Assignment - Assignment Essays

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Week 3 Lab Assignment

Name:________________________ Instructor Name: _______________

Please use this template to help answer the questions listed in the lab instructions. The “parts” below refer to the parts listed in the lab instructions. Type your answers and post your screenshots in the spaces given below. Then, save this document with your name and submit it inside the course room.

Part 1. Read the assigned article.

Please reach out to your instructor if you did not receive the assigned article for the term by Monday of Week 3.

Part 2. Analyze the article.

Title: Review of [Type out name of Article]

Author(s): [Type out names of Author(s) of the Article]

Summarize the article in one paragraph:

Post a screenshot of a graph/chart from the article that you will analyze:

Analysis

(Answer the following questions thoroughly in complete sentences)

A. What

type
of study is used in the article (quantitative or qualitative)?

Explain
how you came to that conclusion.

B. What

type
of graph or table did you choose for your lab (bar graph, histogram, stem & leaf plot, etc.)? What characteristics make it this type (you should bring in material that you learned in the course)?

C.
Describe the data displayed in your frequency distribution or graph (consider class size, class width, total frequency, list of frequencies, class consistency, explanatory variables, response variables, shapes of distributions, etc.)

D.
Draw a conclusion about the data from the graph or frequency distribution in context of the article.

E. How else might this data have been displayed (Pick two different graphs that could have been used to display the same data as your selected graph/table)?

Discuss

pros and cons
of
2 other presentation options, such as tables or different graphical displays.

Explain
how these graphs would be structured to display the data in the article. Why don’t you think those two graphs were not used in this article?

F. Give the full APA
reference of the article you are using for this lab.

Be sure your name is on the Word document, save it, and then submit it. In the assignment module, click “start assignment” and then “upload file” and “submit assignment”.

image1

Evidence Relating Health Care Provider Burnout and Quality of Care
A Systematic Review and Meta-analysis
Daniel S. Tawfik, MD, MS; Annette Scheid, MD; Jochen Profit, MD, MPH; Tait Shanafelt, MD; Mickey Trockel, MD, PhD;
Kathryn C. Adair, PhD; J. Bryan Sexton, PhD; and John P.A. Ioannidis, MD, DSc

Background: Whether health care provider burnout contrib-
utes to lower quality of patient care is unclear.

Purpose: To estimate the overall relationship between burno

and quality of care and to evaluate whether published studies
provide exaggerated estimates of this relationship.

Data Sources: MEDLINE, PsycINFO, Health and Psychosocial
Instruments (EBSCO), Mental Measurements Yearbook (EBSCO),
EMBASE (Elsevier), and Web of Science (Clarivate Analytics),
with no language restrictions, from inception through 28 May
2019.

Study Selection: Peer-reviewed publications, in any language,
quantifying health care provider burnout in relation to quality of
patient care.

Data Extraction: 2 reviewers independently selected studies,
extracted measures of association of burnout and quality of care,
and assessed potential bias by using the Ioannidis (excess signif-
icance) and Egger (small-study effect) tests.

Data Synthesis: A total of 11 703 citations were identified, from
which 123 publications with 142 study populations encompass-
ing 241 553 health care providers were selected. Quality-of-care
outcomes were grouped into 5 categories: best practices (n =
14), communication (n = 5), medical errors (n = 32), patient out-

comes (n = 17), and quality and safety (n = 74). Relations be-
tween burnout and quality of care were highly heterogeneous
(I2 = 93.4% to 98.8%). Of 114 unique burnout–quality combina-
tions, 58 indicated burnout related to poor-quality care, 6 indi-
cated burnout related to high-quality care, and 50 showed no
significant effect. Excess significance was apparent (73% of stud-
ies observed vs. 62% predicted to have statistically significa

results; P = 0.011). This indicator of potential bias was most
prominent for the least-rigorous quality measures of best prac-
tices and quality and safety.

Limitation: Studies were primarily observational; neither causal-
ity nor directionality could be determined.

Conclusion: Burnout in health care professionals frequently is
associated with poor-quality care in the published literature. The
true effect size may be smaller than reported. Future studies
should prespecify outcomes to reduce the risk for exaggerated
effect size estimates.

Primary Funding Source: Stanford Maternal and Child Health
Research Institute.

Ann Intern Med. 2019;171:555-567. doi:10.7326/M19-1152 Annals.org
For author affiliations, see end of text.
This article was published at Annals.org on 8 October 2019.

