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RESEARCH
thebmj
BMJ
2022;378:e070442 | doi: 10.1136/bmj-2022-070442 1
Associations of physician burnout with career engagement and
quality of patient care: systematic review and meta-analysis
Alexander Hodkinson,1,9 Anli Zhou,1 Judith Johnson,2,3 Keith Geraghty,1 Ruth Riley,4
Andrew Zhou,5 Earis Panagopoulou,6 Carolyn A Chew-Graham,7 David Peters,8 Aneez Esmail,1
Maria Panagioti1.9
ABSTRACT
OBJECTIVE
To examine the association of physician burnout with
the career engagement and the quality of patient care
globally.
DESIGN
Systematic review and meta-analysis.
DATA SOURCES
Medline, PsycINFO, Embase, and CINAHL were
searched from database inception until May 2021.
ELIGIBILITY CRITERIA FOR SELECTING STUDIES
Observational studies assessing the association of
physician burnout (including a feeling of overwhelming
emotional exhaustion, feelings of cynicism and
detachment from job dened as depersonalisation,
and a sense of ineectiveness and little personal
accomplishment) with career engagement (job
satisfaction, career choice regret, turnover intention,
career development, and productivity loss) and the
quality of patient care (patient safety incidents, low
professionalism, and patient satisfaction). Data
were double extracted by independent reviewers and
checked through contacting all authors, 84 (49%) of
170 of whom conrmed their data. Random-eect
models were used to calculate the pooled odds
ratio, prediction intervals expressed the amount of
heterogeneity, and meta-regressions assessed for
potential moderators with signicance set using a
conservative level of P<0.10.
RESULTS
4732 articles were identied, of which 170
observational studies of 239 246 physicians were
included in the meta-analysis. Overall burnout in
physicians was associated with an almost four
times decrease in job satisfaction compared with
increased job satisfaction (odds ratio 3.79, 95%
condence interval 3.24 to 4.43, I2=97%, k=73
studies, n=146 980 physicians). In the presence of
increased burnout, career choice regret increased by
more than threefold compared with being satised
with career choice (3.49, 2.43 to 5.00, I2=97%, k=16,
n=33 871). Turnover intention also increased by more
than threefold compared with retention (3.10, 2.30
to 4.17, I2=97%, k=25, n=32 271). Productivity had a
small but signicant association with burnout (1.82,
1.08 to 3.07, I2=83%, k=7, n=9581) and burnout
also aected career development (3.77, 2.77 to 5.14,
I2=0%, n=3411). Overall physician burnout doubled
patient safety incidents compared with no patient
safety incidents (2.04, 1.69 to 2.45, I2=87%, k=35,
n=41 059). As burnout increased, low professionalism
was twice as likely compared with maintained
professionalism (2.33, 1.96 to 2.70, I2=96%, k=40,
n=32 321), as was patient dissatisfaction compared
with patient satisfaction (2.22, 1.38 to 3.57, I2=75%,
k=8, n=1002). Burnout and poorer job satisfaction
was greatest in hospital settings (1.88, 0.91 to 3.86,
P=0.09), physicians aged 31-50 years (2.41, 1.02 to
5.64, P=0.04), and working in emergency medicine
and intensive care (2.16, 0.98 to 4.76, P=0.06);
burnout was lowest in general practitioners (0.16,
0.03 to 0.88, P=0.04). However, these associations
did not remain signicant in the multivariable
regressions. The association between burnout and
patient safety incidents was greatest in physicians
aged 20-30 years (1.88, 1.07 to 3.29, P=0.03), and
people working in emergency medicine (2.10, 1.09
to 3.56, P=0.02). The association of burnout with
low professionalism was smallest in physicians older
than 50 years (0.36, 0.19 to 0.69, P=0.003) and
greatest in physicians still in training or residency
(2.27, 1.45 to 3.60, P=0.001), in those who worked in
a hospital (2.16, 1.46 to 3.19, P<0.001), specically
in emergency medicine specialty (1.48, 1.01 to 2.34,
P=0.042), or situated in a low to middle income
country (1.68, 0.94 to 2.97, P=0.08).
CONCLUSIONS
This meta-analysis provides compelling evidence that
physician burnout is associated with poor function
For numbered aliations see
end of the article
Correspondence to: A Hodkinson
alexander.hodkinson@
manchester.ac.uk
(or @drAlexHodkinson on Twitter:
ORCID 0000-0003-2063-0977)
Additional material is published
online only. To view please visit
the journal online.
Cite this as: BMJ ;:e
http://dx.doi.org/10.1136/
bmj-2022-070442
Accepted: 03 July 2022
WHAT IS ALREADY KNOWN ON THIS TOPIC
Burnout is reaching global epidemic levels among physicians and many
physicians advocate that the capacity in the eld of medicine is almost reaching
crisis point
A better understanding of the association of burnout with the career engagement
of physicians is urgently needed now more than ever as health and care systems
across the globe are facing a critical workforce crisis
No meta-analysis to date has examined the association of burnout with the
career engagement of physicians, nor have they presented this relationship in
parallel with the potential impacts on the quality of patient care
WHAT THIS STUDY ADDS
The largest and most comprehensive systematic review and meta-analysis
assessing the association of burnout with the career engagement of physicians
and the quality of patient care, summarising results from 170 observational
studies with 239 246 physicians
Physicians with burnout are up to four times more likely to be dissatised with
their job and more than three times as likely to have thoughts or intentions to
leave their job (turnover) or to regret their career choice
Physicians with burnout are twice as likely to be involved in patient safety
incidents and show low professionalism, and over twice as likely to receive low
satisfaction ratings from patients
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and sustainability of healthcare organisations
primarily by contributing to the career disengagement
and turnover of physicians and secondarily by
reducing the quality of patient care. Healthcare
organisations should invest more time and eort in
implementing evidence-based strategies to mitigate
physician burnout across specialties, and particularly
in emergency medicine and for physicians in training
or residency.
SYSTEMATIC REVIEW REGISTRATION
PROSPERO number CRD42021249492.
Introduction
Burnout is defined as a syndrome related to work
that involves three key dimensions. Firstly, emotional
exhaustion, which represents the basic individual
stress dimension of burnout and refers to feelings of
being overextended and depleted of emotional and
physical resources. Secondly, depersonalisation,
which is the cynicism component and represents a
motivational, interpersonal distancing dimension of
burnout and refers to a negative, callous, or excessively
detached response to various aspects of the job.
Finally, a sense of reduced personal accomplishment,
which represents the self-evaluation dimension of
burnout and refers to feelings of incompetence and
inadequate achievement and productivity at work.1
Burnout is rampant and reaching global levels among
physicians.2 In the US, four in 10 physicians report at
least one symptom of burnout,3 and in the UK, a third
of trainee doctors report that they experience burnout
to a high or very high degree.4 In a recent review of low
and middle income countries the overall single-point
prevalence of burnout ranged from 2.5% to 87.9%
among 43 studies.5 Moreover, the covid-19 pandemic
has created new causes for stress with unsafe working
conditions and higher workloads, which have further
exacerbated burnout in physicians.6 7
Physicians with burnout often report poor work-
life balance and career dissatisfaction.8 9 However,
previous systematic reviews that focused on the
potential eects of physician burnout on healthcare
eciency have overlooked the association of burnout
with career engagement of physicians. Healthcare
provider burnout was associated with lower quality
patient care in a recent systematic review.10 However,
no pooled estimates of these associations were
provided due to high heterogeneity, which was partly
caused by analysing mixed samples of healthcare
providers and studies with little to no flexibility of the
quality metrics used for patient outcome subgroups.
A joint synthesis of the links of physician burnout with
the career engagement of physicians and the quality of
care provided to patients is important because these
aspects are complementary of the overall eciency
of healthcare organisations according to existing
theoretical frameworks and research evidence.11-13
These reciprocal relations should be made available
to governments and policy organisations to encourage
financial investments and policies to mitigate physician
burnout internationally. No previous systematic
reviews has taken this approach.14-16 For instance,
a meta-analysis published in 2022 that assessed
the association of burnout with only self-reported
medical errors among physicians found an increased
risk of self-reported errors.16 Two further systematic
reviews,14 15 which assessed the association between
physician or healthcare professionals’ wellbeing and
burnout with patient safety, did so through a narrative
review approach due to large heterogeneity.
