Title your paper: “Review of [Name of Article]”State the Author:Summarize the article in one paragraph:Post a screenshot of the article’s frequency table and/or graph. Example: Frequency Distributi

We're the ideal place for homework help. If you are looking for affordable, custom-written, high-quality and non-plagiarized papers, your student life just became easier with us. Click either of the buttons below to place your order.


Order a Similar Paper Order a Different Paper
  1. Title your paper: “Review of [Name of Article]”
  2. State the Author:
  3. Summarize the article in one paragraph:
  4. Post a screenshot of the article’s frequency table and/or graph.  Example: Frequency Distribution -OR- Graph
  5. Answer the following questions about your table or graph.

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

      1. Explain how you came to that conclusion.
    2. What type of graph or table did you choose for your lab (bar graph, histogram, stem & leaf plot, etc.)?

      1. What characteristics make it this type (you should bring in material that you learned in the course)?
    3. 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.)
    4. Draw a conclusion about the data from the graph or frequency distribution in the context of the article.
    5. How else might this data have been displayed?

      1. Discuss the pros and cons of 2 other presentation options, such as tables or different graphical displays.
      2. Why do you think those two other presentation options (i.e., tables or different graphs) were not used in this article?
  6. Give the full APA reference of the article you are using for this lab.

Title your paper: “Review of [Name of Article]”State the Author:Summarize the article in one paragraph:Post a screenshot of the article’s frequency table and/or graph. Example: Frequency Distributi
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. Current author addresses and author contributions are avail- able at Annals.org. References 1.Shanafelt TD, West CP, Sinsky C, et al.Changes in burnout and satisfaction with work-life integration in physicians and the general US working population between 2011 and 2017. Mayo Clin Proc. 2019. [PMID: 30803733] doi:10.1016/j.mayocp.2018.10.023 2.Shanafelt TD, Boone SL, Dyrbye LN, et al.The medical marriage: a national survey of the spouses/partners of US physicians. Mayo Clin Proc. 2013;88:216-25. [PMID: 23489448] doi:10.1016/j.mayocp.2012 .11.021 3.Shanafelt TD, Mungo M, Schmitgen J, et al.Longitudinal study evaluating the association between physician burnout and changes in professional work effort. Mayo Clin Proc. 2016;91:422-31. [PMID: 27046522] doi:10.1016/j.mayocp.2016.02.001 4.Windover AK, Martinez K, Mercer MB, et al.Correlates and out- comes of physician burnout within a large academic medical center. JAMA Intern Med. 2018;178:856-858. [PMID: 29459945] doi:10 .1001/jamainternmed.2018.0019 5.Hamidi MS, Bohman B, Sandborg C, et al.Estimating institutional physician turnover attributable to self-reported burnout and associ- ated financial burden: a case study. BMC Health Serv Res. 2018;18: 851. [PMID: 30477483] doi:10.1186/s12913-018-3663-z 6.van der Heijden F, Dillingh G, Bakker A, et al.Suicidal thoughts among medical residents with burnout. Arch Suicide Res. 2008;12: 344-6. [PMID: 18828037] doi:10.1080/13811110802325349 7.Dewa CS, Loong D, Bonato S, et al.The relationship between physician burnout and quality of healthcare in terms of safety and acceptability: a systematic review. BMJ Open. 2017;7:e015141. [PMID: 28637730] doi:10.1136/bmjopen-2016-015141 8.Hall LH, Johnson J, Watt I, et al.Healthcare staff wellbeing, burn- out, and patient safety: a systematic review. PLoS One. 2016;11: e0159015. [PMID: 27391946] doi:10.1371/journal.pone.0159015 9.Salyers MP, Bonfils KA, Luther L, et al.The relationship between professional burnout and quality and safety in healthcare: a meta- analysis. J Gen Intern Med. 2017;32:475-482. [PMID: 27785668] doi: 10.1007/s11606-016-3886-9 10.Panagioti M, Geraghty K, Johnson J, et al.Association between physician burnout and patient safety, professionalism, and patient satisfaction: a systematic review and meta-analysis. JAMA In- tern Med. 2018;178:1317-1330. [PMID: 30193239] doi:10.1001 /jamainternmed.2018.3713 11.Rathert C, Williams ES, Linhart H.Evidence for the quadruple aim: a systematic review of the literature on physician burnout and patient outcomes. Med Care. 2018;56:976-984. [PMID: 30339573] doi:10.1097/MLR.0000000000000999 12.Rotenstein LS, Torre M, Ramos MA, et al.Prevalence of burnout among physicians: a systematic review. JAMA. 2018;320:1131-1150. [PMID: 30326495] doi:10.1001/jama.2018.12777 13.Ioannidis JP, Munafo` MR, Fusar-Poli P, et al.Publication and other reporting biases in cognitive sciences: detection, prevalence, and prevention. Trends Cogn Sci. 2014;18:235-41. [PMID: 24656991] doi:10.1016/j.tics.2014.02.010 14.Dwan K, Gamble C, Williamson PR, et al; Reporting Bias Group. Systematic review of the empirical evidence of study publication bias and outcome reporting bias – an updated review. PLoS One. 2013; 8:e66844. [PMID: 23861749] doi:10.1371/journal.pone.0066844 15.Moher D, Liberati A, Tetzlaff J, et al; PRISMA Group.Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. Ann Intern Med. 2009;151:264-9, W64. [PMID: 19622511] R EVIEW Burnout and Quality of Care 562Annals of Internal Medicine•Vol. 171 No. 8•15 October 2019Annals.org 16.Stroup DF, Berlin JA, Morton SC, et al.Meta-analysis of observa- tional studies in epidemiology: a proposal for reporting. Meta- analysis Of Observational Studies in Epidemiology (MOOSE) group. JAMA. 2000;283:2008-12. [PMID: 10789670] 17.Chang JT, Hays RD, Shekelle PG, et al.Patients’ global ratings of their health care are not associated with the technical quality of their care. Ann Intern Med. 2006;144:665-72. [PMID: 16670136] 18.Kennedy GD, Tevis SE, Kent KC.Is there a relationship between patient satisfaction and favorable outcomes? Ann Surg. 2014;260: 592-8; discussion 598-600. [PMID: 25203875] doi:10.1097/SLA .0000000000000932 19.Rao M, Clarke A, Sanderson C, et al.Patients’ own assessments of quality of primary care compared with objective records based measures of technical quality of care: cross sectional study. BMJ. 2006;333:19. [PMID: 16793783] 20.Schmocker RK, Cherney Stafford LM, Winslow ER.Satisfaction with surgeon care as measured by the Surgery-CAHPS survey is not related to NSQIP outcomes. Surgery. 2019;165:510-515. [PMID: 30322662] doi:10.1016/j.surg.2018.08.028 21.Chuang CH, Tseng PC, Lin CY, et al.Burnout in the intensive care unit professionals: a systematic review. Medicine (Baltimore). 2016; 95:e5629. [PMID: 27977605] 22.Dewa CS, Loong D, Bonato S, et al.The relationship between resident burnout and safety-related and acceptability-related quality of healthcare: a systematic literature review. BMC Med Educ. 2017; 17:195. [PMID: 29121895] doi:10.1186/s12909-017-1040-y 23.Scheepers RA, Boerebach BC, Arah OA, et al.A systematic re- view of the impact of physicians’ occupational well-being on the quality of patient care. Int J Behav Med. 2015;22:683-98. [PMID: 25733349] doi:10.1007/s12529-015-9473-3 24.West CP, Dyrbye LN, Erwin PJ, et al.Interventions to prevent and reduce physician burnout: a systematic review and meta-analysis. Lancet. 2016;388:2272-2281. [PMID: 27692469] doi:10.1016/S0140 -6736(16)31279-X 25.Chinn S.A simple method for converting an odds ratio to effect size for use in meta-analysis. Stat Med. 2000;19:3127-31. [PMID: 11113947] 26.Lajeunesse M.Recovering Missing or Partial Data from Studies: A Survey of Conversions and Imputations for Meta-analysis. Princeton: Princeton Univ Pr; 2013. 27.Guyatt GH, Thorlund K, Oxman AD, et al.GRADE guidelines: 13. Preparing summary of findings tables and evidence profiles- continuous outcomes. J Clin Epidemiol. 2013;66:173-83. [PMID: 23116689] doi:10.1016/j.jclinepi.2012.08.001 28.Hasselblad V, Hedges LV.Meta-analysis of screening and diag- nostic tests. Psychol Bull. 1995;117:167-78. [PMID: 7870860] 29.Shanafelt TD, Boone S, Tan L, et al.Burnout and satisfaction with work-life balance among US physicians relative to the general US population. Arch Intern Med. 2012;172:1377-85. [PMID: 22911330] 30.Tawfik DS, Phibbs CS, Sexton JB, et al.Factors associated with provider burnout in the NICU. Pediatrics. 2017;139. [PMID: 28557756] doi:10.1542/peds.2016-4134 31.West CP, Shanafelt TD, Kolars JC.Quality of life, burnout, edu- cational debt, and medical knowledge among internal medicine res- idents. JAMA. 2011;306:952-60. [PMID: 21900135] doi:10.1001/jama .2011.1247 32.Knapp G, Hartung J.Improved tests for a random effects meta- regression with a single covariate. Stat Med. 2003;22:2693-710. [PMID: 12939780] 33.Ioannidis JP, Trikalinos TA.An exploratory test for an excess of significant findings. Clin Trials. 2007;4:245-53. [PMID: 17715249] 34.Sterne JA, Sutton AJ, Ioannidis JP, et al.Recommendations for examining and interpreting funnel plot asymmetry in meta-analyses of randomised controlled trials. BMJ. 2011;343:d4002. [PMID: 21784880] doi:10.1136/bmj.d4002 35.Benjamin DJ, Berger JO, Johannesson M, et al.Redefine statis- tical significance. Nat Hum Behav. 2018;2:6-10. [PMID: 30980045] doi:10.1038/s41562-017-0189-z36.Ioannidis JPA.The proposal to lower P value thresholds to .005. JAMA. 2018;319:1429-1430. [PMID: 29566133] doi:10.1001/jama .2018.1536 37.Abe K, Ohashi A.Development and testing of a staff question- naire for evaluating the quality of services at nursing homes in Japan. J Am Med Dir Assoc. 2009;10:189-95. [PMID: 19233059] doi:10.1016/j .jamda.2008.10.004 38.Ozˇ vacˇic´ Adzˇic´ Z, Katic´ M, Kern J, et al.Is burnout in family phy- sicians in Croatia related to interpersonal quality of care? Arh Hig Rada Toksikol. 