Health care providers face a rapidly changing land-
scape of technology, care delivery methods, and

regulations that increase the risk for professional burn-
out. Studies suggest that nearly half of health care pro-
viders may have burnout symptoms at any given time
(1). Burnout has been linked to adverse effects, includ-
ing suicidality, broken relationships, decreased produc-
tivity, unprofessional behavior, and employee turnover,
at both the provider and organizational levels (2–6).

Recent attention has been focused on the relati

between health care provider burnout and reduced
quality of care, with a growing body of primary litera-
ture and systematic reviews reporting associations be-
tween burnout and adherence to practice guidelines,
communication, medical errors, patient outcomes, and
safety metrics (7–11). Most studies in this field use ret-
rospective observational designs and apply a wide
range of burnout assessments and analytic tools to
evaluate myriad outcomes among diverse patient pop-
ulations (12). This lack of a standardized approach to
measurement and analysis increases risk of bias, poten-
tially undermining scientific progress in a rapidly ex-
panding field of research by hampering the ability to
decipher which of the apparent clinically significant re-
sults represent true effects (13). The present analysis
sought to appraise this body of primary and review lit-
erature, developing an understanding of true effects

within the field by using a detailed evaluation for re-
porting biases.

Reporting biases take many forms, each contribut-
ing to overrepresentation of “positive” findings in the
published literature. Publication bias occurs when stud-
ies with negative results are published less frequently
or less rapidly than those with positive results (14). Se-
lective outcome reporting occurs when several out-
comes of potential interest are evaluated, but only
those with positive results are presented or empha-
sized (13). Selective analysis reporting occurs when
several analytic strategies are used, but those that pro-
duce the largest effects are presented. Overall, these
biases result in an excess of statistically significant re-
sults in the published literature, threatening reproduc-
ibility of findings, promoting misappropriation of re-
sources, and skewing the design of studies assessing
interventions to reduce burnout or improve quality (13).

See also:

Editorial comment . . . . . . . . . . . . . . . . . . . . . . . . . 589

Web-Only
Supplement

Annals of Internal Medicine REVIEW

http://www.annals.org

METHODS
We conducted a systematic literature review and

meta-analysis to provide summary estimations of the
relation between provider burnout and quality of care,
estimate study heterogeneity, and explore the potential
of reporting bias in the field. We followed the PRISMA
(Preferred Reporting Items for Systematic reviews and
Meta-Analyses) and MOOSE (Meta-analysis of Observa-
tional Studies in Epidemiology) guidelines for method-
ology and reporting (15, 16).

Data Sources and Searches
We searched MEDLINE, PsycINFO, Health and Psy-

chosocial Instruments (EBSCO), Mental Measurements
Yearbook (EBSCO), EMBASE (Elsevier), and Web of Sci-
ence (Clarivate Analytics) from inception through 28
May 2019, with no language restrictions. We used
search terms for burnout and its subdomains (emo-
tional exhaustion, depersonalization, and reduced per-
sonal accomplishment), health care providers, and
quality-of-care markers, as shown in Supplement Ta-
bles 1 to 3 (available at Annals.org).

Study Selection
We included all peer-reviewed publications report-

ing original investigations of health care provider burn-
out in relation to an assessment of patient care quality.
Providers included all paid professionals delivering
outpatient, prehospital, emergency, or inpatient care,
including medical, surgical, and psychiatric care, to pa-
tients of any age. We chose an inclusive method of
identifying burnout studies, considering assessments to
be related to burnout if the authors defined them as
such and used any inventory intended to identify burnout,
either in part or in full. Likewise, we chose an inclusive
approach to identify quality-of-care metrics, including any
assessment of processes or outcomes indicative of care
quality. We included objectively measured and subjec-
tively reported quality metrics originating from the pro-
vider, other sources within the health care system, or pa-
tients and their surrogates. We considered medical
malpractice allegations a subjective patient-reported
quality metric. Although patient satisfaction is an impor-
tant outcome, it is not consistently indicative of care qual-
ity or improved medical outcomes, suggesting that it may
be related to factors outside the provider’s immediate
control, such as facility amenities and access to care (17–
20). Thus, for the purposes of this review, we excluded
metrics solely indicative of patient satisfaction to reduce
bias from these non–provider-related factors that may af-
fect satisfaction.

We included peer-reviewed, indexed abstracts if
they reported a study population not previously or sub-
sequently reported in a full-length article. For study
populations described in more than 1 full-length arti-
cle, we included the primary result from the paper with
the earliest publication date as the primary outcome,
with any unique outcomes from subsequent articles as
secondary outcomes. We supplemented the database
searches with manual bibliography reviews from in-
cluded studies and related literature reviews (7–9, 21–

24). In line with our aim to look for reporting bias, we
did not expand our search beyond peer-reviewed pub-
lications and did not contact authors for unpublished
data. If an article presented insufficient data to calculate
an effect size, we supplemented the information with
data from subsequent peer-reviewed publications
when available; however, we still attributed these effect
sizes to the initial report. We excluded any studies that
were purely qualitative.

