hands-on time within first 180 s
ICU, intensive care unit.
Studies, effect sizes and moderator variables included in the meta-analytical database
Authors | Year | Study goal | Setting | No. of teams | Professional composition | Team famil-iarity | Average team size | Task type | Patient realism | Perfor-mance measure |
Amacher | 2017 | 0.11 | Emergency medicine | 72 | Uniprofessional | Experi-mental | 3 | Non-routine | Simulated | Process |
Brogaard | 2018 | 0.43 | Obstetrics | 99 | Interprofessional | Real | 5 | Non-routine | Real | Process |
Burtscher | 2011 | −0.27 | Anaesthesia | 31 | Interprofessional | Experi-mental | 2 | Routine | Simulated | Process |
Burtscher | 2011 | 0.19 | Anaesthesia | 15 | Interprofessional | Experi-mental | 2 | Routine & non-routine | Simulated | Process |
Burtscher | 2010 | 0.07 | Anaesthesia | 22 | Interprofessional | Real | 3 | Non-routine | Real | Process |
Carlson * | 2009 | 0.83 | Emergency medicine | 44 | Uniprofessional | Experi-mental | 2.6 | Non-routine | Simulated | Process |
Catchpole | 2007 | .45† | Surgery | 24 | Interprofessional | Real | 9 | Non-routine | Real | Process |
Catchpole | 2007 | .29† | Surgery | 18 | Interprofessional | Real | 5 | Routine | Real | Process |
Catchpole | 2008 | .36† | Surgery | 26 | Interprofessional | Real | Routine | Real | Process | |
Catchpole | 2008 | .09† | Surgery | 22 | Interprofessional | Real | Routine | Real | Process | |
Cooper | 1999 | 0.50 | General care | 20 | Interprofessional | Real | 4 | Routine | Real | Process |
Davenport | 2007 | 0.17 | Surgery | 52 | Interprofessional | Real | Routine | Real | Outcome | |
El Bardissi | 2008 | 0.67 | Surgery | 31 | Interprofessional | Real | 7 | Routine | Real | Process |
Gillespie | 2012 | 0.23 | Surgery | 160 | Interprofessional | Real | 6 | Routine | Real | Process |
Kolbe | 2012 | 0.33 | Anaesthesia | 31 | Interprofessional | Real | 2 | Non-routine | Simulated | Process |
Künzle | 2009 | 0.56 | Anaesthesia | 12 | Interprofessional | Real | 2 | Routine | Simulated | Process |
Manojlovich | 2009 | 0.11 | Intensive care | 25 | Uniprofessional | Real | 36 | Routine | Real | Outcome |
Manser | 2015 | 0.39 | Surgery | 19 | Interprofessional | Experi-mental | 5 | Routine | Simulated | Process |
Marsch | 2004 | 0.23 | Intensive care | 16 | Interprofessional | Experi-mental | 3 | Non-routine | Simulated | Process |
Mazzocco | 2009 | 0.11 | Surgery | 293 | Interprofessional | Real | 6 | Routine | Real | Outcome |
Mishra | 2008 | 0.05 | Surgery | 26 | Interprofessional | Real | 6 | Routine | Real | Process |
Schmutz | 2015 | 0.12 | Emergency medicine | 68 | Interprofessional | Real | 6 | Non-routine | Simulated | Process |
Siassakos | 2012 | 0.66 | Obstetrics | 19 | Interprofessional | Real | 6 | Non-routine | Simulated | Process |
Siassakos | 2011 | 0.55 | Emergency medicine/ obstetrics | 24 | Interprofessional | Experi-mental | 6 | Non-routine | Simulated | Process |
Thomas | 2006 | 0.23 | Neonatal care | 132 | Interprofessional | Real | 5 | Non-routine | Real | Process |
Tschan | 2006 | 0.23 | Emergency medicine | 21 | Interprofessional | Experi-mental | 5 | Non-routine | Simulated | Process |
Tschan | 2009 | 0.37 | Emergency medicine | 20 | Uniprofessional | Experi-mental | 2.65 | Non-routine | Simulated | Outcome |
Westli | 2010 | 0.18 | Emergency medicine | 27 | Interprofessional | Real | 5.1 | Non-routine | Simulated | Process |
Wiegmann | 2007 | 0.56 | Surgery | 31 | Interprofessional | Real | Routine | Real | Process | |
Williams | 2010 | 0.18 | Neonatal care | 12 | Interprofessional | Real | 5 | Non-routine | Real | Process |
Wright | 2009 | 0.81 | General care | 9 | Uniprofessional | Experi-mental | 4 | Non-routine | Simulated | Process |
Yamada | 2016 | 0.11 | Emergency medicine | 13 | Interprofessional | Experi-mental | 3 | Non-routine | Simulated | Process |
*Carlson, Min & Bridges has been identified as an outlier and therefore excluded from the analysis.
†Effect sizes ( r ) with an † represent an average for a single sample and a single outcome and have been combined for this meta-analysis.
For the criterion level of analysis, we included only effect sizes at the team level and not on an individual level. Therefore, the performance measure had to be clearly linked to a team. This approach aligns with research that strongly recommends against mixing levels of analysis in meta-analytical integrations. 49 50
Two reviewers independently screened titles and abstracts from articles yielded in the search. Afterwards full texts of all relevant articles were obtained and screened by the same two reviewers. Agreement was above 90%. Any disagreement in the selection process was resolved through consensus discussion.
