - Findingown data
In multivariate analysis, pediatric specialty was associated with 1.69 fewer scheduled hours per day (95% CI -3.12 to -0.26, p=0.021).
“| Pediatric specialty | À1.89 (À3.31, À0.48) | 0.009 | À1.69 (À3.12, À0.26) | 0.021 |”
Quote in the paperNot a substantial claim - Findingown data
Female surgeons had significantly more time in orders per appointment than male surgeons (median 1.2 vs 0.7 min, p=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 |”
Quote in the paperNot a substantial claim - Findingown data
Among surgeons, those with 0 to 5 years of experience had a significantly lower proficiency score than those with 6-10 or >10 years (median 2.9 vs 3.3 vs 5.2, p=0.035).
“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 ¼ 0.035)”
Quote in the paperSubstantial claim - Findingown data
In multivariate analysis, having 6 to 10 years as attending was associated with 4.50 more messages per day (95% CI 0.51 to 8.49, p=0.027).
“| 6 to 10 years as attending | 4.67 (1.10, 8.23) | 0.011 | 4.50 (0.51, 8.49) | 0.027 |”
Quote in the paperNot a substantial claim - Findingown data
Surgeons with 0 to 5 years of experience had a significantly lower percentage of time in system: ambulatory than those with 6-10 or >10 years (median 53.1% vs 62.7% vs 63.1%, p<0.001).
“| Percentage of time in | 57.3 (41.2-66.6) | 53.1 (25.6-58.9) | 62.7 (50.4-67.4) | 63.1 (46.7-70.2) | <0.001 |”
Quote in the paperSubstantial claim - Findingown data
In multivariate analysis, associate professor rank was associated with 5.32 fewer minutes outside 7 a.m. to 7 p.m. (95% CI -10.21 to -0.42, p=0.034).
“| Associate professor | À5.40 (À9.78, À1.02) | 0.016 | À5.32 (À10.21, À0.42) | 0.034 |”
Quote in the paperNot a substantial claim - Findingown data
Among surgeons, those with 0 to 5 years of experience had significantly fewer aggregate messages per day than those with 6-10 or >10 years (median 8.9 vs 14.5 vs 11.3, p=0.015).
“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 ¼ 0.015)”
Quote in the paperSubstantial claim - Findingown data
Surgeons with 0 to 5 years of experience had a significantly higher percentage of time in system: inpatient than those with 6-10 or >10 years (median 14.5% vs 8.3% vs 9.3%, p=0.008).
“| Percentage of time in | 11.9 (5.9-21.8) | 14.5 (10.3-28.3) | 8.3 (5.5-17.3) | 9.3 (4.7-18.9) | 0.008 |”
Quote in the paperNot a substantial claim - Findingown data
Among surgeons, those with 0 to 5 years of experience had significantly fewer scheduled hours per day than those with 6-10 or >10 years (median 4.4 vs 6.0 vs 5.9, p=0.041).
“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 ¼ 0.041)”
Quote in the paperNot a substantial claim - Findingown data
Among surgeons, those with 0 to 5 years of experience had significantly fewer appointments per day than those with 6-10 or >10 years (median 14.7 vs 19.7 vs 19.3, p=0.017).
“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 ¼ 0.017)”
Quote in the paperSubstantial claim - Findingown data
Among surgeons, those with 0 to 5 years of experience spent significantly less time in visitor navigator per day than those with 6-10 or >10 years (median 2.8 vs 4.5 vs 4.3 min, p=0.023).
“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 ¼ 0.023)”
Quote in the paperSubstantial claim - Findingown data
In multivariate analysis, female sex was associated with 4.75 more minutes outside 7 a.m. to 7 p.m. (95% CI 0.47 to 9.04, p=0.030).
“| Female | 4.05 (À0.43, 8.52) | 0.076 | 4.75 (0.47, 9.04) | 0.030 |”
Quote in the paperNot a substantial claim - Findingown data
In multivariate analysis, having >10 years as attending was associated with 15.26 fewer minutes per completed message (95% CI -25.49 to -5.04, p=0.004).