Therefore, we aimed to add value through a larger
and more robust meta-analysis that controlled for
heterogeneity and other possible biases in career
engagement (which is currently unknown at the
systematic review level) and quality-of-care outcomes.
In this systematic review and meta-analysis, we
examined the association of physician burnout with
the career engagement of physicians focusing on job
satisfaction, career choice regret, career development,
productivity loss and turnover intention; and
the quality of patient care focusing on patient
safety incidents, low professionalism, and patient
satisfaction. Based on existing frameworks that have
studied the relation between occupational distress and
impairment related to sleep deprivation in physicians
and unsolicited patient complains,13 a flow diagram
of the anticipated associations is presented in figure
1. We also conducted meta-regressions to uncover
important moderators of these associations.
Methods
This systematic review followed a registered
(PROSPERO CRD42021249492) protocol17 and is
reported in accordance with the Reporting Checklist
for Meta-analyses of Observational Studies (MOOSE)18
and Preferred Reporting Items for Systematic Reviews
and Meta-Analyses (PRISMA) guidance.19 A protocol
amendment was made in November 2021 to exclude
grey literature from this review. The completed
checklists are available in appendix 1.
Search strategy and study eligibility
We included quantitative observational studies
involving physicians working in any healthcare
setting. We reported comparative data on the
association between burnout and career engagement
of physicians (ie, job satisfaction, career choice regret,
turnover intention, reduced productivity indicated by
presenteeism or absenteeism, and career development)
and quality of patient care outcomes (ie, patient safety
incidents including medication errors, suboptimal
patient care due to low professionalism based on an
established definition,20 and patient satisfaction).
Definitions for each of the outcomes are provided in
appendix 2. Studies that did not report their sample
had this missing information confirmed by contacting
authors. Randomised controlled trials were excluded
because our study focus is on associations and not
interventions, qualitative studies and quantitative
studies involving fewer than 70% of responses from
physicians were also excluded. This 70% threshold
is an arbitrary criterion that we have used in previous
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reviews in this area.21 The reason we adopted this
criterion is because we did not want to exclude relevant
studies that included only a small number of other
health professionals in addition to physicians.
We searched Medline, PsycINFO, Embase, and
CINAHL from database inception to May 2021
for citations in English. The searches included
combinations of key blocks of terms involving Medical
Subject Headings terms and text words. The full search
strategies are detailed in the appendix 3. The reference
lists of relevant systematic reviews and eligible
studies were manually searched to identify additional
literature.
Two independent reviewers (AH and AZ or MP and
AZ) rated the eligibility of each of the abstracts and
full texts in Covidence.22 Disagreements were resolved
by consensus, and we measured inter-rater agreement
with the κ statistic.
Data extraction and quality assessment
Using a standardised form that was pilot tested, we
extracted data for study characteristics (country,
recruitment, healthcare setting, and design), physician
characteristics (sample size, mean age, sex (percentage
of men), specialty, and work experience), burnout
(measure characteristics) and the outcome measures
including the method of reporting. The outcomes of
interest were each assessed against overall burnout
and any of the three subscales of burnout including
emotional exhaustion, depersonalisation, and
personal accomplishment. Where all three subscales
were reported and the overall burnout score was not,
we calculated burnout by pooling across the three
subscale scores. We tr ansformed extr acted quantitative
data to the uniform log scale and standardised
mean dierence using the statistical software
Comprehensive Meta-Analysis.23 The formulae for
these transformations are provided in appendix 4.
One of six reviewers (AH, MP, JJ, KG, RR, and AZ)
completed data extractions and double checked with
any disagreements being resolved by consensus.
We emailed all the study authors to confirm the
accuracy and validity of their data and to obtain any
missing data. 84 (49%) of 170 of the study authors
confirmed their data and our extractions were found
to be accurate in 96% of these studies (appendix 5).
The Newcastle Ottawa critical appraisal tool was
used to assess the quality of the studies.24 Pairs of
reviewers in three groups (AH and AZ, MP and KG,
or JJ and RR) appraised the fundamental criteria:
(1) representativeness of the sample, (2) sample
size, (3) non-respondents, (4) ascertainment of the
exposure, (5) controlled for confounding factors, (6)
assessment of outcome, and (7) adequate statistical
tests. Explanations of how each variable was coded
is detailed in the appendix. The total maximum score
was 7, and we classified overall scores of 0-2 as high
risk, 3-5 as medium risk, and 6-7 as low risk.
Data synthesis
We used DerSimonian-Laird random eects while
pooling the log odds,25 then exponentiated these
results to odds ratio and presented the data in forest
plots. A fixed eect method of pooling was considered
in meta-analysis with fewer than five studies and varied
sample and eect sizes. Heterogeneity in the meta-
analysis was quantified using the I2 statistic with its
95% confidence intervals.26 Because of the high level
of heterogeneity, Hartung-Knapp method of pooling
and estimating 95% confidence intervals were used
to account for uncertainty in the variance estimate.27
Overall burnout and its three subscales (emotional
exhaustion, depersonalisation, and personal
accomplishment) were synthesised individually and
presented as so within the forest plots. In total, 32
possible meta-analysis comparisons were found. A
Physician turnover
Career disengagement
Poor quality patient care
Patient safety incidents
Job dissatisfaction
Poor career development
Career choice regrets Productivity loss
Physician burnoutHealthcare inefficiency
Unprofessional care Patient dissatisfaction
Fig | Flow diagram of examined associations of physician burnout with career engagement and quality of patient care. Outcomes assessed in the
analysis are in yellow or red. Outcomes in red emphasise the potential heightened risk of the outcome compared with the outcomes in yellow (which
could be less serious to the physician and healthcare system
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subgroup meta-analysis was performed to assess for
dierences between the four burnout measures (full
22-item Maslach Burnout Inventory, abbreviated
(shortened) version of the Maslach Burnout Inventory,
Copenhagen burnout inventory, and use of another
inventory). Dierences between these groups were
assessed using the statistic ratio of odds ratio.28
In a meta-analysis involving 10 or more studies,
funnel plots and Egger’s test were used to assess for
publication bias,29 and prediction intervals were
calculated to express the amount of heterogeneity.30
We used univariable and multivariable meta-
regressions using the following variables: region
(US, UK/European Union, Commonwealth, South
East Asia/other); setting (primary care, hospital,
mixed); design (cross sectional, prospective cohort
or longitudinal); age (≤30, 31-50, and ≥51 years);
sex (female, male, mixed); specialty or position of
profession ((1) physician or internal medicine, (2)
general practitioners (GPs), (3) surgery including
neurosurgery, (4) emergency medicine and intensive
care, (5) cancer or oncology, (6) intern or resident, (7)
paediatrics, (8) psychiatry, (9) mixed, (10) neurology,
and (11) other); work experience (experienced with
over 6 years, less experienced intern/resident ≤6
years, mixture of experience), and burnout measure
(Maslach Burnout Inventory, any iteration,31 or other
classified burnout inventory v mixed and other32).
Variables from the univariable regressions with a more
conservative level of significance of P<0.10 rather
than p<0.05 were used in the multivariable model.33
The variables were added by using forward selection
process.34 Sensitivity analyses for risk of bias was done
based on the three categories for the total score of
the Newcastle Ottawa assessment (low, medium, and
high). All meta-analyses were conducted in R version
4.0.5 (R Foundation for Statistical Computing)35 using
the meta36 and metafor37 packages.
Patient and public involvement
We consulted five GPs in the Greater Manchester region
who were members of an established patient and
public involvement group about the appropriateness
of our research questions and classification of
outcomes and appropriateness of wellbeing measures
used. These GPs also advised on the interpretation
of our findings and will help with the dissemination
strategy.