All investigators contributed to the development of
study inclusion and exclusion criteria. The literature re-
view and study selection were conducted by 2 inde-
pendent reviewers in parallel (D.S.T. and either A.S. or
K.C.A.), with ambiguities and discrepancies resolved by
consensus.

Data Extraction and Quality Assessment
We extracted data into a standard template reflect-

ing publication characteristics, methods of assessing
burnout and quality metrics, and strength of the re-
ported relationship. Data were extracted by 2 indepen-
dent reviewers (D.S.T. and A.S.), with discrepancies re-
solved by consensus. We estimated effect sizes and
precision using the Hedges g and SEs, respectively.
The Hedges g estimates effect size similarly to the Co-
hen d, but with a bias correction factor for small sam-
ples. In general, 0.2 indicates small effect; 0.5, medium
effect; and 0.8, large effect.

We classified each assessment of burnout as over-
all burnout, emotional exhaustion, depersonalization,
or low personal accomplishment. We also identified
burnout assessments as standard if defined as an emo-
tional exhaustion score of 27 or greater or a deperson-
alization score of 10 or greater on the Maslach Burno

Inventory, or as the midpoint and higher on validated
single-item scales. We categorized quality metrics within
5 groups—best practices, communication, medical errors,
patient outcomes, and quality and safety—and reverse
coded any “high-quality” metrics such that positive effect
sizes indicate burnout’s relation to poor-quality care.

For publications with several distinct (nonover-
lapping) study populations reported separately, we con-
sidered each population separately for analytic purposes.
For publications with more than 1 outcome for the same
study population, we decided to perform analyses using
only 1 outcome per study, ideally the specified primary
outcome. If no primary outcome was clear, we chose the
first-listed outcome, consistent with reporting conventions
of presenting the primary outcome first. We considered
other outcomes secondary, excluding them from the pri-
mary analyses to avoid bias from intercorrelation but in-
cluding them in selected descriptive statistics and strati-
fied analyses when appropriate.

Data Synthesis and Analysis
We calculated the Hedges g from odds ratios (di-

chotomized data) by using the transformation

log�OR�*
�3

�
or from correlation coefficients (unscaled

continuous data) by using the transformation
2*r

�1 � r2
,

REVIEW Burnout and Quality of Care

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both multiplied by a bias correction factor �N � 2

N
con-

sistent with published norms (25, 26). Further details
are provided in the Supplement (available at Annals
.org).

Most studies reported burnout as a dichotomous
variable or with unscaled effect size estimates, facilitat-
ing the aforementioned transformations. We scaled ef-
fect sizes accordingly for the 6 studies reporting burn-
out only as a continuous variable in order to maintain
comparability, adapting our methods from published
guidelines (27, 28). On the basis of known distributions
of burnout scores among providers (29–31), we calcu-
lated the difference between the mean scores of pro-
viders with and without burnout to average 47.6% of
the span of the particular burnout scale used. We thus
converted effect sizes from continuous scales to the
corresponding effect size reflecting a 47.6% change in
scale score when needed to extrapolate to dichoto-
mized burnout. We also performed sensitivity analyses
excluding these few scaled effect sizes. Details of this
process are presented in the Supplement.

Initially, we intended to primarily perform a
random-effects meta-analysis including all primary (or
first-listed) effect sizes, with secondary meta-analyses
stratified by quality metric category and by each unique
burnout–quality metric combination. However, because
of high heterogeneity in the pooled meta-analyses, we
report only summary effects from the unique burnout–
quality metric combinations. We also performed sensi-
tivity analyses limited to studies with standard burnout
assessments and those with independently observed or
objectively measured quality-of-care markers. We used
the empirical Bayes method with Knapp–Hartung mod-

ification to estimate the between-study variance �2 (32).
We evaluated study heterogeneity using I2. Details re-
garding this meta-analytic approach are presented in
the Supplement.

We performed the Ioannidis test to evaluate for ex-
cess significance (33) by identifying the study popula-
tion with the highest precision (1/SE) among those with
the lowest risk of bias (studies using a fully validated
burnout inventory with an objective quality metric). We
then calculated the power of all studies to detect the
effect size of this study and compared the observed
versus expected number of studies with statistically sig-
nificant results by using paired t tests. Next, we strati-
fied excess significance testing by outcome category.

