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Brief Communications
Associations of physician burnout with organizational
electronic health record support and after-hours charting
H. C. Eschenroeder Jr,
1
Lauren C. Manzione ,
2
Julia Adler-Milstein,
3
Connor Bice,
2
Robert Cash,
2
Cole Duda,
2
Craig Joseph,
4
John S. Lee,
5
Amy Maneker,
6
Karl A. Poterack,
7
Sarah Rahman,
8
Jacob Jeppson,
2
and Christopher Longhurst
9
1
OrthoVirginia, Lynchburg, Virginia, USA,
2
KLAS Arch Collaborative,
3
School of Medicine, University of California, San Francisco,
California, USA,
4
El Camino Health,
5
Edward-Elmhurst Healthcare,
6
Aimwell Healthcare Advisors,
7
Mayo Health System,
8
John
Muir Health, and
9
UC San Diego Medical Center, USA
Corresponding Author: H. C. Eschenroeder, Jr, MD, Ortho Virginia, 2405 Atherholt Road, Lynchburg, Virginia 24501 USA
(cesch@orthovirginia.com)
Received 29 June 2020; Revised 16 February 2021; Accepted 11 March 2021
ABSTRACT
In 2017, 43.9% of US physicians reported symptoms of burnout. Poor electronic health record (EHR) usability
and time-consuming data entry contribute to burnout. However, less is known about how modifiable dimen-
sions of EHR use relate to burnout and how these associations vary by medical specialty. Using the KLAS Arch
Collaborative’s large-scale nationwide physician (MD/DO) data, we used ordinal logistic regression to analyze
associations between self-reported burnout and after-hours charting and organizational EHR support. We exam-
ined how these relationships differ by medical specialty, adjusting for confounders. Physicians reporting 5
hours weekly of after-hours charting were twice as likely to report lower burnout scores compared to those
charting 6 hours. (aOR: 2.43, 95% CI: 2.30, 2.57). Physicians who agree that their organization has done a great
job with EHR implementation, training, and support (aOR: 2.14, 95% CI: 2.01, 2.28) were also twice as likely to re-
port lower scores on the burnout survey question compared to those who disagree. Efforts to reduce after-
hours charting and increase organizational EHR support could help address physician burnout.
INTRODUCTION
Burnout, a syndrome of emotional exhaustion, depersonalization,
and a low sense of personal accomplishment, is a common problem
among US physicians.
1
The prevalence of US physician burnout was
43.9% in 2017, which was higher than among other working
adults.
2–4
Physician burnout is recognized as a significant problem
with associated morbidity and mortality, including poor clinical
care, medical mistakes, physicians leaving medicine, and suicide.
5,6
The US economic burden of turnover and reduced clinical hours at-
tributable to physician burnout is estimated to be $4.6 billion each
year.
7
A national report quantified top contributors to burnout based
on the frequency with which each answer choice was selected. Too
many bureaucratic tasks (55%) and the increasing computerization
of practice (30%) were among the top responses.
5
Specifically, evi-
dence suggests that poor electronic health record (EHR) usability,
high volume of patient call messages, and time-consuming data en-
try contribute to professional dissatisfaction.
8–11
The amount of
time spent on EHRs doing nonclinical work and documentation
outside regular clinic hours are also associated with physician
burnout.
12–16
Existing literature examining the associations between burnout
and the EHR is limited to specific medical specialties or select
healthcare organizations. The narrow scope of these studies limits
their generalizability and precludes the possibility of examining the
impact of individual organizations. The effect of EHRs on
V
CThe Author(s) 2021. Published by Oxford University Press on behalf of the American Medical Informatics Association.
This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs licence (http://creativecommons.org/licenses/
by-nc-nd/4.0/), which permits non-commercial reproduction and distribution of the work, in any medium, provided the original work is not altered or transformed in any way,
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Journal of the American Medical Informatics Association, 28(5), 2021, 960–966
doi: 10.1093/jamia/ocab053
Brief Communications
physicians could vary depending on how the EHR is implemented
and supported within an organization, but there is little data to sup-
port this idea.
Using KLAS Arch Collaborative survey responses from 25 018
physicians (MD/DO) at 213 organizations throughout the United
States, we aimed to quantify the extent to which EHR satisfaction
factors, namely after-hours charting and organizational EHR sup-
port, are associated with self-reported burnout overall and by physi-
cian specialty. Better understanding these measures will allow
healthcare organizations to more effectively focus efforts to mitigate
physician burnout.