Additional records identified through other sources
Full text articles excluded
Not burnout and patient or career outcome association
Non-amenable data for meta-analysis*
Randomised controlled trial design
Not physicians
Fewer than 70% were physicians
Not in English
Response letter
Conference abstract
Dissertation
Systematic or literature review or meta-analysis
361
49
35
34
16
11
4
2
1
1
Total records identified
Records identified through database searching
Duplicates removed
Records screened
Studies included in systematic review and meta-analysis
514
10 554
170
38
4732
Full text articles assessed for eligibility
5822
Records excluded at abstract and title level
4048
10 516
684
Fig | Study selection. *See references in appendix
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Results
Our search strategy identified 4732 articles, of which
684 met the criteria for full text review (fig 2). A total
of 170 studies involving 239 246 physicians (150
cross sectional studies including 231 964 physicians
and 20 prospective or longitudinal studies including
7282 physicians) met the eligibility criteria. The
characteristics of the included studies are summarised
in appendix 6, and citations are provided in appendix
7. Agreement between reviewers for study inclusion
was high (κ 0.89, 95% confidence interval 0.81 to
0.96).
Table | Meta-analysis of the association of burnout with outcomes based on the career engagement of physicians and quality of patient care
Burnout and submeasure
No of studies (No of
physicians) Direction of association
Odds ratio (% CI);
(% PI) I (% CI) Publication bias†
Career engagement of physicians
Career choice regret:
Burnout 16 (33 871) Favours career choice regret compared
with being satised with their career
choice
3.49 (2.43 to 5.00);
(0.90 to 13.49)
97 (96 to 98) P=0.004
Emotional exhaustion* 4 (2014) 4.16 (3.34 to 5.19) 90 (77 to 95) NA
Depersonalisation* 2 (274) 1.54 (0.97 to 2.45) 65 (0 to 92) NA
Personal accomplishment* 1 (147) 1.12 (0.36 to 3.48) NA NA
Career development:
Burnout* 2 (3411) Favours negative career development
compared with positive career
development
3.77 (2.77 to 5.14) 0NA
Emotional exhaustion* 1 (593) 1.08 (0.80 to 1.44) NA NA
Depersonalisation* 1 (593) 1.12 (0.83 to 1.49) NA NA
Personal accomplishment No data No data No data NA
Job satisfaction:
Burnout 73 (146 980) Favours decreased job satisfaction
compared with increased job satisfaction
3.79 (3.24 to 4.43);
(1.13 to 12.77)
97 (96.6 to 98) P=0.002
Emotional exhaustion 33 (22 699) 4.81 (3.67 to 6.30);
(1.11 to 20.93)
98 (97 to 98.3) P=0.04
Depersonalisation 30 (22 002) 2.89 (2.37 to 3.53);
(1.07 to 7.82)
92 (90 to 94) P=0.98
Personal accomplishment 32 (27 374) 2.88 (2.28 to 3.63);
(0.86 to 9.66)
93 (91 to 95) P=0.83
Productivity loss:
Burnout 7 (9581) Favours increase in productivity loss
compared with sustained productivity
1.82 (1.08 to 3.07) 83 (66 to 91) NA
Emotional exhaustion* 4 (3421) 1.06 (1.00 to 1.12) 90 (77 to 96) NA
Depersonalisation* 3 (2969) 1.23 (1.18 to 1.28) 96 (92 to 98) NA
Personal accomplishment* 3 (2969) 1.53 (1.43 to 1.63) 97 (94 to 99) NA
Turnover intention:
Burnout 25 (32 271) Favours turnover intention compared with
retention
3.10 (2.30 to 4.17);
(0.71 to 13.56)
97 (96 to 97.3) P<0.001
Emotional exhaustion 16 (23 625) 2.81 (1.80 to 4.40);
(0.46 to 17.11)
99 (98.8 to 99.2) P=0.001
Depersonalisation 11 (23 257) 1.82 (1.26 to 2.62);
(0.53, 6.26)
99 (98.7 to 99.2) P=0.03
Personal accomplishment 5 (11 028) 1.28 (0.98 to 1.68) 86 (70 to 94) NA
Quality of patient care
Professionalism:
Burnout 40 (32 321) Favours low professionalism compared
with maintained professionalism
2.33 (1.96 to 2.70);
(0.88 to 6.16)
96 (95.5 to 97.4) P<0.001
Emotional exhaustion 16 (11 861) 2.45 (1.71 to 3.53);
(0.63 to 9.62)
94 (91.8 to 95.6) P<0.001
Depersonalisation 12 (10 488) 2.93 (1.93 to 4.46);
(0.72 to 11.94)
93 (89.9 to 95.1) P=0.03
Personal accomplishment 9 (2992) 2.17 (1.36 to 3.46) 92 (87 to 95) NA
Patient safety incidents:
Burnout 35 (41 059) Favours patient safety incidents compared
with no patient safety incidents
2.04 (1.69 to 2.45);
(0.71 to 5.81)
87 (84 to 90) P=0.04
Emotional exhaustion 17 (20 213) 2.15 (1.82 to 2.53);
(1.19 to 3.86)
73 (56 to 83) P<0.001
Depersonalisation 14 (19 616) 2.44 (1.84 to 3.23);
(0.92 to 6.44)
90 (85 to 94) P<0.001
Personal accomplishment 14 (19 616) 1.47 (1.20 to 1.80);
(0.78 to 2.76)
87 (79 to 91) P<0.001
Patient satisfaction:
Burnout 8 (1,002) Favours lower patient satisfaction
compared with them being satised
2.22 (1.38 to 3.57) 75 (53.4 to 86.6) NA
Emotional exhaustion 5 (527) 2.79 (0.75 to 10.42) 77 (44.2 to 90.5) NA
Depersonalisation 6 (571) 3.82 (1.57 to 9.29) 81 (60 to 91) NA
Personal accomplishment 5 (527) 1.79 (1.14 to 2.81) 5 (0 to 80) NA
Results pooled using the standardised mean dierence are provided in appendix 11. No changes in signicance were found when pooling using standardised mean dierence. CI=condence
interval; PI=prediction intervals were calculated only for meta-analysis involving 10 or more studies as advised in Cochrane handbook; NA=Estimate not applicable.
*Fixed eect results were reported when fewer than ve studies were reported and the meta-analysis involved varied sample and eect sizes.
†Assessment of publication bias was done using Eggers’s test in all meta-analysis of 10 or more studies as advised in Cochrane handbook, and also checked using trim-and-ll method (see
appendix 13 for full results). Forest plots for each analysis are provided in appendix 10, where the log odds ratio estimates are also available within the plots.
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Study characteristics
77 (45%) of the 170 studies were conducted in the US,
48 (28%) in European countries, four (2%) of which
were in the UK, two (1%) in the African Region, eight
(5%) in the Region of the Americas, two (1%) in the
South-East Asian Region, three (2%) in the Eastern
Mediterranean Region, 29 (17%) in the Western Pacific
Region, and one (1%) multinational study.38-44 107
(63%) of the studies were based in a hospital setting, 33
(19%) involved mixed settings, 29 (17%) were based in
primary care setting, and one study45 was unclear but
involved medically qualified academics. We aimed to
exclude studies with fewer than 70% of responses from
physicians, however, in reality, only three (2%) of 170
studies with a mixed sample of physicians (70% and
over) and other health professionals were included in
our analyses.
The median number of physicians across studies
was 312 (interquartile range 162-1015 ) with a median
age of 42 years (32-48) and where data for sex were
reported, 112 (66%) studies involved mostly male
physicians. The physician specialty varied across
studies: 42 (25%) mixed specialties, 32 (19%) internal
medicine, 21 (12%) surgery (ie, trauma, plastic, and
neurosurgical), 19 (11%) emergency medicine and
intensive care, 11 (6%) general practitioners, eight
(5%) interns or residents, eight (5%) paediatrics,
seven (4%) oncology (ie, gynaecologist, radiation,
or palliative care), six (4%) neurology, three (2%)
psychiatry, and 13 (8%) involving other specialties.