Because small studies may carry increased risk of
bias, we performed the Egger test to look for small-
study effects (34). We regressed standard normal devi-
ate (Hedges g/SE) on precision (1/SE) by using robust
SEs due to clustering of effect sizes at the study popu-
lation level.

We used Stata 15.0 (StataCorp) for all analyses. All
tests were 2-sided. For summary effects, we considered
2 different thresholds of statistical significance, P < 0.050 and the newly proposed P < 0.005 (35, 36). We made no further corrections for multiple testing.

This study was performed in accordance with the
institutional review board requirements of Stanford
University and was classified as research not involving
human subjects.

Role of the Funding Source
The funders had no role in study design, data col-

lection, analysis, interpretation, or writing of the report.

Figure 1. Evidence search and selection.

Articles identified in MEDLINE
and PsyclNFO (n = 6715)

Articles identified in Web of
Science (n = 3116)

Articles identified in
EMBASE (n = 3871)

Duplicate publications (n = 1999)

Titles/abstracts screened (n = 11 703)

Not relevant (n = 11 390)

Selected for full-text review (n = 313)

Bibliographic reviews (n = 3)

Included in final analysis (n = 123)

Excluded (n = 193)
No burnout predictor: 123
No quality outcome: 46
Review/repeat population: 16
Not quantitative: 7
Not health care providers: 1

Burnout and Quality of Care REVIEW

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557

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RESULTS
The search identified 11 703 citations. Screening

resulted in 313 potentially eligible publications re-
trieved in full text—120 of which were included—plus 3
additional publications identified by bibliography re-
view (Figure 1). Overall, we included 123 publications
from 1994 through 2019 (37–159), encompassing 142
distinct study populations, as detailed in Supplement
Table 4 (available at Annals.org). The median sample
size was 376 (interquartile range, 129 to 1417). The 142

study populations included physicians (n = 71 [50%]),
nurses (n = 84 [59%]), and other providers (n = 18
[13%]) for a total of 241 553 health care providers eval-
uated. Quality metrics covered inpatients (n = 122
[86%]); outpatients (n = 62 [44%]); and adult (n = 134
[94%]), pediatric (n = 93 [65%]), medical (n = 135
[95%]), and surgical (n = 89 [63%]) patients. Only 4
studies explicitly specified a primary outcome. Six stud-
ies did not provide sufficient data to derive an effect
size from the original publication but provided usable

Figure 2. Summary of all included burnout–quality metric combinations, showing frequency of effect size reporting (count)
and value of summary effect size (Hedges g).

Burnout Metric

Bur
no

Em
ot

io
na

l e
xh

au
sti

Dep
er

so
na

liz
at

io
n

Lo
w p

er
so

na
l a

cc
om

pl
ish

m
en

Bur
no

Em
ot

io
na

l e
xh

au
sti

Dep
er

so
na

liz
at

io
n

Lo
w p

er
so

na
l a

cc
om

pl
ish

m
en

Q
ua

lit
y

M
et

ri
c

Quality and safety

Outcomes

Errors

Communication

Best practices

C
ou

7
5
3
1

2.0

1.5

1.0

0.5

–0.5

–1.0

–2.0

–1.5

H
ed

ge
s
g

Inappropriate laboratory tests
Inappropriate timing of discharge
Suboptimal patient care practices
Inappropriate use of patient restraints
Poor adherence to infection control
Inappropriate antibiotic prescribing
Lack of close monitoring
Low best practice score
Neglect of work
Poor adherence to management guidelines

Poor communication
Low patient enablement score
Forgetting to convey information
Low attention to patient impact
Low physcian empathy score
Not fully discussing treatment options
Poor handoff quality
Short consultation length

Self-reported medical errors
Self-reported medication errors
Self-reported treatment/medication errors
Medical error score
Observed medical errors
Accident propensity
Diagnosis delay
Diagnostic errors
Observed medication errors
Self-reported impairment

Adverse events
Health care–associated infections
Patient falls
Length of stay
Urinary tract infections
Mortality
Poor pain control
HIV viral load suppression
Morbidity
Posthospitalization recovery time

Low quality of care
Low patient safety score
Low safety climate score
Low quality during most recent shift
Low work unit safety grade
Poor patient care quality score
Malpractice allegations
Low individual safety grade
Low safety perceptions
Near-miss reporting
Prolonged emergency department visit

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data published in a subsequent review (39, 66, 69, 107,
115, 117). One research group reported results from a
single study population in 2 publications; the first pub-
lished effect was considered primary, with results from
the later publication considered secondary effects
(112, 160).

