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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 burnout 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 (I 2= 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 signiﬁcant 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/M19-1152Annals.org For author afﬁliations, see end of text. This article was published at Annals.org on 8 October 2019. H ealth 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 relation 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 effectswithin 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 R EVIEW © 2019 American College of Physicians555 M ETHODS 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 inSupplement Ta- bles 1to3(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 Hedgesgand SEs, respectively. The Hedgesgestimates effect size similarly to the Co- hend, 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 Burnout 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 Hedgesgfrom odds ratios (di- chotomized data) by using the transformation log OR * 3 or from correlation coefﬁcients (unscaled continuous data) by using the transformation2*r 1 r 2, R EVIEW Burnout and Quality of Care 556Annals of Internal Medicine•Vol. 171 No. 8•15 October 2019Annals.org both multiplied by a bias correction factor N 2 Ncon- sistent with published norms (25, 26). Further details are provided in theSupplement(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 theSupplement. 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 usingI 2. Details re- garding this meta-analytic approach are presented in theSupplement. 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 pairedttests. 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 (Hedgesg/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 proposedP< 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 R EVIEW Annals.orgAnnals of Internal Medicine•Vol. 171 No. 8•15 October 2019557 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 inSupplement Table 4(available at Annals.org). The median sample size was 376 (interquartile range, 129 to 1417). The 142study 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 (Hedgesg). Burnout Metric Burnout Emotional exhaustion Depersonalization Low personal accomplishment Burnout Emotional exhaustion Depersonalization Low personal accomplishment Quality Metric Quality and safetyOutcomesErrors CommunicationBest practices 30 25 15 Count 10 7 5 3 1 2.0 1.5 1.0 0.5 –0.5 –1.0 –2.0–1.5 0 Hedges g 20 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 R EVIEW Burnout and Quality of Care 558Annals of Internal Medicine•Vol. 171 No. 8•15 October 2019Annals.org 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 inSupplement 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 inFigure 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 inFigure 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 revealedI 2 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 theP< 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 inTable 1, and primary effect sizes from all included studies are shown inSupplement 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 theP< 0.050 threshold. When theP< 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 inSup- 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 inTable 1. The most precise study with low risk of bias (143) reported a small effect size (Hedgesg= 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 theP< 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 inSupplement 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 (%) Hedgesg>0‡ Hedgesg<0§ No Effect Hedgesg>0‡ Hedgesg<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 speciﬁedPvalue threshold. Burnout and Quality of Care R EVIEW Annals.orgAnnals of Internal Medicine•Vol. 171 No. 8•15 October 2019559 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 ofr= 0.26 (Hedgesg= 0.54) and r= 0.23 (Hedgesg= 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 Hedgesg= 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 ofcare 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,nP<0.050 ThresholdP<0.005 Threshold Predicted Signiﬁcance,%Observed Signiﬁcance,n (%)PValue Predicted Signiﬁcance,%Observed Signiﬁcance,n (%)PValue 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. R EVIEW Burnout and Quality of Care 560Annals of Internal Medicine•Vol. 171 No. 8•15 October 2019Annals.org 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 nowidely 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 importantorfea- sible to measure. However, if core outcomes are to be widely accepted, they must be both importantandfea- 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 (Hedgesg/SE) in relation to precision (1/SE). Standard Normal Deviate Robust SE Parameter Estimate –3.48 to 0.85 0.33 to 0.75 1.10 0.100.23 <0.001 –1.32 0.54 Intercept Slo peP Value 95% CI Precision 95% CI Fitted values 0 0 20 20 –20 40 40 60 60 80 80 Burnout and Quality of Care R EVIEW Annals.orgAnnals of Internal Medicine•Vol. 171 No. 8•15 October 2019561 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. 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Burnout and Quality of Care R EVIEW Annals.orgAnnals of Internal Medicine•Vol. 171 No. 8•15 October 2019567 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. Annals.orgAnnals of Internal Medicine•Vol. 171 No. 8•15 October 2019 Copyright ©American CollegeofPhysicians 2019.