Physicians had more than seven years of experience in
52 (31%) studies, a mixture of experience was reported
in 47 (28%) studies, and 38 (22%) studies involved
residents, junior doctors, or interns with fewer than
seven years of experience.
The most common measure of burnout was the
full 22-item Maslach Burnout Inventory (81 (48%) of
170 studies). An abbreviated version of the Maslach
Burnout Inventory was used in 50 (29%) studies, other
types were used in 34 (20%) studies and only five
Burnout
Attenello 2018
Baghdadi 2020
Dominguez 2019
Duan 2019
Estryn-Behar 2011
Goldberg 1996
Hamidi 2018
Hartwell 2010
Huang 2019
Karayurek 2021
Kassam 2021
Khalafallah 2020
Khorfan 2021
Lall 2020
O'Connor 2019
Pantenburg 2016
Rabatin 2016
Shanafelt 2009
Shanafelt 2014
Sinsky 2017
Soler 2007
Sun 2021
Voultsos 2020
Willard-Grace 2019
Zhang 2011
Fixed effects model
Random effects model
Heterogeneity: I2=97% (96-97%), τ2=0.49
4.54 (2.69 to 7.67)
3.09 (1.43 to 6.64)
4.05 (2.36 to 6.96)
5.44 (4.36 to 6.79)
1.43 (1.02 to 2.01)
2.64 (1.96 to 3.57)
2.40 (1.38 to 4.18)
4.44 (1.98 to 9.93)
4.05 (3.13 to 5.25)
1.40 (1.02 to 1.92)
64.00 (7.71 to 531.60)
4.57 (1.35 to 15.41)
2.26 (1.62 to 3.17)
5.20 (3.68 to 7.35)
16.24 (5.08 to 51.91)
1.05 (1.00 to 1.10)
4.94 (3.10 to 7.86)
2.28 (1.13 to 4.60)
2.17 (1.53 to 3.09)
2.16 (1.81 to 2.58)
1.83 (1.05 to 3.20)
10.07 (7.53 to 13.47)
2.67 (0.87 to 8.16)
1.57 (1.02 to 2.41)
1.98 (1.52 to 2.59)
1.44 (1.38 to 1.50)
3.10 (2.30 to 4.17)
0.01 0.1 10 1001
Study
Favours low
turnover
intention
Favours high
turnover
intention
Odds ratio
(95% CI)
Odds ratio
(95% CI)
1.51
1.13
1.40
1.69
0.36
0.97
0.88
1.49
1.40
0.33
4.16
1.52
0.82
1.65
2.79
0.05
1.60
0.82
0.77
0.77
0.61
2.31
0.98
0.45
0.68
TE
0.27
0.39
0.28
0.11
0.17
0.15
0.28
0.41
0.13
0.16
1.08
0.62
0.17
0.18
0.59
0.03
0.24
0.36
0.18
0.09
0.28
0.15
0.57
0.22
0.14
seTE
Fig | Association of physician burnout with turnover intention. TE=log odds ratio; seTE=standard error of log odds ratio; OR=odds ratio;
CI=condence interval
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(3%) studies used the Copenhagen burnout inventory
(see appendix 8 in supplement for breakdown of the
measures used). Thirty one (18%) studies reported
secondary measures of depression and 24 (14%)
studies reported emotional distress, which were
analysed separately. In terms of career engagement for
physicians, 81 (48%) studies reported on decreased job
satisfaction compared with increased job satisfaction,
19 (11%) on career choice regret compared with
being satisfied with career choice, three (2%)42 46-48
Emotional exhaustion
Blanchard 2010
Campbell 2001
Golub 2008
Gyorffy 2016, 2018
Hewitt 2020
Karayurek 2021
Khan 2018
Khorfan 2021
Makara-Studzinska 2020
Moreno-Jimenez 2012
Ochoa 2018
Pantenburg 2016
Pit 2014
Salles 2019
Soler 2007
Zhang 2011
Fixed effects model
Random effects model
Heterogeneity: I2=99% (99-99%), τ2=0.67
Depersonalisation
Blanchard 2010
Campbell 2001
Gyorffy 2016, 2018
Hewitt 2020
Karayurek 2021
Khan 2018
Khorfan 2021
Pantenburg 2016
Salles 2019
Soler 2007
Zhang 2011
Fixed effects model
Random effects model
Heterogeneity: I2=99% (98-99%), τ2=0.27
Personal accomplishment
Karayurek 2021
Khorfan 2021
Pantenburg 2016
Soler 2007
Zhang 2011
Fixed effects model
Random effects model
Heterogeneity: I2=86% (70-94%), τ2=0.04
2.75 (1.64 to 4.64)
8.12 (5.77 to 11.42)
2.02 (1.37 to 2.97)
1.58 (1.44 to 1.74)
1.39 (1.28 to 1.52)
1.19 (0.80 to 1.77)
2.18 (1.62 to 2.94)
2.98 (2.45 to 3.63)
1.08 (0.72 to 1.61)
11.69 (7.90 to 17.28)
5.44 (3.22 to 9.18)
1.09 (1.08 to 1.10)
2.45 (0.93 to 6.45)
18.97 (9.32 to 38.61)
3.36 (3.14 to 3.59)
2.35 (1.94 to 2.84)
1.13 (1.12 to 1.14)
2.81 (1.80 to 4.40)
1.39 (0.84 to 2.30)
3.26 (2.39 to 4.45)
1.40 (0.93 to 2.11)
1.35 (1.24 to 1.47)
1.01 (0.68 to 1.51)
1.67 (1.24 to 2.24)
2.39 (1.96 to 2.91)
1.01 (0.99 to 1.03)
7.28 (3.88 to 13.65)
1.70 (1.63 to 1.77)
2.19 (1.81 to 2.65)
1.14 (1.12 to 1.16)
1.82 (1.26 to 2.62)
1.58 (1.06 to 2.36)
1.59 (1.23 to 2.05)
1.04 (1.02 to 1.06)
1.08 (1.00 to 1.16)
1.52 (1.26 to 1.83)
1.05 (1.03 to 1.07)
1.28 (0.98 to 1.68)
0.01 0.1 10 1001
Study
Favours low
turnover
intention
Favours high
turnover
intention
Odds ratio
(95% CI)
Odds ratio
(95% CI)
1.01
2.09
0.70
0.46
0.33
0.17
0.78
1.09
0.07
2.46
1.69
0.09
0.90
2.94
1.21
0.85
0.33
1.18
0.34
0.30
0.01
0.51
0.87
0.01
1.98
0.53
0.78
0.46
0.46
0.04
0.08
0.42
TE
0.27
0.17
0.20
0.05
0.04
0.20
0.15
0.10
0.20
0.20
0.27
0.00
0.49
0.36
0.03
0.10
0.26
0.16
0.21
0.04
0.20
0.15
0.10
0.01
0.32
0.02
0.10
0.20
0.13
0.01
0.04
0.10
seTE
Fig | Association of emotional exhaustion, depersonalisation, and personal accomplishment of physicians with turnover intention. TE=log odds
ratio; seTE=standard error of log odds ratio; OR=odds ratio; CI=condence interval
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poor career development compared with good career
development, nine (5%)47 49-59 on reduced productivity
compared with sustained productivity, and 36 (21%)
on turnover intention compared with retention.
Concerning quality of patient care outcomes, 39 (23%)
studies reported patient safety incidents compared
with no patient safety incidents, 43 (25%) reported
indicators of low professionalism compared with
maintained professionalism, and eight (5%) studies
reported measures of patient dissatisfaction compared
with satisfied patients. Nineteen (11%) studies
reported more than one of these outcomes.
Of the 119 (70%) studies reporting career
engagement, all were self-reported by the physician.
Physicians self-reported across most of the studies
for patient safety incidents (31 (79%) of 39) and
professionalism (37 (80%) of 46 studies), whereas
the remaining studies used patient record reviews and
surveillance systems. Patient satisfaction was based
on self-reports by patients.