Overall burnout, emotional exhaustion, and deper-
sonalization were the primary predictors for 56, 75, and
11 study populations, respectively, from a variety of sur-
vey instruments, as outlined in Supplement Table 5
(available at Annals.org). The 50 distinct quality metrics
included 10 best practices, 8 communication, 10 med-
ical errors, 10 patient outcomes, and 12 quality and
safety measures (26 measured provider perception of
quality, 15 used independent or objective measures of
quality, and 9 included both types of assessments).

As illustrated in Figure 2, 38 (33%) of the 114 dis-
tinct burnout–quality combinations were reported 3 or
more times. The most frequently reported effect re-
lated emotional exhaustion to low quality of care (n =
41), with most of the reported effect sizes in the quality
and safety and medical errors categories. Although all
5 categories of outcomes had estimates more fre-
quently relating burnout in the direction of poor quality
of care (denoted in red in Figure 2), 7 of the 16 esti-
mates pointing in the opposite direction were found in
the communication category. Results were similar when
limited to primary (or first-listed, when primary was not
specified) effect sizes only (Supplement Figure 1, avail-
able at Annals.org).

Meta-analyses combining burnout and quality met-
rics within quality categories revealed I2 values of
93.4% to 98.8%, indicating extremely high heterogene-
ity; therefore, summary effects are provided only at the
level of the 114 distinct burnout–quality combinations,
46 of which included primary effect sizes. Meta-
analyses of these 46 combinations revealed 24 (52%)
with a statistically significant summary effect greater
than 0 (burnout related to poor quality of care), 1 (2%)
with statistically significant summary effects less than 0
(burnout related to high quality of care), and 21 (46%)
with no difference at the P < 0.050 threshold. When the P < 0.005 threshold was used, the respective numbers were 18 (39%), 1 (2%), and 27 (59%). Results are sum- marized in Table 1, and primary effect sizes from all included studies are shown in Supplement Figure 2 (available at Annals.org).

Results were similar when secondary effect sizes
were included. Of the 114 distinct burnout–quality met-
ric combinations, 58 (51%) had statistically significant
summary effects greater than 0, 6 (5%) had statistically
significant effects less than 0, and 50 (44%) showed no
difference at the P < 0.050 threshold. When the P < 0.005 threshold was used, the respective numbers were 47 (41%), 6 (5%), and 61 (54%). Results from all burnout–quality metric combinations are shown in Sup- plement Figure 3 (available at Annals.org). Our findings were similar when limited to studies explicitly using standard burnout definitions, but the observed rela- tionships were attenuated when limited to indepen- dent or objective quality metrics, as shown in Table 1.

The most precise study with low risk of bias (143)
reported a small effect size (Hedges g = 0.26, analo-
gous to an odds ratio of 1.5 to 1.6). Using this estimate,
the Ioannidis test found an excess of observed versus
predicted statistically significant studies (73% observed
vs. 62% predicted at the 0.050 significance threshold,
P = 0.011) (Table 2). When stratified by quality metric
category, an excess of statistically significant studies was
seen in the categories of best practices and quality and
safety. Results were similar for the P < 0.005 threshold.

The Egger test did not show small-study effects (inter-
cept, �1.32 [95% CI, �3.48 to 0.85]), indicating that
smaller studies did not systematically overestimate effect
sizes (Figure 3). A funnel plot relating effect size to SE is
shown in Supplement Figure 4 (available at Annals.org).

DISCUSSION
This overview extends previous work in the field by

including a comprehensive evaluation for reporting bi-
ases in the health care provider burnout literature, en-
compassing 145 published study populations that
quantified the relation between burnout and quality of
care over 25 years for 241 553 health care profession-
als. Most of the evidence suggests a relationship be-
tween provider burnout and impaired quality of care,
consistent with recent reviews of various dimensions (7–
10, 22). Although the effect sizes in the published liter-
ature are modestly strong, our finding of excess signif-
icance implies that the true magnitude may be smaller
than reported, and the studies that attempted to lower
the risk of bias demonstrate fewer significant associa-
tions than the full evidence base. That only 4 studies

Table 1. Number and Direction of Summary Effect Sizes for Each Combination of Burnout and Quality Metric*

Criteria for Inclusion Burnout–Quality
Combinations, n†

P < 0.050 Threshold, n (%) P < 0.005 Threshold, n (%)

Hedges g > 0‡ Hedges g < 0§ No Effect�� Hedges g > 0‡ Hedges g < 0§ No Effect��

Primary effects only 46 24 (52) 1 (2) 21 (46) 18 (39) 1 (2) 27 (59)
Primary and secondary effects 114 58 (51) 6 (5) 50 (44) 47 (41) 6 (5) 61 (54)
Standard burnout definitions 24 15 (62) 1 (4) 8 (33) 14 (58) 1 (4) 9 (38)
Independent/objective quality metrics 48 14 (29) 2 (4) 32 (67) 9 (19) 2 (4) 37 (77)

* Summary effect sizes obtained via empirical Bayes meta-analysis.
† Number of distinct burnout–quality combinations represented.
‡ Indicates burnout related to poor-quality care.
§ Indicates burnout related to high-quality care.
�� Not significantly different from 0 at the specified P value threshold.