2013;64:255-64. [PMID: 23819934] doi:10.2478 /10004-1254-64-2013-2307 39.Aiken LH, Sermeus W, Van den Heede K, et al.Patient safety, satisfaction, and quality of hospital care: cross sectional surveys of nurses and patients in 12 countries in Europe and the United States. BMJ. 2012;344:e1717. [PMID: 22434089] doi:10.1136/bmj.e1717 40.Angermeier I, Dunford BB, Boss AD, et al.The impact of partic- ipative management perceptions on customer service, medical er- rors, burnout, and turnover intentions. J Healthc Manag. 2009;54: 127-40; discussion 141. [PMID: 19413167] 41.Baer TE, Feraco AM, Tuysuzoglu Sagalowsky S, et al.Pediatric resident burnout and attitudes toward patients. Pediatrics. 2017;139. [PMID: 28232639] doi:10.1542/peds.2016-2163 42.Baier N, Roth K, Felgner S, et al.Burnout and safety outcomes – a cross-sectional nationwide survey of EMS-workers in Germany. BMC Emerg Med. 2018;18:24. [PMID: 30126358] doi:10.1186/s12873 -018-0177-2 43.Balch CM, Oreskovich MR, Dyrbye LN, et al.Personal conse- quences of malpractice lawsuits on American surgeons. J Am Coll Surg. 2011;213:657-67. [PMID: 21890381] doi:10.1016/j.jamcollsurg .2011.08.005 44.Bao Y, Vedina R, Moodie S, et al.The relationship between value incongruence and individual and organizational well-being out- comes: an exploratory study among Catalan nurses. J Adv Nurs. 2013;69:631-41. [PMID: 22632178] doi:10.1111/j.1365-2648.2012 .06045.x 45.Basar U, Basim N.A cross-sectional survey on consequences of nurses’ burnout: moderating role of organizational politics. J Adv Nurs. 2016;72:1838-50. [PMID: 26988276] doi:10.1111/jan.12958 46.Beckman TJ, Reed DA, Shanafelt TD, et al.Resident physician well-being and assessments of their knowledge and clinical perfor- mance. J Gen Intern Med. 2012;27:325-30. [PMID: 21948207] doi: 10.1007/s11606-011-1891-6 47.Block L, Wu AW, Feldman L, et al.Residency schedule, burnout and patient care among first-year residents. Postgrad Med J. 2013; 89:495-500. [PMID: 23852828] doi:10.1136/postgradmedj-2012 -131743 48.Boamah SA, Read EA, Spence Laschinger HK.Factors influenc- ing new graduate nurse burnout development, job satisfaction and patient care quality: a time-lagged study. J Adv Nurs. 2017;73:1182- 1195. [PMID: 27878844] doi:10.1111/jan.13215 49.Bronkhorst B, Vermeeren B.Safety climate, worker health and organizational health performance: testing a physical, psychosocial and combined pathway. International Journal of Workplace Health Management. 2016;9:270-89. 50.Brunsberg KA, Landrigan CP, Garcia BM, et al.Association of pediatric resident physician depression and burnout with harmful medical errors on inpatient services. Acad Med. 2019;94:1150-1156. [PMID: 31045601] doi:10.1097/ACM.0000000000002778 51.Chao M, Shih CT, Hsu SF.Nurse occupational burnout and patient-rated quality of care: the boundary conditions of emotional intelligence and demographic profiles. Jpn J Nurs Sci. 2016;13:156- 65. [PMID: 26542752] doi:10.1111/jjns.12100 52.Chen KY, Yang CM, Lien CH, et al.Burnout, job satisfaction, and medical malpractice among physicians. Int J Med Sci. 2013;10: 1471-8. [PMID: 24046520] doi:10.7150/ijms.6743 53.Cheng C, Bartram T, Karimi L, et al.The role of team climate in the management of emotional labour: implications for nurse reten- tion. J Adv Nurs. 2013;69:2812-25. [PMID: 23834619] doi:10.1111 /jan.12202 Burnout and Quality of Care R EVIEW Annals.orgAnnals of Internal Medicine•Vol. 171 No. 8•15 October 2019563 54.Cimiotti JP, Aiken LH, Sloane DM, et al.Nurse staffing, burnout, and health care-associated infection. Am J Infect Control. 2012;40: 486-90. [PMID: 22854376] doi:10.1016/j.ajic.2012.02.029 55.Colindres CV, Bryce E, Coral-Rosero P, et al.Effect of effort- reward imbalance and burnout on infection control among Ecuador- ian nurses. Int Nurs Rev. 2018;65:190-199. [PMID: 29114886] doi:10 .1111/inr.12409 56.Cummings GG, Estabrooks CA, Midodzi WK, et al.Influence of organizational characteristics and context on research utilization. Nurs Res. 2007;56:S24-39. [PMID: 17625471] 57.Davenport DL, Henderson WG, Mosca CL, et al.Risk-adjusted morbidity in teaching hospitals correlates with reported levels of communication and collaboration on surgical teams but not with scale measures of teamwork climate, safety climate, or working con- ditions. J Am Coll Surg. 2007;205:778-84. [PMID: 18035261] 58.de Oliveira GS Jr, Chang R, Fitzgerald PC, et al.The prevalence of burnout and depression and their association with adherence to safety and practice standards: a survey of United States anesthesiol- ogy trainees. Anesth Analg. 