Burnout
Baer 2017
Brunsberg 2019
Coombs 2019, 2020
Dirvar 2020
Faivre 2018
Frahrenkopt 2008
Garrouste-Orgeas 2015
Grover 2018
Hansen 2011
Hayashino 2012
Huang 2019
Kang 2013
Kassam 2020
Kemper 2020
Klein 2010
Kwah 2017
Linzer 2009
Linzer 2017
Lu 2015
Nwosu 2020
O'Connor 2017
Prins 2010
Qureshi 2015
Shanafelt 2010
Sulaiman 2017
Tawfik 2018
Trockel 2018
Vanhaecht 2019
Voultsos 2020
Watson 2018
Welp 2015
Wen 2016
West 2006
West 2009
Williams 2007
Fixed effects model
Random effects model
Heterogeneity: I2=87% (84-90%), τ2=0.26
7.10 (1.98 to 25.50)
1.03 (0.71 to 1.49)
2.12 (1.16 to 3.89)
1.36 (1.17 to 1.59)
8.90 (1.35 to 58.79)
0.72 (0.35 to 1.52)
2.07 (1.27 to 3.38)
1.18 (0.75 to 1.88)
0.83 (0.56 to 1.22)
1.69 (1.36 to 2.10)
5.36 (4.11 to 6.99)
2.48 (1.57 to 3.92)
2.08 (0.50 to 8.66)
2.55 (1.94 to 3.36)
1.94 (1.39 to 2.70)
0.35 (0.05 to 2.23)
1.06 (0.72 to 1.57)
1.44 (0.93 to 2.22)
2.89 (1.13 to 7.42)
6.22 (4.70 to 8.24)
2.75 (1.78 to 4.24)
1.93 (1.76 to 2.11)
1.89 (1.56 to 2.28)
1.10 (0.76 to 1.58)
1.90 (1.47 to 2.46)
3.25 (2.70 to 3.92)
2.88 (1.80 to 4.62)
2.81 (2.21 to 3.56)
4.16 (0.87 to 19.84)
1.97 (1.33 to 2.91)
2.07 (1.58 to 2.71)
2.28 (1.63 to 3.18)
2.44 (1.77 to 3.37)
2.56 (2.02 to 3.24)
1.61 (1.14 to 2.28)
2.05 (1.96 to 2.15)
2.04 (1.69 to 2.45)
0.1 0.5 210
1
Study
Favours no
patient safety
incidents
Favours
patient safety
incidents
Odds ratio
(95% CI)
Odds ratio
(95% CI)
1.96
0.03
0.75
0.31
2.19
-0.32
0.73
0.17
-0.19
0.52
1.68
0.91
0.73
0.94
0.66
-1.05
0.06
0.36
1.06
1.83
1.01
0.66
0.64
0.09
0.64
1.18
1.06
1.03
1.43
0.68
0.73
0.82
0.89
0.94
0.48
TE
0.65
0.19
0.31
0.08
0.96
0.38
0.25
0.24
0.20
0.11
0.14
0.23
0.73
0.14
0.17
0.95
0.20
0.22
0.48
0.14
0.22
0.05
0.10
0.19
0.13
0.09
0.24
0.12
0.80
0.20
0.14
0.17
0.17
0.12
0.18
seTE
Fig | Association of burnout with patient safety incidents. TE=Log odds ratio; seTE=standard error of log odds ratio; OR=odds ratio; CI=condence
interval
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Emotional exhaustion
Bressi 2009
Dirvar 2020
Hayashino 2012
Kang 2013
O'Connor 2017
Prins 2010
Prudenzi 2021
Shanafelt 2010
Sulaiman 2017
Tawfik 2018
Torppa 2015
Trockel 2018
Watson 2018
Welp 2015
West 2006
West 2009
Yost 2014
Fixed effects model
Random effects model
Heterogeneity: I2=73% (56-83%), τ2=0.07
Depersonalisation
Dirvar 2020
Hayashino 2012
Kang 2013
O'Connor 2017
Prins 2010
Shanafelt 2010
Sulaiman 2017
Tawfik 2018
Trockel 2018
Watson 2018
Welp 2015
West 2006
West 2009
Yost 2014
Fixed effects model
Random effects model
Heterogeneity: I2=90% (85-94%), τ2=0.18
Personal accomplishment
Dirvar 2020
Hayashino 2012
Kang 2013
O'Connor 2017
Prins 2010
Shanafelt 2010
Sulaiman 2017
Tawfik 2018
Trockel 2018
Watson 2018
Welp 2015
West 2006
West 2009
Yost 2014
Fixed effects model
Random effects model
Heterogeneity: I2=87% (79-91%), τ2=0.08 0.1 0.5 2 10
1
Favours no
patient safety
incidents
Favours
patient safety
incidents
1.24 (0.56 to 2.79)
2.02 (1.55 to 2.65)
1.68 (1.16 to 2.43)
3.35 (1.50 to 7.46)
2.16 (1.16 to 4.00)
2.10 (1.79 to 2.46)
1.61 (0.77 to 3.36)
1.32 (1.15 to 1.52)
2.01 (1.29 to 3.14)
2.64 (2.23 to 3.13)
2.15 (0.97 to 4.80)
2.36 (1.48 to 3.76)
6.17 (2.78 to 13.70)
2.55 (1.59 to 4.10)
2.43 (1.39 to 4.26)
2.66 (1.77 to 4.00)
2.18 (0.74 to 6.44)
1.95 (1.81 to 2.10)
2.15 (1.82 to 2.53)
1.17 (0.90 to 1.53)
2.72 (1.15 to 6.43)
2.85 (1.29 to 6.32)
5.89 (2.48 to 14.02)
3.00 (2.55 to 3.53)
1.32 (1.15 to 1.52)
2.16 (1.38 to 3.39)
3.33 (2.83 to 3.92)
5.63 (3.41 to 9.30)
2.45 (1.36 to 4.42)
1.80 (1.13 to 2.87)
2.47 (1.41 to 4.33)
3.05 (2.02 to 4.60)
1.16 (0.40 to 3.38)
2.20 (2.03 to 2.37)
2.44 (1.84 to 3.23)
1.06 (0.81 to 1.39)
1.61 (1.22 to 2.13)
1.62 (0.74 to 3.55)
2.09 (0.90 to 4.86)
1.20 (1.03 to 1.40)
0.76 (0.66 to 0.87)
1.59 (1.02 to 2.47)
2.03 (1.69 to 2.45)
1.24 (0.79 to 1.96)
1.59 (0.80 to 3.15)
1.94 (1.22 to 3.10)
2.43 (1.39 to 4.26)
2.07 (1.38 to 3.11)
1.39 (0.47 to 4.07)
1.24 (1.15 to 1.33)
1.47 (1.20 to 1.80)
Study Odds ratio
(95% CI)
Odds ratio
(95% CI)
0.22
0.71
0.52
1.21
0.77
0.74
0.48
0.28
0.70
0.97
0.77
0.86
1.82
0.94
0.89
0.98
0.78
0.16
1.00
1.05
1.77
1.10
0.28
0.77
1.20
1.73
0.90
0.59
0.90
1.12
0.15
0.06
0.48
0.48
0.74
0.18
-0.28
0.46
0.71
0.22
0.46
0.66
0.89
0.73
0.33
TE
0.41
0.14
0.19
0.41
0.31
0.08
0.38
0.07
0.23
0.09
0.41
0.24
0.41
0.24
0.29
0.21
0.55
0.14
0.44
0.41
0.44
0.08
0.07
0.23
0.08
0.26
0.30
0.24
0.29
0.21
0.55
0.14
0.14
0.40
0.43
0.08
0.07
0.23
0.10
0.23
0.35
0.24
0.29
0.21
0.55
seTE
Fig | Association of emotional exhaustion, depersonalisation, and personal accomplishment with patient safety incidents. TE=Log odds ratio;
seTE=standard error of log odds ratio; OR=odds ratio; CI=condence interval
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Quality assessment
One hundred and 30 studies provided a representative
sample of the target population (76% met criterion
1); 103 studies provided an ample sample size of
physicians (61% met criterion 2); 58 studies reported
a response rate of 60% or greater (34% met criterion
3); 25 studies satisfied low risk of bias for the
ascertainment of exposure mostly due to many of the
surveys being self-reported (15% met criterion 4); 100
of the studies adequately adjusted for confounding
factors (59% met criterion 5); 165 reported a low
risk of bias due to assessment of outcome (97% met
criterion 6); and 118 studies had used adequate
statistical tests and measures to report their findings
(69% met criterion 7). Overall, 32 (19%) of the studies
reported low risk of bias (total score 6-7), 23 (14%)
reported high risk of bias (total score: 0-2), and 115
(67%) studies reported medium risk of bias (total score
3-5). The full results of the Newcastle Ottawa critical
appraisals are presented in appendix 9.