MATERIALS AND METHODS
KLAS, an independent health information technology firm, began
the Arch Collaborative in 2017 to measure and establish a bench-
mark for the clinician EHR experience. KLAS partnered with health
systems to pilot research surveys. Since then, more than 200 separate
health organizations (comprised primarily of community health sys-
tems, large health systems, and academic health systems) have par-
ticipated in the Arch Collaborative, and most areas of medical
practice are represented. The Collaborative’s main data-collection
instrument is a web-based survey that is available for public use. Ad-
ditional benchmarking data are available to organizations with a
paid subscription to the Arch Collaborative. The core survey con-
tains 35 questions.
17
Participating organizations can make minor
edits for clarity, add additional questions, or exclude irrelevant or
sensitive questions. The survey is distributed to clinicians by health-
care organization leadership. The Arch Collaborative collects and
analyzes the data and generates a report that includes national
benchmarking. The database includes responses from over 46 000
physicians. A single-question burnout measure was added to the sur-
vey in 2018, and more than 25 000 burnout responses from over
200 healthcare organizations have been collected through June
2020.
Measures
The dependent variable of this study is represented by the single-
question taken from the AMA mini-Z measurement for burnout.
18
“Using your own definition of burnout, select 1 of the answers be-
low: 1. I enjoy my work. I have no symptoms of burnout. 2. I am un-
der stress and don’t always have as much energy as I did, but I don’t
feel burned out. 3. I am definitely burning out and have 1 or more
symptoms of burnout (eg, emotional exhaustion). 4. The symptoms
of burnout that I am experiencing won’t go away. I think about
work frustrations a lot. 5. I feel completely burned out. I am at the
point where I may need to seek help.” Similar self-reported single
questions have been validated and used in studies as a representation
of burnout, though this single measure likely underrepresents burn-
out.
19–21
We combined responses from options 3, 4, and 5 to calcu-
late the percentage of physicians burned out for each specialty.
The focus of the Arch survey is to measure the EHR end-user ex-
perience. Accordingly, several questions are used to measure aspects
of the EHR experience. Preliminary analyses indicated that 1 of our
main independent variables, organizational EHR support, was
among the survey measures most highly correlated with burnout.
Other similar variables were excluded from our analysis due to col-
linearity. The Arch survey assesses the provider’s impression of or-
ganizational support of their use of the EHR with the prompt: “Our
organization has done a great job of implementing, training on, and
supporting the EHR.” The respondents could then answer on a 5-
point Likert scale from strongly disagree to strongly agree. Conse-
quently, rather than assessing each individual component, this ques-
tion collectively assesses EHR implementation, training, and
support.
Our other main independent variable is after-hours charting.
Prior research has highlighted the effect that after-hours work has
on burnout among much smaller populations than in the current
study.
13,15,16
The Arch Collaborative measures after-hours charting
with the following question: “How many hours per week do you
spend completing your charting outside of your normal business
hours (evenings, weekends, after your shift, etc.)?” Respondents
then selected banded answers under the following ranges: 0–5 hours,
6–15 hours, 16–25 hours, and 25þhours. The likelihood of
experiencing symptoms of burnout became more common with each
increase in time spent on after-hours charting (see Table 1), with the
largest jump occurring between 0–5 hours and 6–15 hours.
Confounding variables (see Table 1) were also taken from the
Arch Collaborative survey, including healthcare organization, geo-
graphic region (taken from the longitude and latitude using the
respondent’s IP address and converted into census regions), the EHR
vendor used, years of practicing medicine, and years using the EHR
(0–4 years and 5þyears). Physicians with missing responses for any
of the included variables were excluded from the study.
Analyses
Ordinal logistic regression was used to analyze the association be-
tween burnout and after-hours charting and organizational EHR
support. Individual burnout was measured on an ordinal scale from
completely burned out to no burnout. Organizational EHR support
was measured using the question: “Do you agree with the following
statement? Our organization has done a great job of implementing,
training on, and supporting the EHR.” Responses ranging from
“strongly disagree” to “neither agree nor disagree” were coded as
“disagree.” Responses ranging from “agree” to “strongly agree”
were coded as “agree.” For after-hours charting, physicians were
grouped by those who reported 0–5 hours of after-hours charting
per week and those who reported 6þhours of charting.
The final model included burnout on a 5-point Likert scale as
the main outcome variable with the dichotomized variables for
after-hours charting and organizational EHR support as the main
explanatory variables, and controlling for organization using clus-
tering, census region, EHR vendor, years practicing medicine, and
years using the EHR. The physician specialty model replicated the
original model but was limited to specialties with 300 or more
respondents. Each specialty was mutually exclusive, meaning they
were only able to select 1 self-identified specialty.