2013;117:182-93. [PMID: 23687232] doi:10.1213/ANE.0b013e3182917da9 59.De Stefano C, Philippon AL, Krastinova E, et al.Effect of emer- gency physician burnout on patient waiting times. Intern Emerg Med. 2018;13:421-428. [PMID: 28677043] doi:10.1007/s11739-017 -1706-9 60.Deckard G, Meterko M, Field D.Physician burnout: an examina- tion of personal, professional, and organizational relationships. Med Care. 1994;32:745-54. [PMID: 8028408] 61.Dorigan GH, Guirardello EB.Effect of the practice environment of nurses on job outcomes and safety climate. Rev Lat Am Enferma- gem. 2018;26:e3056. [PMID: 30379243] doi:10.1590/1518-8345 .2633.3056 62.Fahrenkopf AM, Sectish TC, Barger LK, et al.Rates of medication errors among depressed and burnt out residents: prospective co- hort study. BMJ. 2008;336:488-91. [PMID: 18258931] doi:10.1136 /bmj.39469.763218.BE 63.Faivre G, Kielwasser H, Bourgeois M, et al.Burnout syndrome in orthopaedic and trauma surgery residents in France: a nationwide survey. Orthop Traumatol Surg Res. 2018;104:1291-1295. [PMID: 30341030] doi:10.1016/j.otsr.2018.08.016 64.Galletta M, Portoghese I, D’Aloja E, et al.Relationship between job burnout, psychosocial factors and health care-associated infec- tions in critical care units. Intensive Crit Care Nurs. 2016;34:51-8. [PMID: 26961918] doi:10.1016/j.iccn.2015.11.004 65.Garrouste-Orgeas M, Perrin M, Soufir L, et al.The Iatroref study: medical errors are associated with symptoms of depression in ICU staff but not burnout or safety culture. Intensive Care Med. 2015;41: 273-84. [PMID: 25576157] doi:10.1007/s00134-014-3601-4 66.Gasparino RC, Guirardello Ede B, Aiken LH.Validation of the brazilian version of the nursing work index-revised (B-NWI-r). J Clin Nurs. 2011;20:3494-501. [PMID: 21749511] doi:10.1111/j.1365-2702 .2011.03776.x 67.Gopal R, Glasheen JJ, Miyoshi TJ, et al.Burnout and internal medicine resident work-hour restrictions. Arch Intern Med. 2005; 165:2595-600. [PMID: 16344416] 68.Guirardello EB.Impact of critical care environment on burnout, perceived quality of care and safety attitude of the nursing team. Rev Lat Am Enfermagem. 2017;25:e2884. [PMID: 28591294] doi:10 .1590/1518-8345.1472.2884 69.Gunnarsdo´ ttir S, Clarke SP, Rafferty AM, et al.Front-line manage- ment, staffing and nurse-doctor relationships as predictors of nurse and patient outcomes. a survey of Icelandic hospital nurses. Int J Nurs Stud. 2009;46:920-7. [PMID: 17229425] 70.Gupta K, Lisker S, Rivadeneira NA, et al.Decisions and repercus- sions of second victim experiences for mothers in medicine (SAVE DR MoM). BMJ Qual Saf. 2019;28:564-573. [PMID: 30718333] doi: 10.1136/bmjqs-2018-008372 71.Halbesleben JR, Rathert C.Linking physician burnout and patient outcomes: exploring the dyadic relationship between physicians and patients. Health Care Manage Rev. 2008;33:29-39. [PMID: 18091442]72.Halbesleben JR, Wakefield BJ, Wakefield DS, et al.Nurse burnout and patient safety outcomes: nurse safety perception versus reporting behavior. West J Nurs Res. 2008;30:560-77. [PMID: 18187408] doi:10.1177/0193945907311322 73.Hansen RP, Vedsted P, Sokolowski I, et al.General practitioner characteristics and delay in cancer diagnosis. a population-based co- hort study. BMC Fam Pract. 2011;12:100. [PMID: 21943310] doi:10 .1186/1471-2296-12-100 74.Hayashino Y, Utsugi-Ozaki M, Feldman MD, et al.Hope modified the association between distress and incidence of self-perceived medical errors among practicing physicians: prospective cohort study. PLoS One. 2012;7:e35585. [PMID: 22530055] doi:10.1371 /journal.pone.0035585 75.Holden RJ, Patel NR, Scanlon MC, et al.Effects of mental de- mands during dispensing on perceived medication safety and em- ployee well-being: a study of workload in pediatric hospital pharma- cies. Res Social Adm Pharm. 2010;6:293-306. [PMID: 21111387] doi: 10.1016/j.sapharm.2009.10.001 76.Holden RJ, Scanlon MC, Patel NR, et al.A human factors frame- work and study of the effect of nursing workload on patient safety and employee quality of working life. BMJ Qual Saf. 2011;20:15-24. [PMID: 21228071] doi:10.1136/bmjqs.2008.028381 77.Huang CH, Wu HH, Chou CY, et al.The perceptions of physicians and nurses regarding the establishment of patient safety in a re- gional teaching hospital in Taiwan. Iran J Public Health. 2018;47:852- 860. [PMID: 30087871] 78.Huang CH, Wu HH, Lee YC.The perceptions of patient safety culture: a difference between physicians and nurses in Taiwan. Appl Nurs Res. 2018;40:39-44. [PMID: 29579497] doi:10.1016/j.apnr .2017.12.010 79.Huang EC, Pu C, Huang N, et al.Resident burnout in Taiwan Hospitals-and its relation to physician felt trust from patients. J For- mos Med Assoc. 2019. [PMID: 30626545] doi:10.1016/j.jfma.2018.12 .015 80.Johnson J, Louch G, Dunning A, et al.Burnout mediates the association between depression and patient safety perceptions: a cross-sectional study in hospital nurses. J Adv Nurs. 2017;73:1667- 1680. [PMID: 28072469] doi:10.1111/jan.13251 81.Kang EK, Lihm HS, Kong EH.Association of intern and resident burnout with self-reported medical errors. Korean J Fam Med. 2013; 34:36-42. [PMID: 23372904] doi:10.4082/kjfm.2013.34.1.36 82.Kim MH, Mazenga AC, Simon K, et al.Burnout and self-reported suboptimal patient care amongst health care workers providing HIV care in Malawi. PLoS One. 2018;13:e0192983. [PMID: 29466443] doi: 10.1371/journal.pone.0192983 83.Kirwan M, Matthews A, Scott PA.The impact of the work envi- ronment of nurses on patient safety outcomes: a multi-level model- ling approach. Int J Nurs Stud. 2013;50:253-63. [PMID: 23116681] doi:10.1016/j.ijnurstu.2012.08.020 84.Klein J, Grosse Frie K, Blum K, et al.Burnout and perceived quality of care among German clinicians in surgery. Int J Qual Health Care. 2010;22:525-30. [PMID: 20935011] doi:10.1093/intqhc /mzq056 85.Kwah J, Fallar R, Weintraub JP, Ripp J.The impact of job burnout on measures of professionalism in first-year internal medicine resi- dents at a large urban academic medical center. J Gen Intern Med. 2014;29:S228. 