Meta-analysis of association of burnout with career
engagement and quality of patient care
The results of all the meta-analyses are provided in
table 1. All forest plots for each outcome are available in
appendix 10. Only significant results are reported here.
Physician burnout was associated with almost
fourfold decreases in job satisfaction compared
with increased job satisfaction based on measures
of overall burnout (3.79, 95% confidence interval
3.24 to 4.43, I2=97%, k=73 studies, n=146 980
physicians), emotional exhaustion (4.81, 3.67 to 6.30,
I2=98%, k=33, n=22 699), depersonalisation (2.89,
2.37 to 3.53, I2=92%, k=30, n=22 002) and personal
accomplishment (2.88, 2.28 to 3.63, I2=93%, k=32,
n=27 374). Burnout was associated with threefold
increases in career choice regrets compared with being
satisfied with their career choice based on measures
of overall burnout (3.49, 2.43 to 5.00, I2=97%, k=16,
n=33 871) and emotional exhaustion (4.16, 3.34 to
5.19, I2=90%, k=4, n=2014). Burnout was associated
with up to threefold increases in turnover intention
compared with retention based on measures of overall
burnout (3.10, 2.30 to 4.17, I2=97%, k=25, n=32 271;
fig 3), emotional exhaustion (2.81, 1.80 to 4.40,
I2=99%, k=16, n=23 625), and depersonalisation
(1.82, 1.26 to 2.62, I2=99%, k=11, n=23 257; fig 4)
but no eect was seen for personal accomplishment.
Burnout was associated with small but significant
decreases in productivity compared with sustained
productivity based on measures of overall burnout
(1.82, 1.08 to 3.07, I2=83%, k=7, n=9581),
depersonalisation (1.23, 1.18 to 1.28, I2=96%, k=3,
n=2969) and personal accomplishment (1.53, 1.43
to 1.63, I2=97%, k=3, n=2969). Finally, only two
studies46-48 reported a significant pooled association
between overall burnout and career development
concerns compared with good career development
(3.77, 2.77 to 5.14, I2=0%, k=2, n=3411).
Physician burnout was associated with double
the risk of patient safety incidents compared with
no patient safety incidents based on measures of
overall burnout (odds ratio 2.04, 95% confidence
interval 1.69 to 2.45, I2=87%, k=35, n=41 059; fig 5),
emotional exhaustion (2.15, 1.82 to 2.53, I2=73%,
k=17, n=20 213), depersonalisation (2.44, 1.84
to 3.23, I2=90%, k=14, n=19 616), and personal
accomplishment (1.47, 1.20 to 1.80, I2=87%, k=14,
n=19 616; fig 6). Burnout was associated with more
than twofold decreases in professionalism compared
with maintained professionalism based on measures
of overall burnout (2.33, 1.96 to 2.70, I2=96%, k=40,
n=32 321), emotional exhaustion (2.45, 1.71 to 3.53,
I2=94%, k=16, n=11 861), depersonalisation (2.93,
1.93 to 4.46, I2=93%, k=12, n=10 488), and personal
accomplishment (2.17, 1.36 to 3.46, I2=92%, k=9,
n=2992). Burnout was also associated with up to
threefold decreases in patient satisfaction compared
with patients being satisfied based on measures of
overall burnout (2.22, 1.38 to 3.57, I2=75%, k=8,
n=1002), depersonalisation (3.82, 1.57 to 9.29,
I2=81%, k=6, n=571), and personal accomplishment
(1.79, 1.44 to 2.81, I2=5%, k=5, n=527). Publication
bias was found after visual inspection of funnel plots
and the test statistics for most comparisons were
significant (appendix 13).
Some studies had used dierent scales to measure
burnout, therefore, we also did analyses using
standardised mean dierence to account for measures
of dierent length (see forest plots in appendix 11).
However, we found no significant dierences in this
analysis and the results were consistent with those
reported when analysed with the odds ratio.
The subgroup meta-analyses for the dierent
measures of burnout used for the outcomes job
satisfaction, patient safety incident, professionalism,
and turnover intention is provided in appendix 8 in
supplement.
For job satisfaction, the abbreviated version of
Maslach Burnout Inventory provided the largest
association with burnout (odds ratio 4.62, 95%
confidence interval 3.21 to 6.65, I2=99%) and
smallest with the Copenhagen burnout inventory
(2.59, 2.22 to 3.01, I2=95%). The Copenhagen
inventory had the highest association of burnout
with patient safety incidents (3.59, 2.92 to 4.42,
I2=95%) and the abbreviated versions of the Maslach
Burnout Inventory had the lowest association (1.68,
1.16 to 2.43, I2=79%). The association between
burnout and low professionalism was greatest
when using an abbreviated version of the Maslach
Burnout Inventory (2.91, 1.65 to 5.13, I2=87%) and
lowest when using the Copenhagen inventory (1.89,
1.69 to 2.12, I2=43%). The association between
burnout and turnover intention was greatest when
other non-specific measures of burnout were used
(7.23, 5.93 to 3.18, I2=77%) and lowest when an
abbreviated version of Maslach Burnout Inventory
was used (2.53, 1.39 to 4.59, I2=98%). No significant
dierences were noted between the dierent burnout
measures when tested using the ratio of odds ratios
(appendix 8).
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Meta regressions
The results of the univariable and multivariable meta-
regression analyses are provided in appendix 12. In the
univariable regression results, a stronger association
of overall burnout with low job satisfaction was found
in physicians working in hospitals compared with
primary care settings (1.88, 0.91 to 3.86, P=0.09),
and more specifically in emergency medicine and
intensive care (2.16, 0.98 to 4.76, P=0.06) compared
with a general internal medicine specialty, and in
physicians older than the age of 50 years compared
with individuals aged 31-50 years (2.41, 1.02 to 5.64,
P=0.04). The association was weakest in GPs (0.16,
0.03 to 0.88, P=0.04). However, these associations
did not remain significant in the multivariable
regressions. The association between burnout and
patient safety incidents in the univariable regression
results was found to be larger in younger physicians
(20-30 years; 1.88, 1.07 to 3.29, P=0.03), working
in emergency medicine and intensive care settings
(2.10, 1.09 to 3.56, P=0.02), or in training based
in the Commonwealth region (3.03, 0.83 to 11.25,
P=0.09). The only association to remain significant
in the multivariable regression results was that found
in younger physicians (1.55, 0.94 to 2.56, P=0.08).
The univariable regression results of the association
of burnout with low professionalism was found to be
smaller in physicians aged older than 50 years (0.36,
0.19 to 0.69, P=0.003) and larger in physicians still
in training or residency (2.27, 1.45 to 3.60, P=0.001),
who worked in a hospital (2.16, 1.46 to 3.19, P<0.001),
specifically in the emergency medicine specialty and
intensive care (1.48, 1.01 to 2.34, P=0.04), or when
situated in a low to middle income country (1.68,
0.94 to 2.97, P=0.08). Multivariable regression results
show that the association remained significant in
middle aged physicians aged 31-50 years (0.45, 0.26
to 0.76, P=0.003), working in a hospital (3.82, 1.84 to
8.00, P<0.001), or specialising in cancer (0.25, 0.09
to 0.74, P=0.01) or neurology (0.22, 0.07 to 0.73,
P=0.01). The univariable regression results of the
association of burnout with career choice regret was
found to be largest in physicians with a specialisation
in emergency medicine and intensive care (2.89,
0.97 to 14.89, P=0.10) and neurology (2.52, 0.82 to
7.80, P=0.10). The association between burnout and
turnover intentions did not vary according to any other
factors included in the univariable regression analyses
(appendix 12). No significant associations were found
between burnout and job satisfaction.