RESULTS
The demographic characteristics of the study population are pre-
sented in Table 1. Of the 25 018 respondents, 35.6% were from aca-
demic health systems, followed by 28.6% from large health systems
(those with over 1500 beds). About 29% of respondents were in the
Midwest and 28% in the South. Over 68% of respondents use Epic
as their primary EHR vendor, followed by Cerner (15.8%). About
33% of participating physicians had been practicing medicine for
25þyears at the time of the survey. Almost 52% had been using the
EHR for at least 5 years when surveyed.
Journal of the American Medical Informatics Association, 2021, Vol. 28, No. 5 961
Table 2 presents descriptive data for burnout, after-hours chart-
ing, and organizational EHR support. The level of burnout in our
study ranged from 22% to 34% by specialty. The specialties with the
highest levels of burnout in our study were family medicine (34%)
and hematology/oncology (33%). The specialties with the lowest
levels of burnout were psychiatry (22%) and anesthesiology (24%).
The associations between physician burnout and after-hours
charting are presented in Figure 1. After adjusting for confounding
variables, physicians with 5 or fewer hours of weekly after-hours
charting (aOR: 2.43, 95% CI: 2.30, 2.57) were twice as likely to re-
port lower levels of burnout than those with 6 or more hours. Fig-
ure 2 presents the associations between physician burnout and
organizational EHR support. Those who agree that their organiza-
tion has done a great job with EHR implementation, training, and
support (aOR: 2.14, 95% CI: 2.01, 2.28) were also twice as likely to
report lower levels of burnout than those who disagreed.
Table 1. Demographic variables
Demographic Variables Organizations n ¼213 Organization % Respondents n ¼25 018 Respondent %
Organization Type —— ——
Community Health System 45 21.00% 3267 13.00%
Large Health System 44 20.60% 7180 28.60%
Academic Health System 40 18.70% 8941 35.60%
Community Hospital 29 13.60% 695 2.80%
Midsize Health System 22 10.30% 2592 10.30%
Children’s Hospital 17 7.90% 1324 5.30%
Ambulatory Care Group 19 8.90% 1132 4.50%
Geographic Regiona —— ——
West 100 47% 4460 18%
Midwest 94 44% 7135 29%
Northeast 88 41% 6543 26%
South 113 53% 6880 28%
EHR Vendor
b
—— ——
Epic 148 69.20% 17 204 68.50%
Other 81 37.90% 1060 4.20%
Cerner 43 20.10% 3976 15.80%
MEDITECH 32 15.00% 525 2.10%
Allscripts 23 10.70% 1083 4.30%
eClinicalWorks 22 10.30% 1075 4.30%
NextGen Healthcare 11 5.10% 39 0.20%
Years Practicing Medicine —— ——
0–4 years — — 1343 5.30%
5–14 years — — 7789 31.00%
15–24 years — — 7196 28.60%
25þyears — — 8315 33.10%
Years Using the EHR —— ——
1 year — — 2274 9.00%
2 years — — 1818 7.20%
3 years — — 1861 7.40%
4 years — — 2284 9.10%
5þyears — — 13 012 51.80%
Self-Reported After-hours Charting ——
0-5 hours — — 20 912 57%
6-15 hours — — 12 829 35%
16-25 hours — — 2473 7%
25þhours — — 752 2%
Organizational EHR Support
c
——
Strongly Agree — — 4038 9%
Agree — — 15 586 35%
Indifferent — — 11 134 25%
Disagree — — 8511 19%
Strongly Disagree — — 5359 12%
Self-Reported Burnout ——
Completely burned out — — 340 1%
Symptoms of burnout won’t go away — — 1859 7%
Definitely burning out — — 5417 22%
Under stress — — 11 328 45%
No burnout — — 6074 24%
a
Healthcare organizations that have locations in more than 1 region are included multiple times in the geographic region counts.
b
Healthcare organizations that use more than 1 EHR vendor are included multiple times in the EHR vendor counts.
c
Agreement that organization has done a great job with EHR implementation, training, and support.