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specified primary outcomes further supports the possi-
bility of reporting bias causing exaggerated effects.

From a 2015 search of MEDLINE, Web of Science,
and CINAHL (EBSCO), Salyers and colleagues (9) re-
ported effect sizes of r = �0.26 (Hedges g = 0.54) and
r = �0.23 (Hedges g = 0.47) for the relationship be-
tween burnout and quality and safety outcomes, re-
spectively. These effect sizes are somewhat larger than
those observed in the present study. However, the pre-
vious meta-analysis also included markers of patient
satisfaction and included only 82 studies through
March 2015. More recently, a 2017 all-language search
of MEDLINE, EMBASE, and CINAHL by Panagioti and
colleagues (10) identified 47 physician studies and re-
ported a more similar summary odds ratio of 1.96 for
patient safety incidents (approximate Hedges g = 0.37).
However, that review included 42 473 physicians (less
than 20% of the number of providers represented here)
and did not include diverse health care professionals.

The observed relationships between burnout and
quality of care are probably multifactorial. Providers
who have burnout may have less time or commitment
to optimize the care of their patients, may take more
unnecessary risks, or may be unable to pay attention to
necessary details or recognize the consequences of
their actions (71). Conversely, exposure to adverse pa-
tient events or recognition of poor-quality care may re-
sult in emotional or other psychological distress among
providers. This phenomenon often is referred to as sec-
ondary trauma, particularly in relation to sentinel events
or important safety incidents, but it might also arise
from repeated minor incidents (161). The true effect
sizes relating burnout and quality of care in both direc-
tions are important to understand in order to make
sound decisions regarding resource allocation and
study design of interventions, both to improve quality
of care and to diminish burnout.

Recent concerns have arisen regarding variability
in burnout assessment methods, and this inconsistency
was evident in the body of literature compiled here
(12). In this regard, the subset of studies in our analysis
that used the most widely accepted “standard” burnout
assessment methods demonstrated a similar to slightly
increased frequency of significant associations com-
pared with the full evidence base. This finding suggests
that the relationship between burnout and quality of

care in the published literature is not a result of subop-
timal measures or variability in the definition of burn-
out.

Excess significance in the published literature was
noted specifically for adherence to best practice guide-
lines and for quality and safety metrics. Investigations of
burnout in relation to these outcomes are typically ret-
rospective studies of routinely collected outcome met-
rics in existing data sets, without preregistered proto-
cols. The relative ease of defining and evaluating many
outcomes in many ways with these data sets increases
the risk for selective outcome and selective analysis re-
porting, which may have contributed to excess signifi-
cance. We found slightly lower effect sizes, but without
excess significance, for the patient outcomes sub-
group, possibly reflecting the more common use by
these studies of quality metrics with little or no flexibility
in their definition and measurement (such as mortality
or length of stay).

In direct assessment, studies using independent or
objective quality metrics demonstrated less frequent
significant effects. This finding is not surprising, be-
cause previous research suggests that current methods
of objectively measuring quality of care cannot reliably
identify certain events, such as errors in judgment,
technical procedural mistakes, or near misses (10, 162).
Objective metrics also are costly to measure and diffi-
cult to connect to an individual provider because of the
team-based nature of most clinical care, limiting appli-
cation to smaller studies and those in which a quality
metric can be connected reliably to a provider. On the
other hand, subjective quality metrics may be more
sensitive and comprehensive but more prone to bias
(for example, having burnout may create recall bias).
Further research is needed to determine the appropri-
ate balance between insensitivity of objective quality
metrics and potential for recall bias with subjective
quality metrics.