Discussion
Principal ndings
This systematic review and meta-analysis provides
compelling evidence that physician burnout is
strongly associated with the career disengagement
of physicians and suboptimal patient care. However,
even after confirming the consistency of the data in
up 84 (49%) of 170 study authors, the results should
be considered in tandem with the large amount of
heterogeneity presented in all comparisons.
We found that physicians with burnout were up to
four times more likely to be dissatisfied with their job
compared with being satisfied with their job, three
times as likely to have thoughts or intentions to quit
their job (turnover) compared with job retention,
and three times as likely to regret their career choice
compared with being satisfied with their career choice.
Emotional exhaustion contributed most to increases
in the turnover intention of physicians compared with
retention. The association of physician burnout with
lower job satisfaction compared with increased job
satisfaction was more prevalent in older physicians
working in emergency medicine and intensive care.
Physicians with burnout are twice as likely to be
involved in patient safety incidents compared with no
patient safety incidents and show low professionalism
compared with maintained professionalism, and over
two times more likely to receive low satisfaction ratings
from patients compared with satisfied patient ratings.
The depersonalisation subscale of burnout appeared
to have the most adverse association with the quality
of care and patient dissatisfaction. Patient safety
incidents compared with no patient safety incidents
were more likely to occur in younger physicians
working in emergency medicine and intensive care.
Comparisons with similar research
No previous meta-analysis has examined the
association of burnout with the career engagement of
physicians. Only one review,60 predominately based on
studies of nurses, has linked inter-professional work
with employee outcomes. A number of systematic
reviews have assessed the association of burnout
with the quality of patient care, however, these
studies mostly included mixed samples of healthcare
professionals and rarely used meta-analysis due to
heterogeneous samples and outcomes.14 15 We chose
to focus on physicians because they are twice as
likely to experience burnout than any other worker,
including other healthcare professionals8 12 and
this choice has improved our confidence in using a
meta-analysis. Moreover, both career engagement of
physicians and quality of patient care were chosen
because these dimensions of health service quality are
complimentary and some of our outcomes including
low professionalism, low job satisfaction, and reduced
patient satisfaction, are precursors of safety risks with
potential to lead to active patient safety incidents and
have serious career implication on the physician.61
This balanced approach (to be comprehensive in terms
of outcomes but specific on physicians) was agreed
by our core research team involving physicians and
patients.
Policy implications
Many countries including the US and the UK have
described levels of physician burnout as the highest in
the history of health and care systems.2 62 63 Our findings
arm that physician burnout can be a catalyst for the
career disengagement of physicians and that burnout
is associated with unsafe patient care,64 65 which costs
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billions to healthcare systems annually.66-68 Physician
burnout deepens the workforce crisis and undermines
a fundamental societal need to be in receipt of safe care.
In line with our results, a survey from the US concluded
that physicians at the front line of care access are at
greatest risk of burnout, work longer hours, and
have greater struggles with work-life balance and job
satisfaction than other healthcare workers.69 These
factors often unite as a result of burnout, and can lead
to higher physician turnover rates,70 which in itself
has substantial costs in terms of both the interruption
in continuity of care relationships and high expense
associated with recruiting new clinicians and sta.71
Eective interventions that can curtail physician
burnout are needed now more than ever as health
and care systems across the globe are encountering a
workforce crisis.72 A range of eective interventions for
reducing burnout in physicians are available, including
interventions focusing on improving the culture on
healthcare organisations, interventions supporting
individual physicians through organisational funded
initiatives, and multicomponent interventions.21 73
We found that physicians with high scores of
depersonalisation are especially likely to be involved
in lower quality of patient care whereas physicians
with high scores of emotional exhaustion are especially
likely to express intentions to leave their job. Thus,
interventions targeting specific dimensions of burnout
could be oered to subgroups of physicians with career
concerns or adverse patient care experiences taking
also into consideration their reciprocal relationships
between burnout, career engagement, and quality of
patient care. For example, physicians experiencing
burnout might have less time or commitment to
optimise the care of their patients, can take more
unnecessary risks, or might lack accountability.74
Conversely, exposure to adverse patient events or
recognition of poor quality of care can result in
burnout, which in turn could force physicians to quit.
This process can often be referred to as secondary
trauma, particularly in relation to sentinel events or
important safety incidents.75
Our results highlight subgroups of physicians
with burnout who could be at particularly high risk
for career disengagement and provision of unsafe
patient care. These physicians are mainly frontline
physicians in emergency medicine and intensive care.
Unsurprisingly, reports from frontline physicians
advocate that the field of medicine is almost reaching
crisis point with an increasing number of physicians
working part time, resigning from their job, or retiring
early in response to excessive workload and symptoms
of burnout.6 76
Limitations
The large heterogeneity for some of the outcomes
such as, patient safety, professionalism, and job
satisfaction, might have been due to variations of
outcome definition. Despite this variation, we selected
these definitions based on theories and consultations
with stakeholders. For example, the outcome job
dissatisfaction includes many dierent aspects such as
poor work engagement, dissatisfaction with workload,
and poor relationships with patients. This diversity in
the outcome definition might lead to overestimating
the association with physician burnout, as the
prediction intervals (which conveniently express
heterogeneity) suggest. Therefore, the results should
be interpreted with this potential overestimation
in mind. Similarly, patient safety incidents often
originate from complex and interchangeable factors
including the dierent nature and types (preventable
or not), severity, dispensing stage, and systems used.77
Meaning that the observed meta-analytical association
with physician burnout might be more attributable
to general factors of the whole organisation or work
setting in healthcare.78 Additionally, our definition for
the patient safety incidents was broad and captured
any of the following incidents; potentially avoidable
readmission, prescribing errors, monitoring errors,
and potentially avoidable adverse events. Thus, owing
to the large variation in the possible cause of a safety
incident, we urge some caution when interpreting the
pooled eect sizes for patient safety incidents.
The tools or questionnaires used to assess these
above outcomes varied considerably and this variation
did not allow us to make any meaningful subgroup or
sensitivity analyses. Reaching consensus about a gold
standard set of tools to assess at least some of these
outcomes would be an important step for improving
the precision of the eect sizes in future meta-
analyses. Moreover, career engagement outcomes
have conceptual similarities with the personal
accomplishment subscale of burnout but exclusive
focus on only the other two subscales of burnout
(emotional exhaustion and depersonalisation)
could introduce more bias than omitting personal
accomplishment would avoid. Our findings call for
future studies to examine the causal and temporal
relations (eg, structural equation modelling) between
the dierent career engagement outcomes and the
three subscales of burnout.
We extracted and analysed the rawest available data
in each study where possible, standardised these data
using odds ratios (and standardised mean dierences),
and then performed several meta-regressions and
sensitivity analyses to validate the findings. Despite
these precautions, some degree of imprecision is still
possible in the pooled eect sizes driven by variations
in the aggregate data that we used. Accessing
individual participant data could considerably
improve the precision of the eect sizes, which we
strongly recommend in future research.
Although the focus of this investigation was on
physicians, this population should still be considered
as working in various settings and specialties. We
performed meta-regressions, which did explain
some of the heterogeneity due to specialty area, but
because of the low numbers of participants in some
groups, these meta-regressions had to be combined
into hierarchical categories of healthcare settings
or specialties, which could conflate some findings.