962 Journal of the American Medical Informatics Association, 2021, Vol. 28, No. 5
Table 2. Physician burnout, after-hours charting, and organizational EHR support descriptive statistics
Specialty
a
Physician n Organization n
% Of Physicians
Burned Out
c
% of Physicians with 6 or
More Hours of Weekly
After-hours Charting
Organizational
EHR Support
b
All Organizations 25 018 213 30% 43% 44%
Family Medicine 3010 181 34% 53% 47%
Hematology/Oncology 455 129 33% 60% 35%
Internal Medicine 2164 170 32% 53% 47%
Neurology 459 142 31% 52% 44%
Cardiology 726 164 30% 50% 38%
Radiology 349 128 29% 12% 35%
Gynecology and Obstetrics 1126 166 29% 41% 43%
Pediatrics 1684 176 28% 43% 50%
Pulmonology 322 135 28% 56% 39%
Emergency Medicine 1391 171 27% 31% 46%
General Surgery 895 166 27% 40% 43%
Gastroenterology 328 147 26% 49% 42%
Hospital Medicine 973 141 25% 34% 54%
Orthopedics 751 165 25% 42% 37%
Anesthesiology 959 163 24% 14% 49%
Psychiatry 465 148 22% 37% 47%
a
Physician specialties with <300 respondents and physicians who did not indicate a specialty were included in the overall analysis but excluded from specialty
analyses.
b
Percent that agree organization has done a great job with EHR implementation, training, and support.
c
Percent Burned Out is the percent who responded “I am definitely burning out and have 1 or more symptoms of burnout (eg, emotional exhaustion),” “The
symptoms of burnout that I am experiencing won’t go away. I think about work frustrations a lot,” or “I feel completely burned out. I am at the point where I
may need to seek help.”
Figure 1. Associations between 5 hours or fewer of weekly after-hours charting and lower levels of self-reported physician burnout.
†
*Significant at P<.05
**Significant at P<.001
***Significant at P<.0001
†
Self-reported burnout was measured using a 5-point scale from completely burned out to no burnout with a positive odds ratio indicating lower levels of burn-
out.
Note: Physician specialties with <300 respondents and physicians who did not indicate a specialty were included in the overall analysis but excluded from spe-
cialty analyses.
Journal of the American Medical Informatics Association, 2021, Vol. 28, No. 5 963
Physicians with 5 or fewer hours of weekly after-hours charting
were significantly more likely to report lower levels of burnout for
most physician specialties, with the highest odds ratios for gynecol-
ogy and obstetrics (aOR: 3.34, 95% CI: 2.57, 4.35) and pediatrics
(aOR: 3.20, 95% CI: 2.59, 3.94). Physicians who agreed that their
organization has done a great job with EHR implementation, train-
ing, and support were significantly more likely to report lower levels
of burnout for all physician specialties, with the highest odds ratios
for cardiology (aOR: 3.28, 95% CI: 2.41, 4.47) and neurology
(aOR: 2.74, 95% CI: 1.87, 4.02).
DISCUSSION
In our study, after-hours charting was significantly associated with
physician burnout. Physicians who reported spending 5 or fewer
hours on weekly after-hours charting were significantly more likely
to report lower levels of burnout overall and for most included spe-
cialties. Several other smaller-scale studies also found an association
between after-hours charting and symptoms of burnout.
13,15,16
However, the etiology of after-hours charting is multifactorial—
clinical work volume and complexity, workflows, staffing, provider
EHR mastery, the EHR build, and other factors can all contribute.
EHR factors are not solely responsible for after-hours charting, and
other efforts besides EHR improvements, such as team documenta-
tion and new approaches to care team models for clinical sup-
port,
22,23
may reduce after-hours charting.
Satisfaction with organizational EHR support was significantly
associated with lower levels of burnout overall and for all specialties
included in our study, independent of the after-hours charting vari-
able. While the organizational EHR support measurement has not
been used in prior literature, previous studies have reported that or-
ganizational IT improvements can reduce burnout symptoms.
24–27
Our findings suggest that EHR interventions focused on improving
organizational EHR support could help reduce physician burnout
regardless of the time physicians spend on after-hours charting.
Furthermore, variation in burnout by specialty in this study was
notable. The specialties with the highest levels of burnout in our
study were family medicine, hematology/oncology, internal medi-
cine, pulmonology, neurology, and cardiology. A nationally repre-
sentative study also reported that neurology and family medicine
were among the specialties with the highest levels of burnout, in ad-
dition to urology, nephrology, and diabetes and endocrinology,
which were not included in our analyses.
5
The specialties with the
lowest levels of burnout in our study were psychiatry, gastroenterol-
ogy, anesthesiology, orthopedics, and hospital medicine. Similarly, a
prior study reported that psychiatry, orthopedic, and gastroenterol-
ogy specialists were among those physicians with the lowest levels of
burnout.
5
However, we found much lower levels of burnout com-
pared to the prior study which was also on a national scale.