Our analysis found no evidence specifically for
small-study effects, that is, small (more imprecise) stud-
ies reporting larger effects than large studies. These
findings are consistent with those of previous meta-
analyses, which traditionally evaluated for small-study
effects as a surrogate for all forms of reporting bias (9,
10). The discrepancy between our findings of overall
excess significance without evidence of small-study ef-

Table 2. Predicted Versus Observed Significance for Primary* Effect Sizes, Among All Included Studies and Stratified by
Quality Metric Category

Category Studies, n P < 0.050 Threshold P < 0.005 Threshold

Predicted
Significance, %

Observed
Significance, n (%)

P Value Predicted
Significance, %

Observed
Significance, n (%)

P Value

Full cohort 142 62 104 (73) 0.011 46 96 (68) <0.001 Best practices 14 12 9 (64) 0.001 2 8 (57) 0.001 Communication 5 43 3 (60) 0.67 40 3 (60) 0.63 Medical errors 32 50 20 (62) 0.169 33 15 (47) 0.182 Patient outcomes 17 64 9 (53) NP 54 9 (53) NP Quality and safety 74 65 62 (84) <0.001 50 60 (81) <0.001

NP = not pertinent (observed smaller than predicted).
* Or first listed, when the primary effect size was not specified.

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fects may highlight the insensitivity of the latter test as a
marker of all forms of bias. Moreover, smaller studies in
this field are more likely to have objective measure-
ments, whereas larger studies are more likely to have
subjective measurements. This would dilute the ability
of the small-study effect test to show a typical bias
pattern.

Our study should be viewed in light of its design.
Although most included studies were cross-sectional,
observational, and unable to determine the directional-
ity of a causal relationship, longitudinal studies suggest
bidirectional causality (62, 149, 151, 152). Although 2
independent reviewers conducted extensive searches,
they may have missed some relevant studies. Burnout
has several important outcomes beyond its effects on
quality of care that were not the focus of our analysis
(2–6). Finally, excess significance may be a result of
genuine heterogeneity of effects across studies rather
than reporting bias (33). The effects reported here rep-
resent the results of heterogeneous studies; therefore,
we do not report a single summary effect size. Rather,
we report frequencies of significant summary effect
sizes within burnout–quality metric combinations to
provide a quantitative framework for interpretation
while acknowledging that a distribution of true effect
sizes is expected in this field-wide assessment, in con-
trast to a traditional meta-analysis (163).

We avoided scoring quality assessments of the in-
cluded studies, choosing instead to analyze key aspects
of study quality, as suggested by the proposed report-
ing guidelines for meta-analyses of observational stud-
ies (16). Judging the quality of mostly cross-sectional
observational studies is notoriously difficult, and no

widely accepted tools exist. Salyers and colleagues (9)
created a 10-item tool to assess quality aspects in 82
burnout and quality-of-care studies and did not identify
any relationship between study quality score and effect
size.

Our findings carry several important implications
for future intervention trials and observational studies.
For intervention trials, the potential for exaggerated
published effects should be considered in power calcu-
lations to lower the risk for false-negative results (type II
error). In addition, future studies should attempt to re-
duce the risk of reporting biases. Standardization and
consensus on core outcomes may be useful for future
studies if appropriate targets can be identified (164).
Such standardization may improve comparability
among studies, facilitating traditional meta-analysis es-
timates of the relevant effect sizes. Some outcomes,
such as self-reported medical errors, low quality of
care, and low patient safety score, are particularly prev-
alent in the literature, suggesting that researchers al-
ready consider these outcomes either important or fea-
sible to measure. However, if core outcomes are to be
widely accepted, they must be both important and fea-
sible to measure. Thus, in addition to this “popular
vote” approach, expert consensus is needed to curate
an appropriate list of core outcomes for this field. Other
outcome evaluations might then be discouraged unless
a unique justification is present.

Study registration may further reduce the risk of
study publication bias and increase transparency of un-
published studies. By registering a study publicly at its
outset, researchers can reduce the likelihood that a
study was conceived and conducted but remains un-

Figure 3. Standard normal deviate (Hedges g/SE) in relation to precision (1/SE).

St
an

da
rd

N
or

m
al

D
ev

ia
te

Robust
SE

Parameter
Estimate

–3.48 to 0.85
0.33 to 0.75

1.10
0.10

0.23
<0.001

–1.32
0.54

Intercept
Slope

P Value95% CI

Precision

95% CI
Fitted values

–20

Burnout and Quality of Care REVIEW

Annals.org Annals of Internal Medicine • Vol. 171 No. 8 • 15 October 2019 561

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published because of undesirable or lackluster results
(165). In a similar manner, protocol prespecification
may reduce the risk for selective outcome and selective
analysis reporting within published studies, allowing
easier identification of any post hoc analyses. Published
analyses that deviate from the prespecified protocol
would require justification from the authors, and this
approach would alert the readers that those results
may be more susceptible to bias. Currently, these
mechanisms are used rarely in any field of medicine
outside clinical trials, but they could become widely ad-
opted with sufficient advocacy by researchers, publish-
ers, funders, and other stakeholders.