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Furthermore, because fewer patient safety incidents
were found in studies with response rates above 70%,
this might have attributed to possible bias in studies
with lower response rates.79
Our protocol amendment resulted in excluding grey
literature from this review. Although the exclusion
of this type of literature could actually lead to an
increase in publication bias80; the sheer high volume
of additional grey literature (eg, mostly engagement
surveys by medical associations or colleges and
universities) were of poor quality and provided
limited, if any, association data that could be used
in a meta-analysis. Thus, we have captured the
highest quality of evidence providing meta-analytical
pertinent data. However, peer reviewed literature
is likely to be subject to some exaggeration of the
association of burnout when assessed against similar
patient care outcomes.10 Also, only English language
publications were included so other studies could
have been missed.
Sensitivity analysis of the reporting method (ie,
by physician or patient surveillance system) was not
possible because more than 79% of the reports were
self-reports by physicians for both patient safety
incidents and low professionalism. The design of the
original studies (mostly cross-sectional) inevitably
imposes limits on our ability to establish causal links
between physician burnout and patient care or career
engagement, and the mechanisms that underpin these
links.81 However, with only 20 prospective cohort or
longitudinal studies, assessment of direct causality
would not have been feasible in this study. Finally,
method bias is a common problem in cross-sectional
studies, when measuring one or more constructs
with the same method it can have significant eects
on the relationship between them.82 Method bias can
influence inventory validation and reliabilities as well
as the covariation between latent constructs, such as
measures of physician wellness. Thus, researchers need
to be knowledgeable about ways to control for method
biases thorough the use of more suited statistical
remedies, such as structural equational models, which
provide an explicit assessment of measurement error
and estimation of latent constructs.83
Conclusions
Burnout is a strong predictor for career disengagement
in physicians as well as for patient care. Moving
forward, investment strategies to monitor and improve
physician burnout are needed as a means of retaining
the healthcare workforce and improving the quality
of patient care. Scalable implementation of eective
interventions for physician burnout, such as those
improving the culture of healthcare organisations, and
multicomponent interventions are strongly supported
by our findings.21 73
AUTHOR AFFILIATIONS
1National Institute for Health and Care Research (NIHR) School
for Primary Care Research, Division of Population Health, Health
Services Research and Primary Care, School of Health Sciences,
Faculty of Biology, Medicine and Health, Manchester Academic
Health Science Centre, University of Manchester, Manchester, UK
2School of Psychology, University of Leeds, Leeds, UK
3Bradford Institute for Health Research, Bradford Royal Inrmary,
Bradford, UK
4School of Health Sciences, Faculty of Health and Medical Sciences,
University of Surrey, Guildford, UK
5School of Clinical Medicine, University of Cambridge, Cambridge,
UK
6Laboratory of Hygiene, Aristotle Medical School, Aristotle University
of Thessaloniki, Thessaloniki, Greece
7School of Medicine, Keele University, Keele, Newcastle, UK
8Westminster Centre for Resilience, Faculty of Science and
Technology, University of Westminster, London, UK
9National Institute for Health Research Greater Manchester Patient
Safety Translational Research Centre, Division of Population
Health, Health Services Research and Primary Care, University of
Manchester, Manchester, UK
We thank the following for conrming their study data: Colin West,
Mark Linzer, Daniel S Tawk, Sami Abdo Radman Al-Dubai, Hamzeh
Mohammad Alrawashdeh, Ala’a B Al-Tammemi, Tamara Elizabeth
Baer, Suphi Bahadirli, Pierre Blanchard, Tom Bourne, Katherine A
Brunsberg, Neil A Busis, Demetrius M Coombs, Risal Djohan, Mao
Ding, Oksana Babenko, Sergio Dominguez-Lara, Ingrid Gilles, Justin
S Golub, Michael M Johns III, Zsuzsa Győry, Rikke P Hansen, D Brock
Hewitt, Karl Y Bilimoria, Matthew R Janko, Matthew R Smeds, Hakan
Demirci, Julie Welch, Kathi J Kemper, Debraj Mukherjee, Atir Khan, Jens
Klein, Michael Kriss, Henry M Kuerer, Shailesh Kumar, Jason Kwah,
Michelle D Lall, John Leung, Kerry H Levin, Dave W Lu, Laxmi S Mehta,
Leonardo Potenza, Maha Sulaiman Younis, Bernardo Moreno-Jiménez,
Paul O’Connor, Alec O’Connor, Geon Ho Bahn, Sabrina Pit, Jelle T
Prins, Vinay Rawlani, Ashim Roy, Arghavan Salles, Wilmar B Schaufeli,
Christine A Sinsky, James A Sliwa, Leyla Ozturk Sonmez, Colm M P
O’Tuathaigh, Lois J Surgenor, Hyo Jung Tak, Okan Taycan, Yasuharu
Tokuda, Wakako Umene-Nakano, Deborah Seys, David Ring, Krzysztof
A Tomaszewski, Lars Peterson, Alexander G Watson, Matthias Weigl,
Hui-Ching Weng, Robin R Whitebird, Rachel Willard-Grace, James
Gardner Wright, Xiaowei Zhang, Morgan G Yost, Sandeep Grover, and
Peng Xie.
Contributors: MP, AH, and AE had the initial research idea and
obtained funding for this study. AH, MP, AE formulated the research
questions and designed the study. AH searched for published
work, selected articles, extracted and analysed data, and draed
the protocol and manuscript. MP, AZ, JJ, RR, AZ, and KG helped in
the searching of articles and data selection and extraction. Data
extraction was conrmed by 49% of the authors. MP substantially
contributed to designing the searches and the statistical analysis plan,
writing of the manuscript, and interpreting the ndings. AE, CAC-G, JJ,
DP, and RR substantially contributed to the manuscript by providing
review comments and edits. AH is the guarantor. All authors have read
and approved the nal manuscript. The corresponding author attests
that all listed authors meet authorship criteria and that no others
meeting the criteria have been omitted.
Funding: This study is funded by the UK National Institute for Health
Research (NIHR) School for Primary Care Research (project 411). AH is
also funded by the University of Manchester through his Presidential
Fellowship. The NIHR Greater Manchester Patient Safety Translational
Research Centre funded MP’s time contributed to this project.
CCG would also like to acknowledge the NIHR Applied Research
Collaboration West Midlands who is responsible for partly funding her
research. The study funder had no role in the design and conduct of
the study; collection, management, analysis, or interpretation of the
data; preparation, review, or approval of the manuscript; or decision to
submit the manuscript for publication. The views expressed are those
of the author(s) and not necessarily those of the UK National Institute
for Health Research or the Department of Health and Social Care.
Competing interests: All authors have completed the ICMJE
uniform disclosure form at www.icmje.org/coi_disclosure.pdf and
declare: support from the UK National Institute for Health and Care
Research School for Primary Care Research for the submitted work;
no nancial relationships with any organisations that might have an
interest in the submitted work in the previous three years; no other
relationships or activities that could appear to have influenced the
submitted work.
Ethical approval: Not required in this study.
Data sharing: Because this meta-analysis was based on data
extracted from previously published research, most of the data and
study materials are available in the public domain. However, the
raw data extractions, transformed data sheets and author emails
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conrming the data will be made available and will be published on
Mendeley.
The lead author AH (the manuscript’s guarantor) arms that the
manuscript is an honest, accurate, and transparent account of the
study being reported; that no important aspects of the study have
been omitted; and that any discrepancies from the study as planned
(and, if relevant, registered) have been explained.
Dissemination to participants and related patient and public
communities: The results will be presented at this year’s Annual
Society of Academic Primary Care (SAPC) Annual Scientic Meeting
2022 (https://sapc.ac.uk/conference/2022) and at the WELL-Med’s
4th International Meeting on Well Being and Performance in Clinical
Practice (https://www.well-med.gr). The results will also be shared
through press releases through the aliated universities of the
author’s involved, social medial including twitter and with funders
including the NIHR SPCR.
Provenance and peer review: Not commissioned; externally peer
reviewed.
This is an Open Access article distributed in accordance with the
Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license,
which permits others to distribute, remix, adapt, build upon this work
non-commercially, and license their derivative works on dierent
terms, provided the original work is properly cited and the use is non-
commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.
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