“| >10 years as attending | À15.69 (À24.57, À6.81) | <0.001 | À15.26 (À25.49, À5.04) | 0.004 |”
Quote in the paperSubstantial claim - Findingown data
Surgeons with 0 to 5 years of experience had significantly more time in clinical review per appointment than those with 6-10 or >10 years (median 1.9 vs 1.4 vs 1.1 min, p=0.005).
“| Time in clinical review (min) | 1.4 (0.7-2.3) | 1.9 (1.1-3.0) | 1.4 (0.9-2.1) | 1.1 (0.6-1.8) | 0.005 |”
Quote in the paperSubstantial claim - Findingown data
Among surgeons, those with 0 to 5 years of experience spent significantly more time per note than those with 6-10 or >10 years (median 2.4 vs 1.4 vs 1.8 min, p=0.016).
“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 ¼ 0.016)”
Quote in the paperSubstantial claim - Findingown data
Female surgeons had significantly more time per completed message than male surgeons (median 0.5 vs 0.4 min, p=0.045).
“| 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 |”
Quote in the paperNot a substantial claim - Findingown data
Female surgeons had significantly more time in in-basket per appointment than male surgeons (median 0.7 vs 0.4 min, p=0.01).
“| 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 |”
Quote in the paperNot a substantial claim - Findingown data
Female surgeons had significantly more time in in-basket per day than male surgeons (median 4.5 vs 3.4 min, p=0.016).
“| 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 |”
Quote in the paperNot a substantial claim - Findingown data
In multivariate analysis, having >10 years as attending was associated with 7.96 more appointments per day (95% CI 2.30 to 13.62, p=0.006).
“| >10 years as attending | 6.18 (1.42, 10.94) | 0.011 | 7.96 (2.30, 13.62) | 0.006 |”
Quote in the paperNot a substantial claim - Findingown data
In multivariate analysis, female sex was associated with 1.07 more minutes in notes per appointment (95% CI 0.18 to 1.95, p=0.018).
“| Female | 1.16 (0.31, 2.01) | 0.008 | 1.07 (0.18, 1.95) | 0.018 |”
Quote in the paperNot a substantial claim - Findingown data
Among surgeons, those with 0 to 5 years of experience spent significantly more time per completed message than those with 6-10 or >10 years (median 0.6 vs 0.4 vs 0.3 min, p=0.002).
“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 ¼ 0.002)”
Quote in the paperSubstantial claim - Interpretationreasoning
Burnout in our study may be more prevalent among female surgeons.
“Given that documentation burden is a contributor to burnout, burnout in our study may be more prevalent among female surgeons”
Quote in the paperSubstantial claim- The authors state this as an assumption: Time spent in the EHR outside working hours is a valid proxy for documentation burden.
- Rests on an assumption: Epic Signal metrics accurately reflect clinicians' EHR usage.
- Study design and conductreasoning
Time per day spent in the EHR may not be representative of actual time because Epic Signal incorporates a 5-second time-out rule.
“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.”
Quote in the paperNot a substantial claim - Study design and conductown data
The 5-month data collection period may not fully capture the overall experience that surgeons actually have with the EHR.
“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.”
Quote in the paperNot a substantial claim - Study design and conductnone given
The study was unable to control for the presence of physician extenders who may be helping with documentation.
“We were unable to control for presence of physician extenders who may be helping with documentation and easing the burden for more senior surgeons.”
Quote in the paperNot a substantial claim - Interpretationown data
Female surgeon burnout may also be attributed to spending more time in the EHR.
“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.”
Quote in the paperSubstantial claim- The authors state this as an assumption: Time spent in the EHR outside working hours is a valid proxy for documentation burden.
- Rests on an assumption: Epic Signal metrics accurately reflect clinicians' EHR usage.
- Interpretationown data
EHR-generated reports can serve as indicators of EHR workload that are influenced by both external factors and specific traits of physicians.
“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.”
Quote in the paperSubstantial claim- Rests on an assumption: Epic Signal metrics accurately reflect clinicians' EHR usage.
- Interpretationown data
Surgeons spend a considerable amount of time using the EHR.
“Surgeons spend a considerable amount of time using the EHR.”