86.Lafreniere JP, Rios R, Packer H, et al. Burned out at the bedside: patient perceptions of physician burnout in an internal medicine res- ident continuity clinic. J Gen Intern Med. 2016;31:203-208. [PMID: 26340808] doi:10.1007/s11606-015-3503-3 87.Spence Laschinger HK, Leiter MP.The impact of nursing work environments on patient safety outcomes: the mediating role of burnout/engagement. J Nurs Adm. 2006;36:259-67. [PMID: 16705307] 88.Laschinger H, Shamian J, Thomson D.Impact of magnet hospital characteristics on nurses’ perceptions of trust, burnout, quality of care, and work satisfaction. Nursing Economic$. 2001;19(5):209-19. R EVIEW Burnout and Quality of Care 564Annals of Internal Medicine•Vol. 171 No. 8•15 October 2019Annals.org 89.Lewis EJ, Baernholdt MB, Yan G, et al.Relationship of adverse events and support to RN burnout. J Nurs Care Qual. 2015;30:144- 52. [PMID: 25148522] doi:10.1097/NCQ.0000000000000084 90.Linzer M, Manwell LB, Williams ES, et al; MEMO (Minimizing Error, Maximizing Outcome) Investigators.Working conditions in primary care: physician reactions and care quality. Ann Intern Med. 2009;151:28-36, W6-9. [PMID: 19581644] 91.Linzer M, Poplau S, Brown R, et al.Do work condition interven- tions affect quality and errors in primary care? results from the healthy work place study. J Gen Intern Med. 2017;32:56-61. [PMID: 27612486] doi:10.1007/s11606-016-3856-2 92.Liu X, Zheng J, Liu K, et al.Hospital nursing organizational fac- tors, nursing care left undone, and nurse burnout as predictors of patient safety: a structural equation modeling analysis. Int J Nurs Stud. 2018;86:82-89. [PMID: 29966828] doi:10.1016/j.ijnurstu.2018 .05.005 93.Liu Y, Aungsuroch Y.Factors influencing nurse-assessed quality nursing care: a cross-sectional study in hospitals. J Adv Nurs. 2018; 74:935-945. [PMID: 29148146] doi:10.1111/jan.13507 94.Loerbroks A, Glaser J, Vu-Eickmann P, et al.Physician burnout, work engagement and the quality of patient care. Occup Med (Lond). 2017;67:356-362. [PMID: 28510762] doi:10.1093/occmed /kqx051 95.Lorenz VR, Sabino MO, Correˆ a Filho HR.Professional exhaustion, quality and intentions among family health nurses. Rev Bras Enferm. 2018;71:2295-2301. [PMID: 30365797] doi:10.1590/0034-7167 -2016-0510 96.Lu DW, Dresden S, McCloskey C, et al.Impact of burnout on self-reported patient care among emergency physicians. West J Emerg Med. 2015;16:996-1001. [PMID: 26759643] doi:10.5811 /westjem.2015.9.27945 97.MacPhee M, Dahinten VS, Havaei F.The impact of heavy per- ceived nurse workloads on patient and nurse outcomes. Administra- tive Sciences. 2017;7:7. 98.Martinussen M, Kaiser S, Adolfsen F, et al.Reorganisation of healthcare services for children and families: improving collabora- tion, service quality, and worker well-being. J Interprof Care. 2017; 31:487-496. [PMID: 28481168] doi:10.1080/13561820.2017.1316249 99.Mazurkiewicz RA, Smith KL, Korenstein D, Ripp J.The impact of resident physician burnout on the quality of care of hospitalized pa- tients. J Gen Intern Med. 2012;27:S323-S4. 100.Mion G, Libert N, Journois D.[Burnout-associated factors in anesthesia and intensive care medicine. 2009 survey of the French Society of Anesthesiology and Intensive Care]. Ann Fr Anesth Re- anim. 2013;32:175-88. [PMID: 23395149] doi:10.1016/j.annfar.2012 .12.004 101.Mohr DC, Eaton JL, Meterko M, et al.Factors associated with internal medicine physician job attitudes in the Veterans Health Ad- ministration. BMC Health Serv Res. 2018;18:244. [PMID: 29622008] doi:10.1186/s12913-018-3015-z 102.Molina Siguero A, Garcı´a Pe´ rez MA, Alonso Gonza´ lez M, et al. [Prevalence of worker burnout and psychiatric illness in primary care physicians in a health care area in Madrid]. Aten Primaria. 2003;31: 564-71. [PMID: 12783745] 103.Nantsupawat A, Nantsupawat R, Kunaviktikul W, et al.Nurse burnout, nurse-reported quality of care, and patient outcomes in thai hospitals. J Nurs Scholarsh. 2016;48:83-90. [PMID: 26650339] doi: 10.1111/jnu.12187 104.O’Connor P, Lydon S, O’Dea A, et al.A longitudinal and multicentre study of burnout and error in Irish junior doctors. Postgrad Med J. 2017;93:660-664. [PMID: 28600343] doi:10 .1136 /postgradmedj-2016-134626 105.Panunto MR, Guirardello Ede B.Professional nursing practice: environment and emotional exhaustion among intensive care nurses. Rev Lat Am Enfermagem. 2013;21:765-72. [PMID: 23918023] doi:10.1590/S0104-11692013000300016 106.Passalacqua SA, Segrin C.The effect of resident physician stress, burnout, and empathy on patient-centered communicationduring the long-call shift. Health Commun. 