In conclusion, burnout among health care provid-
ers is frequently associated with reduced quality of care
in the published literature. However, few rigorous stud-
ies exist, and the effect size may be smaller than report-
ed—and may be particularly smaller for objective quality
measures. Whether curtailing burnout improves quality
of care, or whether improving quality of care reduces
burnout, is not yet known, and adequately powered
and designed randomized trials (91, 166, 167) will be
indispensable in answering these questions.

From Stanford University School of Medicine, Stanford, Cali-
fornia (D.S.T., T.S., M.T.); Brigham and Women’s Hospital and
Harvard Medical School, Boston, Massachusetts (A.S.); Stan-
ford University School of Medicine, Stanford, California, and
California Perinatal Quality Care Collaborative, Palo Alto, Cal-
ifornia (J.P.); Duke University School of Medicine, Duke Uni-
versity Health System, and Duke Patient Safety Center, Dur-
ham, North Carolina (K.C.A., J.B.S.); and Stanford University
School of Medicine, Stanford University School of Humanities
and Sciences, and Meta-Research Innovation Center at Stan-
ford (METRICS), Stanford, California (J.P.I.).

Note: The lead author had full access to all data in the study
and affirms that the manuscript is an honest, accurate, and
transparent account of the study; that no important aspects of
the study have been omitted; and that any discrepancies from
the study as originally planned have been explained.

Financial Support: By the Stanford Maternal and Child Health
Research Institute.

Disclosures: Dr. Tawfik reports grants from Stanford Maternal
and Child Health Research Institute during the conduct of the
study. Dr. Profit reports grants from the Eunice Kennedy
Shriver National Institute of Child Health and Human Develop-
ment during the conduct of the study and has received hon-
oraria for speaking at scientific meetings on the topic of burn-
out. Dr. Sexton reports grants from the National Institutes of
Health during the conduct of the study. Authors not named
here have disclosed no conflicts of interest. Disclosures can
also be viewed at www.acponline.org/authors/icmje/Conflict
OfInterestForms.do?msNum=M19-1152.

Reproducible Research Statement: Study protocol, statistical
code, and data set: Available from Dr. Tawfik (e-mail, dtawfik
@stanford.edu).

Corresponding Author: Daniel S. Tawfik, MD, MS, 770 Welch
Road, Suite 435, Palo Alto, CA 94304; e-mail, dtawfik
@stanford.edu.

Current author addresses and author contributions are avail-
able at Annals.org.

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INFORMATION FOR AUTHORS

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/aim/pages/authors. All manuscripts must be submitted electronically us-
ing the manuscript submission option at Annals.org.

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Current Author Addresses: Dr. Tawfik: 770 Welch Road, Suite
435, Palo Alto, CA 94304.
Dr. Scheid: Office BL341G, 221 Longwood Avenue, Boston,
MA 02115.
Dr. Profit: 1265 Welch Road, MSOB x1C07, Stanford, CA
94305.
Dr. Shanafelt: 300 Pasteur Drive, Room H3215, Stanford, CA
94305.
Dr. Trockel: 401 Quarry Road, Room 2303, Stanford, CA
94305.
Drs. Adair and Sexton: 3100 Tower Boulevard, Suite 300, Dur-
ham, NC 27707.
Dr. Ioannidis: 1265 Welch Road, MSOB x306, Stanford, CA
94305.

Author Contributions: Conception and design: D.S. Tawfik,
J.P.A. Ioannidis.
Analysis and interpretation of the data: D.S. Tawfik, J. Profit, T.
Shanafelt.
Drafting of the article: D.S. Tawfik, T. Shanafelt, J.P.A. Ioannidis.
Critical revision for important intellectual content: D.S. Tawfik,
A. Scheid, T. Shanafelt, M. Trockel, J.B. Sexton, J.P.A. Ioannidis.
Final approval of the article: D.S. Tawfik, A. Scheid, J. Profit, T.
Shanafelt, M. Trockel, K.C. Adair, J.B. Sexton, J.P.A. Ioannidis.
Provision of study materials or patients: D.S. Tawfik.
Statistical expertise: D.S. Tawfik.
Obtaining of funding: D.S. Tawfik.
Administrative, technical, or logistic support: D.S. Tawfik, A.
Scheid, J.B. Sexton.
Collection and assembly of data: D.S. Tawfik, A. Scheid, K.C.
Adair.

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