Quote in the paperSubstantial claim- Rests on an assumption: Epic Signal metrics accurately reflect clinicians' EHR usage.
- Interpretationown data
Our data demonstrate that EHR-provided data can be used to assess volume, productivity, and surrogate markers of 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.”
Quote in the paperSubstantial claim- The authors state this as an assumption: Time spent in the EHR outside working hours is a valid proxy for documentation burden.
- Rests on an assumption: Epic Signal metrics accurately reflect clinicians' EHR usage.
- Interpretationown data
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.
“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.”
Quote in the paperSubstantial claim- The authors state this as an assumption: Time spent in the EHR outside working hours is a valid proxy for documentation burden.
- Rests on an assumption: Epic Signal metrics accurately reflect clinicians' EHR usage.
- Rests on an assumption: The Epic Signal Proficiency Score validly measures EHR proficiency.
- Rests on an assumption: Years of experience determined from the year of graduation from last residency or fellowship is a valid measure of years practicing as an attending.
- Premisecitation
Documentation burden is a contributor to burnout.
“Given that documentation burden is a contributor to burnout, burnout in our study may be more prevalent among female surgeons”
- Also stated in: § Background and Significance · plain
“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]”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [3][4][5]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Also stated in: § Background and Significance · plain
- Premisecitation
Few studies have described the time spent on the EHR in individual surgical specialties, such as general surgery, vascular surgery, orthopaedic surgery, and ophthalmology.
“Few studies have described the time spent on the EHR in individual surgical specialties, such as general surgery, vascular surgery, orthopaedic surgery, and ophthalmology.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [11][12][13]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Premisecitation
Across general surgery and the surgical subspecialties, quantitative evidence demonstrating the struggles of EHR documentation is lacking.
“Across general surgery and the surgical subspecialities, quantitative evidence demonstrating the struggles of EHR documentation is lacking.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [11][12][13]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Premisecitation
Several recent studies have associated EHR use with an increase in clinician frustration and burnout.
“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]”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [3][4][5]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Premisecitation
None have broadly analyzed all surgical fields and attempted to describe predictors of time spent on EHR.
“Despite this, none have broadly analyzed all surgical fields and attempted to describe predictors of time spent on EHR.[11][12][13][14][15]”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [11][12][13]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- The quote only partly states this claim. 5
- Classified as probably not a substantial claim. 9
- May combine two claims. 6
The quote only partly states this claim.
Findingown dataAll surgeons had a median pajama time of 10.4 minutes.
“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).”
Quote in the paperNot a substantial claim- The authors state this as an assumption: Time spent in the EHR outside working hours is a valid proxy for documentation burden.
- Rests on an assumption: Epic Signal metrics accurately reflect clinicians' EHR usage.
The quote only partly states this claim.
Findingown dataHaving >10 years of experience was an independent predictor of more scheduled hours per day.
“>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)”
Quote in the paperNot a substantial claim- Rests on an assumption: Epic Signal metrics accurately reflect clinicians' EHR usage.
- Rests on an assumption: Years of experience determined from the year of graduation from last residency or fellowship is a valid measure of years practicing as an attending.
The quote only partly states this claim.
Findingown dataSurgeons with 0 to 5 years of experience had a significantly higher percentage of time in system: ED than those with 6-10 or >10 years (median 0.6% vs 0.1% vs 0.1%, p=0.001).
“| Percentage of time in system: ED | 0.2 (0.0-1.0) | 0.6 (0.1-1.7) | 0.1 (0.0-0.5) | 0.1 (0.0-0.8) | 0.001 |”
Quote in the paperNot a substantial claimThe quote only partly states this claim.
Findingown dataFemale surgeons had significantly more time on unscheduled days than male surgeons (median 23.4 vs 17.2 min, p=0.023).
“| 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 |”
Quote in the paperNot a substantial claimThe quote only partly states this claim.
Findingown dataSurgeons with 0 to 5 years of experience had significantly more time in in-basket per appointment than those with 6-10 or >10 years (median 0.7 vs 0.6 vs 0.4 min, p=0.008).