5
The
difference in the findings for the level of burnout could be related to
differences in the burnout measurement and study design.
19–21
The
overall self-reported burnout in our study is similar to what was
found in previous smaller-scale studies examining the relationship
between after-hours charting and self-reported burnout.
13,15
We acknowledge that there are limitations to this study. The re-
sponse rate for some of the participating organizations is not
known, which precludes reporting the overall response rate. Our
Figure 2. Associations between perceived organizational EHR support and lower levels of self-reported physician burnout.
†
*Significant at P<.05
**Significant at P<.001
***Significant at P<.0001
†
Self-reported burnout was measured using a 5-point scale from completely burned out to no burnout with a positive odds ratio indicating lower levels of burn-
out.
Note: Physician specialties with <300 respondents and physicians who did not indicate a specialty were included in the overall analysis but excluded from spe-
cialty analyses.
964 Journal of the American Medical Informatics Association, 2021, Vol. 28, No. 5
sample methodology includes a risk of selection and nonresponse
bias at both the organization and individual level of unknown direc-
tion and magnitude. We were also unable to control for sociodemo-
graphic variables such as sex, race, and age because they were not
included in data collection. However, we used years practicing med-
icine as a proxy for age. In addition, while our data include physi-
cians in all 50 states, they are a nonrandom sample and not
nationally representative. Our sample disproportionately comes
from health systems, with less representation of hospitals that re-
main independent of health systems.
28
We have an oversampling of
organizations using Epic. In our sample, 69% use the Epic EHR,
while nationally, 29% of acute-care multispecialty hospitals use
Epic.
29
We also likely have an oversample of physicians from the
northeast and midwest regions.
30
We included these demographic
variables as covariates to help account for these differences. Our
study uses self-reported after-hours work which may reflect various
inaccuracies, as opposed to objective measures of EHR use outside
of scheduled hours. In addition, we collectively assessed EHR imple-
mentation, training, and support rather than assessing each individ-
ual component. Despite these limitations, this study provides new
insights into factors associated with physician burnout and high-
lights areas for future research. The large sample of organizations
and individual participants allowed us to analyze factors associated
with burnout by specialty.
Using the Arch Collaborative database, we found that organiza-
tional EHR support and time spent on after-hours charting are inde-
pendently associated with physician burnout. In addition, these
associations and the level of burnout differed by physician specialty.
Family medicine had the highest levels of burnout. Gynecology and
obstetrics had the highest odds of not being burned out in associa-
tion with 5 or fewer hours of after-hours charting. Cardiology had
the highest odds of not being burned out in association with report-
ing adequate organizational EHR support. Organizational efforts to
reduce after-hours charting and programs directed at supporting the
provider’s use of the EHR could mitigate physician burnout. These
findings, if verified by other research, may help healthcare organiza-
tions allocate and focus time and resources more effectively.
FUNDING
This research was supported in part by KLAS, a healthcare information tech-
nology research firm. The healthcare organizations who participated in this
survey purchased an Arch Collaborative membership. The Arch Collaborative
survey is available to the public domain. Arch Collaborative members sub-
scribe to access benchmarked data and summarized content through KLAS.
Funds from these memberships also helped sponsor this article.
AUTHOR CONTRIBUTIONS
Conceived study concept: CB, HCE, RC. Conceived study design: CD, JAM,
JJ, LCM, RC. Contributed to data analysis and visualization: CD, CL, HCE,
JJ. Wrote the manuscript: CB and LCM. All authors reviewed and edited
manuscript. All authors approved the final manuscript.
ACKNOWLEDGMENTS
We would like to acknowledge Taylor Davis and Jenifer Gordon for the con-
structive feedback they provided throughout the analysis and writing pro-
cesses.
AVAILABILITY OF DATA
The data underlying this article were provided by KLAS Research by permis-
sion. Data will be shared on request to the corresponding author with permis-
sion of KLAS Research.
CONFLICT OF INTEREST STATEMENT
KLAS works with most major healthcare IT vendors and consulting firms.
Funds from these firms are used to conduct market research and to provide
consulting engagements. These funds do not directly support the research pre-
sented in this article but are a major source of revenue for KLAS. In addition
to these funds, HIT vendors can be members of the Arch Collaborative. Those
that are paying members of the collaborative have received consulting engage-
ments and data that are related to this research. Dr. Christopher Longhurst is
an uncompensated KLAS advisory board member. Dr. Craig Joseph works
for the consulting firm Nordic and formerly worked for the consulting com-
pany Avaap.
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