2012;27:449-56. [PMID: 21970629] doi:10.1080/10410236.2011.606527 107.Patrician PA, Shang J, Lake ET.Organizational determinants of work outcomes and quality care ratings among Army Medical De- partment registered nurses. Res Nurs Health. 2010;33:99-110. [PMID: 20151409] doi:10.1002/nur.20370 108.Pedersen AF, Carlsen AH, Vedsted P.Association of GPs’ risk attitudes, level of empathy, and burnout status with PSA testing in primary care. Br J Gen Pract. 2015;65:e845-51. [PMID: 26541183] doi:10.3399/bjgp15X687649 109.Poghosyan L, Clarke SP, Finlayson M, et al.Nurse burnout and quality of care: cross-national investigation in six countries. Res Nurs Health. 2010;33:288-98. [PMID: 20645421] doi:10.1002/nur.20383 110.Pratt M, Kerr M, Wong C.The impact of ERI, burnout, and car- ing for SARS patients on hospital nurses’ self-reported compliance with infection control. Can J Infect Control. 2009;24:167-72, 174. [PMID: 19891170] 111.Prins JT, van der Heijden FM, Hoekstra-Weebers JE, et al.Burn- out, engagement and resident physicians’ self-reported errors. Psy- chol Health Med. 2009;14:654-66. [PMID: 20183538] doi:10.1080/ 13548500903311554 112.Profit J, Sharek PJ, Amspoker AB, et al.Burnout in the NICU setting and its relation to safety culture. BMJ Qual Saf. 2014;23:806- 13. [PMID: 24742780] doi:10.1136/bmjqs-2014-002831 113.Qureshi HA, Rawlani R, Mioton LM, et al.Burnout phenomenon in U.S. plastic surgeons: risk factors and impact on quality of life. Plast Reconstr Surg. 2015;135:619-26. [PMID: 25357156] doi:10.1097/PRS .0000000000000855 114.Rafferty AM, Ball J, Aiken LH.Are teamwork and professional autonomy compatible, and do they result in improved hospital care? Qual Health Care. 2001;10 Suppl 2:ii32-7. [PMID: 11700377] 115.Ridley J, Wilson B, Harwood L, et al.Work environment, health outcomes and magnet hospital traits in the Canadian nephrology nursing scene. CANNT J. 2009;19:28-35. [PMID: 19354155] 116.Riquelme I, Chaco´ n JI, Ga´ ndara AV, et al; PAINBO Study Group.Prevalence of burnout among pain medicine physicians and its potential effect upon clinical outcomes in patients with oncologic pain or chronic pain of nononcologic origin. Pain Med. 2018;19: 2398-2407. [PMID: 29361180] doi:10.1093/pm/pnx335 117.Rochefort CM, Clarke SP.Nurses’ work environments, care ra- tioning, job outcomes, and quality of care on neonatal units. J Adv Nurs. 2010;66:2213-24. [PMID: 20626479] doi:10.1111/j.1365-2648 .2010.05376.x 118.Salyers MP, Fukui S, Rollins AL, et al.Burnout and self-reported quality of care in community mental health. Adm Policy Ment Health. 2015;42:61-9. [PMID: 24659446] 119.Schmidt SG, Dichter MN, Bartholomeyczik S, et al.The satisfac- tion with the quality of dementia care and the health, burnout and work ability of nurses: a longitudinal analysis of 50 German nursing homes. Geriatr Nurs. 2014;35:42-6. [PMID: 24131899] doi:10.1016 /j.gerinurse.2013.09.006 120.Schwartz SP, Adair KC, Bae J, et al.Work-life balance behav- iours cluster in work settings and relate to burnout and safety culture: a cross-sectional survey analysis. BMJ Qual Saf. 2019;28:142-150. [PMID: 30309912] doi:10.1136/bmjqs-2018-007933 121.Shanafelt TD, Balch CM, Bechamps G, et al.Burnout and med- ical errors among American surgeons. Ann Surg. 2010;251:995- 1000. [PMID: 19934755] doi:10.1097/SLA.0b013e3181bfdab3 122.Shanafelt TD, Bradley KA, Wipf JE, et al.Burnout and self- reported patient care in an internal medicine residency program. Ann Intern Med. 2002;136:358-67. [PMID: 11874308] 123.Shields CG, Fuzzell LN, Christ SL, et al.Patient and provider characteristics associated with communication about opioids: an ob- servational study. Patient Educ Couns. 2019;102:888-894. [PMID: 30552013] doi:10.1016/j.pec.2018.12.005 124.Shirom A, Nirel N, Vinokur AD.Overload, autonomy, and burn- out as predictors of physicians’ quality of care. J Occup Health Psy- chol. 2006;11:328-42. [PMID: 17059297] 125.Sillero-Sillero A, Zabalegui A.Safety and satisfaction of patients with nurse’s care in the perioperative. Rev Lat Am Enfermagem. Burnout and Quality of Care R EVIEW Annals.orgAnnals of Internal Medicine•Vol. 171 No. 8•15 October 2019565 2019;27:e3142. [PMID: 31038636] doi:10.1590/1518-8345.2646 .3142 126.Sokolova O, Pogosova N, Isakova S, et al.Impact of primary care physicians burnout on their adherence to national guidelines for common CVD. Global Heart. 2018;13:486. 127.Squires M, Tourangeau A, Spence Laschinger HK, et al.The link between leadership and safety outcomes in hospitals. J Nurs Manag. 