“| Time in in-basket (min) | 0.5 (0.2-1.0) | 0.7 (0.4-1.5) | 0.6 (0.3-1.1) | 0.4 (0.1-0.7) | 0.008 |”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Study design and conductnone givenThe gold standard for burnout is the quantitative burnout survey, which was not included in the study.
“The gold standard for burnout is the quantitative burnout survey, which we did not include in our study.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable). May combine two claims.
Interpretationown dataStudying 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.
“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.”
Quote in the paperNot a substantial claim- The authors state this as an assumption: Time spent in the EHR outside working hours is a valid proxy for documentation burden.
- Rests on an assumption: Epic Signal metrics accurately reflect clinicians' EHR usage.
Probably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductown dataThe study did not have any other EHR usage metrics in the system other than Signal to compare and validate the findings.
“Additionally, we did not have any other EHR usage metrics in our system other than Signal to compare and validate our findings.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).
Study design and conductreasoningWithout universally accepted methodology for measuring proficiency, Epic's algorithm may not factor in various other time-saving methods implemented by physicians.
“Without universally accepted methodology for measuring proficiency, Epic's algorithm may not factor in various other time-saving methods implemented by physicians.”
Quote in the paperNot a substantial claim- Rests on an assumption: The Epic Signal Proficiency Score validly measures EHR proficiency.
Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).
Study design and conductreasoningCaution should be used when considering the metrics for efficiency and proficiency, as Epic Signal definitions for Proficiency Score have not been extensively used in other studies.
“Caution should be used when considering our metrics for efficiency and proficiency, as Epic Signal definitions for”
Quote in the paperNot a substantial claim- Rests on an assumption: The Epic Signal Proficiency Score validly measures EHR proficiency.
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationSince the American Recovery and Reinvestment Act, physicians' use of EHR has risen from 9% in 2008 to 96% in 2017.
“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”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [1] 1. American Recovery and Reinvestment Act of 2009. 111th Con- gress (2009-2010). Accessed January 3, 2023 at: https://www. congress.gov/bill/111th-congress/house-bill/1
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [2] 2. Hecht J. The future of electronic health records. Nature 2019;573 (7775):S114-S116
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Study design and conductreasoningTime spent working outside normal working hours was used as a proxy for burden, but there are many other contributors to burden.
“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.”
Quote in the paperNot a substantial claim- The authors state this as an assumption: Time spent in the EHR outside working hours is a valid proxy for documentation burden.
Probably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductnone givenAppointments per day did not consider whether a physician had a full or half day at the clinic.
“Appointments per day did not consider whether a physician had a full or half day at the clinic.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductown dataBecause scheduled time was determined by Epic Signal, the analysis could not stratify based on scheduled time for clinic versus scheduled time for operating room or administrative tasks.
“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.”
Quote in the paperNot a substantial claimMay combine two claims.
InterpretationreasoningThese findings indicate that while overall workload may increase, physicians are learning to become even more efficient to maintain a balanced lifestyle.
“These findings indicate that while the overall workload may increase, physicians are learning to become even more efficient to maintain a balanced lifestyle.”
Quote in the paperSubstantial claim- Rests on an assumption: Epic Signal metrics accurately reflect clinicians' EHR usage.
- Rests on an assumption: Years of experience determined from the year of graduation from last residency or fellowship is a valid measure of years practicing as an attending.
May combine two claims.
PremisecitationEvidence highlights that women and less experienced clinicians spend more time on the EHR, possibly correlating with more feelings of burnout.
“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”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [16] 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)
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
May combine two claims.
PremisecitationThese studies were limited to the ambulatory setting and lacked an analysis on clinician characteristics, such as age, gender, and experience.
“However, these studies were limited to the ambulatory setting and lacked an analysis on clinician characteristics, such as age, gender, and experience.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [16] 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)
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
The authors state this as an assumption.
Time spent in the EHR outside working hours is a valid proxy for documentation burden.
“Metrics measuring time spent in the EHR outside working hours were used as a correlate for documentation burden.”
“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 paper relies on this without support.
Epic Signal metrics accurately reflect clinicians' EHR usage.