2010;18:914-25. [PMID: 21073565] doi:10.1111/j.1365-2834.2010 .01181.x 128.Sturm H, Rieger MA, Martus P, et al; WorkSafeMed Consortium. Do perceived working conditions and patient safety culture correlate with objective workload and patient outcomes: a cross-sectional ex- plorative study from a German university hospital. PLoS One. 2019; 14:e0209487. [PMID: 30608945] doi:10.1371/journal.pone.0209487 129.Sulaiman CFC, Henn P, Smith S, et al.Burnout syndrome among non-consultant hospital doctors in Ireland: relationship with self-reported patient care. Int J Qual Health Care. 2017;29:679-684. [PMID: 28992145] doi:10.1093/intqhc/mzx087 130.Sun BZ, Chaitoff A, Hu B, et al.Empathy, burnout, and antibiotic prescribing for acute respiratory infections: a cross-sectional primary care study in the US. Br J Gen Pract. 2017;67:e565-e571. [PMID: 28717000] doi:10.3399/bjgp17X691901 131.Tawfik DS, Profit J, Morgenthaler TI, et al.Physician burnout, well-being, and work unit safety grades in relationship to reported medical errors. Mayo Clin Proc. 2018;93:1571-1580. [PMID: 30001832] doi:10.1016/j.mayocp.2018.05.014 132.Teng CI, Shyu YI, Chiou WK, et al.Interactive effects of nurse- experienced time pressure and burnout on patient safety: a cross- sectional survey. Int J Nurs Stud. 2010;47:1442-50. [PMID: 20472237] doi:10.1016/j.ijnurstu.2010.04.005 133.Toral-Villanueva R, Aguilar-Madrid G, Jua´ rez-Pe´ rez CA.Burnout and patient care in junior doctors in Mexico City. Occup Med (Lond). 2009;59:8-13. [PMID: 18796698] doi:10.1093/occmed/kqn122 134.Trockel M, Bohman B, Lesure E, et al.A brief instrument to assess both burnout and professional fulfillment in physicians: reli- ability and validity, including correlation with self-reported medical errors, in a sample of resident and practicing physicians. Acad Psy- chiatry. 2018;42:11-24. [PMID: 29196982] doi:10.1007/s40596-017 -0849-3 135.Tsiga E, Panagopoulou E, Montgomery A.Examining the link between burnout and medical error: a checklist approach. Burnout Research. 2017;6:1-8. 136.Van Bogaert P, Clarke S, Roelant E, et al.Impacts of unit-level nurse practice environment and burnout on nurse-reported out- comes: a multilevel modelling approach. J Clin Nurs. 2010;19:1664- 74. [PMID: 20579204] doi:10.1111/j.1365-2702.2009.03128.x 137.Van Bogaert P, Clarke S, Wouters K, et al.Impacts of unit-level nurse practice environment, workload and burnout on nurse- reported outcomes in psychiatric hospitals: a multilevel modelling approach. Int J Nurs Stud. 2013;50:357-65. [PMID: 22695484] doi: 10.1016/j.ijnurstu.2012.05.006 138.Van Bogaert P, Dilles T, Wouters K, et al.Practice environment, work characteristics and levels of burnout as predictors of nurse re- ported job outcomes, quality of care and patient adverse events: a study across residential aged care services. Open Journal of Nurs- ing. 2014;4:343-55. 139.Van Bogaert P, Kowalski C, Weeks SM, et al.The relationship between nurse practice environment, nurse work characteristics, burnout and job outcome and quality of nursing care: a cross- sectional survey. Int J Nurs Stud. 2013;50:1667-77. [PMID: 23777786] doi:10.1016/j.ijnurstu.2013.05.010 140.Van Bogaert P, Meulemans H, Clarke S, et al.Hospital nurse practice environment, burnout, job outcomes and quality of care: test of a structural equation model. J Adv Nurs. 2009;65:2175-85. [PMID: 20568322] 141.Van Bogaert P, Timmermans O, Weeks SM, et al.Nursing unit teams matter: impact of unit-level nurse practice environment, nurse work characteristics, and burnout on nurse reported job outcomes, and quality of care, and patient adverse events—a cross-sectional sur-vey. Int J Nurs Stud. 2014;51:1123-34. [PMID: 24444772] doi:10.1016/j .ijnurstu.2013.12.009 142.Bogaert PV, Heusden DV, Slootmans S, et al.Staff empower- ment and engagement in a magnet® recognized and joint commis- sion international accredited academic centre in Belgium: a cross- sectional survey. BMC Health Serv Res. 2018;18:756. [PMID: 30285735] doi:10.1186/s12913-018-3562-3 143.Van Gerven E, Vander Elst T, Vandenbroeck S, et al.Increased risk of burnout for physicians and nurses involved in a patient safety incident. Med Care. 2016;54:937-43. [PMID: 27213542] doi:10 .1097/MLR.0000000000000582 144.Vifladt A, Simonsen BO, Lydersen S, et al.The association be- tween patient safety culture and burnout and sense of coherence: a cross-sectional study in restructured and not restructured intensive care units. Intensive Crit Care Nurs. 2016;36:26-34. [PMID: 27212614] doi:10.1016/j.iccn.2016.03.004 145.Vogus TJ, Cooil B, Sitterding M, et al.Safety organizing, emotional exhaustion, and turnover in hospital nursing units. Med Care. 2014;52:870-6. [PMID: 25222533] doi:10.1097/MLR .0000000000000169 146.Wawrzyniak AJ, Rodriguez AE.The association between physi- cian burnout and satisfaction on health outcomes in HIV-infected outpatients. Psychosomatic Medicine. 2017;79:A102. 147.Weigl M, Schneider A, Hoffmann F, et al.Work stress, burnout, and perceived quality of care: a cross-sectional study among hospi- tal pediatricians. Eur J Pediatr. 