“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]”
The paper relies on this without support.
The Epic Signal Proficiency Score validly measures EHR proficiency.
“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.”
The paper relies on this without support.
Years of experience determined from the year of graduation from last residency or fellowship is a valid measure of years practicing as an attending.
“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.”
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Urology gender percentages are taken from the surgery row
MinorThe stated female and male percentages for urology match the table's Surgery row. The Urology row instead reports 20.5% for female surgeons and 7.4% for male surgeons.
“Urology (female 28.2% vs. male 19.1%, p ¼ 0.010)”
“| Urology | 15 (11.3) | 7 (7.4) | 8 (20.5) | - |”
Univariate coefficient does not match its confidence interval
MinorFor associate professor status and time per completed message, the reported coefficient is 2.00, but the midpoint of its stated 95% confidence interval is −1.995. The other linear-regression intervals in the table are centered on their coefficients.
Reported 2 · recomputed -1.995
“| Associate professor | 2.00 (À10.81, 6.82) | 0.65 | 4.14 (À5.31, 13.58) | 0.97 |”
Message-time median differs between the figure text and Table 3
MinorFor surgeons with 6 to 10 years of experience, the figure text gives a median of 0.4 minutes per completed message, whereas Table 3 gives 0.5 minutes.
“6-10 years 0.4 min [0.2-0.6]”
“| Time per completed message (min) | 0.4 (0.2-0.6) | 0.6 (0.3-0.8) | 0.5 (0.2-0.6) | 0.3 (0.2-0.5) | 0.002 |”
Study duration is reported as both four and five months
MinorThe abstract says the analysis covered four months, while the methods say metrics were collected for five months.
“We analyzed these metrics among surgeons at our institution across 4 months”
“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.”
Cites
Shanafelt, T. D., Dyrbye, L. N., Sinsky, C., Hasan, O., Satele, D., Sloan, J., & West, C. P. (2016) · Mayo Clinic Proceedings · doi:10.1016/j.mayocp.2016.05.007 · Cited by 1,086 · see citing works
Discrimination, abuse, harassment, and burnout in surgical residency training
Hu, Y.-Y., Ellis, R. J., Hewitt, D. B., Yang, A. D., Cheung, E. O., Moskowitz, J. T., Potts, J. R., Buyske, J., Hoyt, D. B., Nasca, T. J., et al. (2019) · New England Journal of Medicine · doi:10.1056/nejmsa1903759 · Cited by 928 · see citing works
Relationship Between Work-Home Conflicts and Burnout Among American Surgeons: A Comparison by Sex
Dyrbye, L. N. (2011) · Archives of Surgery · doi:10.1001/archsurg.2010.310 · Cited by 591 · see citing works
Physician Burnout in the Electronic Health Record Era: Are We Ignoring the Real Cause?
Downing, N. L., Bates, D. W., & Longhurst, C. A. (2018) · Annals of Internal Medicine · doi:10.7326/m18-0139 · Cited by 418 · see citing works
Moy, A. J., Schwartz, J. M., Chen, R., Sadri, S., Lucas, E., Cato, K. D., & Rossetti, S. C. (2021) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaa325 · Cited by 284 · see citing works
Burnout Related to Electronic Health Record Use in Primary Care
Budd, J. (2023) · Journal of Primary Care & Community Health · doi:10.1177/21501319231166921 · Cited by 223 · see citing works
Factors associated with burnout syndrome in surgeons: a systematic review
Galaiya, R., Kinross, J., & Arulampalam, T. (2020) · The Annals of The Royal College of Surgeons of England · doi:10.1308/rcsann.2020.0040 · Cited by 214 · see citing works
Robertson, S. L., Robinson, M. D., & Reid, A. (2017) · Journal of Graduate Medical Education · doi:10.4300/jgme-d-16-00123.1 · Cited by 170 · see citing works
Dahlke, A. R., Johnson, J. K., Greenberg, C. C., Love, R., Kreutzer, L., Hewitt, D. B., Quinn, C. M., Engelhardt, K. E., & Bilimoria, K. Y. (2018) · Annals of Surgery · doi:10.1097/sla.0000000000002700 · Cited by 137 · see citing works
Moy, A. J., Hobensack, M., Marshall, K., Vawdrey, D. K., Kim, E. Y., Cato, K. D., & Rossetti, S. C. (2023) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocad038 · Cited by 98 · see citing works
Time requirements for electronic health record use in an academic ophthalmology center
Read-Brown, S., Hribar, M. R., Reznick, L. G., Lombardi, L. H., Parikh, M., Chamberlain, W. D., Bailey, S. T., Wallace, J. B., Yackel, T. R., & Chiang, M. F. (2017) · JAMA Ophthalmology · doi:10.1001/jamaophthalmol.2017.4187 · Cited by 85 · see citing works
Electronic health records contributing to physician burnout
Collier, R. (2017) · Canadian Medical Association Journal · doi:10.1503/cmaj.109-5522 · Cited by 81 · see citing works
Gender differences in surgeon burnout and barriers to career satisfaction: a qualitative exploration
Lu, P. W., Columbus, A. B., Fields, A. C., Melnitchouk, N., & Cho, N. L. (2020) · Journal of Surgical Research · doi:10.1016/j.jss.2019.10.045 · Cited by 78 · see citing works
The future of electronic health records
Hecht, J. (2019) · Nature · doi:10.1038/d41586-019-02876-y · Cited by 48 · see citing works
Aziz, F., Talhelm, L., Keefer, J., & Krawiec, C. (2019) · Journal of Vascular Surgery · doi:10.1016/j.jvs.2018.08.173 · Cited by 31 · see citing works
Mete, M., Dickman, J., Rowe, S., Trockel, M. T., Rotenstein, L., Khludenev, G., & Marchalik, D. (2022) · The American Journal of Surgery · doi:10.1016/j.amjsurg.2021.08.033 · Cited by 25 · see citing works
Quantified electronic health record (EHR) use by academic surgeons
Cox, M. L., Risoli, T., Peskoe, S. B., Turner, D. A., & Migaly, J. (2021) · Surgery · doi:10.1016/j.surg.2020.12.009 · Cited by 18 · see citing works
Beiser, M., Lu, V., Paul, S., Ni, J., Nazar, N., Epstein, E., Mammen, A., Short, J., Tomer, Y., & Jariwala, S. P. (2021) · Telemedicine and e-Health · doi:10.1089/tmj.2020.0490 · Cited by 15 · see citing works
Self-citationPhysician electronic health record usage as affected by the COVID-19 pandemic
Ruan, E., Beiser, M., Lu, V., Paul, S., Ni, J., Nazar, N., Liu, J., Kim, M., Epstein, E., Keller, M., et al. (2022) · Applied Clinical Informatics · doi:10.1055/a-1877-2745 · Cited by 15 · see citing works
Self-citationTime and Clerical Burden Posed by the Current Electronic Health Record for Orthopaedic Surgeons
Kesler, K., Wynn, M., & Pugely, A. J. (2022) · Journal of the American Academy of Orthopaedic Surgeons · doi:10.5435/jaaos-d-21-00094 · Cited by 14 · see citing works
Trends and perceptions of electronic health record usage among plastic surgeons
Seu, M., Cho, B. H., Pigott, R., Sarmiento, S., Pedreira, R., Bhat, D., & Sacks, J. (2020) · Plastic and Reconstructive Surgery - Global Open · doi:10.1097/gox.0000000000002709 · Cited by 10 · see citing works
Electronic health record use and perceptions among urologic surgeons
Tan, H.-J., Chung, A. E., Gotz, D., Deal, A. M., Heiling, H. M., Teal, R., Vu, M. B., Meeks, W. D., Fang, R., Bennett, A. V., et al. (2023) · Applied Clinical Informatics · doi:10.1055/s-0043-1763513 · Cited by 8 · see citing works
Assessing the prevalence of burnout among female microvascular head and neck surgeons
Benjamin, T., Gulati, A., Zebolsky, A. L., Seth, R., Knott, P. D., Okuyemi, O., & Park, A. M. (2023) · Facial Plastic Surgery & Aesthetic Medicine · doi:10.1089/fpsam.2022.0312 · Cited by 8 · see citing works