2015;174:1237-46. [PMID: 25846697] doi:10.1007/s00431-015-2529-1 148.Welp A, Meier LL, Manser T.Emotional exhaustion and work- load predict clinician-rated and objective patient safety. Front Psy- chol. 2014;5:1573. [PMID: 25657627] doi:10.3389/fpsyg.2014 .01573 149.Welp A, Meier LL, Manser T.The interplay between teamwork, clinicians’ emotional exhaustion, and clinician-rated patient safety: a longitudinal study. Crit Care. 2016;20:110. [PMID: 27095501] doi:10 .1186/s13054-016-1282-9 150.Wen J, Cheng Y, Hu X, et al.Workload, burnout, and medical mistakes among physicians in China: a cross-sectional study. Biosci Trends. 2016;10:27-33. [PMID: 26961213] doi:10.5582/bst.2015 .01175 151.West CP, Huschka MM, Novotny PJ, et al.Association of per- ceived medical errors with resident distress and empathy: a prospec- tive longitudinal study. JAMA. 2006;296:1071-8. [PMID: 16954486] 152.West CP, Tan AD, Habermann TM, et al.Association of resident fatigue and distress with perceived medical errors. JAMA. 2009;302: 1294-300. [PMID: 19773564] doi:10.1001/jama.2009.1389 153.Williams ES, Manwell LB, Konrad TR, et al.The relationship of organizational culture, stress, satisfaction, and burnout with physician-reported error and suboptimal patient care: results from the MEMO study. Health Care Manage Rev. 2007;32:203-12. [PMID: 17666991] 154.Winning AM, Merandi JM, Lewe D, et al.The emotional impact of errors or adverse events on healthcare providers in the NICU: the protective role of coworker support. J Adv Nurs. 2018;74:172-180. [PMID: 28746750] doi:10.1111/jan.13403 155.Yanos PT, Vayshenker B, DeLuca JS, et al.Development and validation of a scale assessing mental health clinicians’ experiences of associative stigma. Psychiatr Serv. 2017;68:1053-1060. [PMID: 28617207] doi:10.1176/appi.ps.201600553 156.Yassi A, Cohen M, Cvitkovich Y, et al.Factors associated with staff injuries in intermediate care facilities in British Columbia, Can- ada. Nurs Res. 2004;53:87-98. [PMID: 15084993] 157.You LM, Aiken LH, Sloane DM, et al. Hospital nursing, care quality, and patient satisfaction: cross-sectional surveys of nurses and patients in hospitals in China and Europe. Int J Nurs Stud. 2013;50: 154-61. [PMID: 22658468] doi:10.1016/j.ijnurstu.2012.05.003 158.Yuguero O, Marsal JR, Buti M, et al.Descriptive study of asso- ciation between quality of care and empathy and burnout in primary care. BMC Med Ethics. 2017;18:54. [PMID: 28950853] doi:10.1186 /s12910-017-0214-9 R EVIEW Burnout and Quality of Care 566Annals of Internal Medicine•Vol. 171 No. 8•15 October 2019Annals.org 159.Zarei E, Khakzad N, Reniers G, et al.On the relationship be- tween safety climate and occupational burnout in healthcare organi- zations. Safety Science. 2016;89:1-10. 160.Tawfik DS, Sexton JB, Kan P, et al.Burnout in the neonatal intensive care unit and its relation to healthcare-associated infec- tions. J Perinatol. 2017;37:315-320. [PMID: 27853320] doi:10.1038 /jp.2016.211 161.Van Gerven E, Vander Elst T, Vandenbroeck S, et al.Increased risk of burnout for physicians and nurses involved in a patient safety incident. Med Care. 2016;54:937-43. [PMID: 27213542] doi:10 .1097/MLR.0000000000000582 162.Sari AB, Sheldon TA, Cracknell A, et al.Sensitivity of routine system for reporting patient safety incidents in an NHS hospital: ret- rospective patient case note review. BMJ. 2007;334:79. [PMID: 17175566] 163.Ioannidis JP, Patsopoulos NA, Rothstein HR.Reasons or ex- cuses for avoiding meta-analysis in forest plots. BMJ. 2008;336: 1413-5. [PMID: 18566080] doi:10.1136/bmj.a117164.Dodd S, Clarke M, Becker L, et al.A taxonomy has been devel- oped for outcomes in medical research to help improve knowledge discovery. J Clin Epidemiol. 2018;96:84-92. [PMID: 29288712] doi: 10.1016/j.jclinepi.2017.12.020 165.Dal-Re´ R, Ioannidis JP, Bracken MB, et al.Making prospective registration of observational research a reality. Sci Transl Med. 2014; 6:224cm1. [PMID: 24553383] doi:10.1126/scitranslmed.3007513 166.Parshuram CS, Amaral AC, Ferguson ND, et al; Canadian Crit- ical Care Trials Group.Patient safety, resident well-being and conti- nuity of care with different resident duty schedules in the intensive care unit: a randomized trial. CMAJ. 2015;187:321-9. [PMID: 25667258] doi:10.1503/cmaj.140752 167.West CP, Dyrbye L, Satele D, et al.A randomized controlled trial evaluating the effect of Compass (Colleagues Meeting to Promote and Sustain Satisfaction) small group sessions on physician well- being, meaning, and job satisfaction. J Gen Intern Med. 2015;30: S89. INFORMATION FOR AUTHORS TheAnnalsInformation for Authors section is available at www.annals.org /aim/pages/authors. All manuscripts must be submitted electronically us- ing the manuscript submission option at Annals.org. 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.

Writerbay.net

Do you need academic writing help? Our quality writers are here 24/7, every day of the year, ready to support you! Instantly chat with a customer support representative in the chat on the bottom right corner, send us a WhatsApp message or click either of the buttons below to submit your paper instructions to the writing team.


Order a Similar Paper Order a Different Paper
Writerbay.net