Physician Burnout in the Electronic Health Record Era
Downing, N. L., Bates, D. W., & Longhurst, C. A. (2019) · Annals of Internal Medicine · doi:10.7326/l18-0604
Flagged first, then by citations
Cited by
Sorted by citations
Measuring Documentation Burden in Healthcare
Murad, M. H., Stelling, B. E. V., West, C. P., Hasan, B., Simha, S., Saadi, S. M., Firwana, M., Viola, K. E., Prokop, L. J., Nayfeh, T., et al. (2024) · Journal of General Internal Medicine · doi:10.1007/s11606-024-08956-8 · Cited by 64 · see citing works
The gender gap in academic surgery: the electronic health record
Bessen, S. Y., Tackett, S. A., Jenks, C. M., Stewart, C. M., Oliva‐Hemker, M. M., & Lee, J. K. (2025) · BMC Surgery · doi:10.1186/s12893-025-02808-w · Cited by 5 · see citing works
Electronic Health Record Usage Among Surgeons—The Gender Gap
Crown, A., & Joseph, K.-A. P. (2024) · JAMA Network Open · doi:10.1001/jamanetworkopen.2024.21637 · Cited by 2 · see citing works
Koirala, T., Burger, C. D., Chaudhry, R., Benitez, P., Heaton, H. A., Gopikrishnan, N., & Helgeson, S. A. (2024) · Applied Clinical Informatics · doi:10.1055/s-0044-1790552 · Cited by 2 · see citing works
“49 This metric is notoriously challenging to de fi ne and measure, as it varies depending on EHRvendors.”
Ha, D., Russell, N., Olson, K. E., Whitney, G., Kulungowski, A. M., McCourt, E. A., & Rove, K. O. (2025) · Journal of Pediatric Surgery · doi:10.1016/j.jpedsurg.2025.162191 · Cited by 1 · see citing works
EHR Use in Inpatient Physicians: Patterns and Predictors
Safian, G., Tio, K., Tang, K., Arora, S., & Jariwala, S. P. (2025) · Applied Clinical Informatics · doi:10.1055/a-2615-4085 · Cited by 1 · see citing works
Self-citationGender and Academic Rank Disparities in Electronic Health Record Burden Among Otolaryngologists
Kim, T. Y., Abdel‐Aty, Y., Akkina, S. R., Ator, G. A., Ballard, M. E., Bhayani, M. K., Bohm, L. A., Born, H. L., Bryson, P., Carroll, T. L., et al. (2026) · The Laryngoscope · doi:10.1002/lary.70431 · Cited by 3 · see citing works
“…examining use of note writing tools and personalization report female physicians are more likely to use personalization and documentation tools than their male counterparts [10], arguing against poor EHR efficiency or proficiency in females as the source of the EHR gender disparity [10, 11, 14].”
- Authors
- Tang, K., Labagnara, K., Babar, M., Loloi, J., Watts, K. L., Jariwala, S., & Abraham, N.
- Affiliations
- Albert Einstein College of Medicine (US)
- Corresponding author
- Babar, Mustufa
- Venue
- Applied Clinical Informatics
- Publisher
- Georg Thieme Verlag KG
- Published
- 2023-10-18
- Volume
- 15
- Issue
- 01
- Pages
- 034-044
- ISSN
- 1869-0327 (ISSN-L)
- PMC
- PMC10781576
- OpenAlex
- W4387735315
- Cited by
- 7
- References
- 24
- Citations by year
- 2026: 1 · 2025: 3 · 2024: 3
- FWCI
- 1.57
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Applied Clinical Informatics (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Health Sciences › Health Professions › Health Information Management › Medical Coding and Health Information
- Health Sciences › Medicine › Emergency Medicine › Hospital Admissions and Outcomes
- Keywords
- electronic health records, surgical subspecialties, documentation workload, gender differences, years of experience, surgeon burnout, clinical volume
- MeSH terms
- Female, Health Facilities, Humans, Male, Time Factors, Workload, Electronic Health Records, Surgeons
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-10-11.
Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-11.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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