With the help of a jointly developed coding scheme, studies were independently coded by one of the authors (JS) and another rater, both with a background in industrial psychology and human factors. Twenty per cent of the studies were rated by both coders. Intercoder agreement was above 90%. Any disagreement was resolved through discussion. The data extracted comprised details of the authors and publication as well as important study characteristics and statistical relationships between a teamwork variable and performance ( table 2 ).
The professional composition of teams was coded either as ‘Interprofessional’ if a team consisted of members from different professions (eg, nurses and physicians) or as ‘Uniprofessional’ if the members of the teams were of the same profession. Team size was coded as the number of members (average number if team size varied) of the investigated teams. Team familiarity was coded either as ‘experimental’ or ‘real’. ‘Real’ indicates that the team members also worked together in their everyday clinical practice. ‘Experimental’ means that the teams only worked together during the study.
Task type was coded either as ‘Routine task’ or ‘Non-routine task’. We defined ‘Non-routine tasks’ as unexpected events that require flexible behaviour often under time-pressure (eg, emergency situations). ‘Routine tasks’ describe previously planned standard procedures (eg, standard anaesthesia induction, planned surgery).
Patient realism was either coded as ‘Real patient’ or ‘Simulated patient’. ‘Simulated patient’ included a patient simulator (manikin) whereas ‘Real patient’ included real patients in clinical settings.
Clinical performance measures were coded either as ‘Outcome performance’ or ‘Process performance’. 38 51 ‘Outcome performance’ includes an outcome that is measured after the treatment process (eg, infection rate, mortality). We focused only on patient-related outcomes and not on team outcomes (eg, team satisfaction). ‘Process performance’ describes the evaluation of the treatment process and describes how well the process was executed (eg, adherence to guidelines through expert rating). Process performance measures are often based on official guidelines and extensive expert knowledge. 52 Thus, we assumed that process performance closely relates to patient outcomes.
Different types of effect sizes (eg, OR, F values and r ) have been reported in the original studies. We therefore converted the different effect sizes to a common metric, namely r using the formulas provided by Borenstein et al 53 and Walker. 54 Moreover, some samples contained effect sizes of teamwork with two or more measures of performance. Because independence of the included effects sizes is required for a meta-analysis, 41 55 we used Fisher’s z score to average the multiple correlations from the same sample (scholars have suggested to convert r to Fisher's z scores, to average the z’s and then to backtransform it to r . 56 Using simple arithmetic average (ie, correlations will be summed and divided by the number of coefficients) is problematic because the distribution of r becomes negatively skewed as the correlation is larger than zero. As a result, the average r tends to underestimate the population correlation). The correlations were weighted for sample size. However, in contrast to many meta-analyses in social sciences, the correlations were not adjusted for measurement reliability. This is because information about the measurement reliability could not be compared (Kappa vs Cronbach’s Alpha) or were not available at all for the majority of studies. Therefore, we report uncorrected, sample-size weighted mean correlation, its 95% CI, and the 80% credibility interval (CR). The CI reflects the accuracy of a point estimate and can be used to examine the significance of the effect size estimates, whereas the CR refers to the deviation of these estimates and informs us about the existence of possible moderators.
Random-effects models were estimated based on two considerations. 57 First, we expected study heterogeneity to be high given the different study design characteristics such as patient realism (‘Real patient’ vs ‘Simulated patient’), task type (‘Routine task’ vs ‘Non-routine task’) and different forms of performance measures. Second, we aimed to provide an inference on the average effect in the entire population of studies from which the included studies are assumed to be a random selection of it. Therefore, random-effects models were estimated. 57 These models were calculated by the restricted maximum-likelihood estimator, an efficient and unbiased estimator. 58 Since we included only descriptive studies and no interventions we only included the sample size of the individual studies as a potential bias into the meta-analysis. To rule out a potential publication bias, we tested for funnel plot asymmetry using the random-effect version of the Egger test. 59 The results indicate that there is no asymmetry in the funnel plot (z=1.79, p=0.074), suggesting that there is no publication bias.
The estimation of meta-analytical models including the outlier analyses were performed with the package ‘metafor’ from the programming language and statistical environment R. 58
The online search resulted in 2002 articles ( figure 1 ). Two studies were identified via contacting authors directly and have been presented at conferences in the past. 60 61 After duplicates were removed 1988 articles were screened using title and abstract. Sixty-seven articles were then selected for a full text review. Full text examination, forward and backward search of selected articles and relevant reviews resulted in 30 studies coming from 28 articles (two publications presented two independent studies in one publication 62 63 ). This led to a total of 32 studies coming from 30 articles. Following the recommendation by Viechtbauer and Cheung, 64 we screened for outliers using studentized deleted residuals. One case (Carlson et al , 9 r =0.89, n=44, studentized deleted residuals=4.26) was identified as outlier and therefore excluded from further analyses, resulting in a final sample size of k =31.
Systematic literature search.
Table 1 provides a qualitative description of the selected articles including study objectives, the setting in which the studies were carried out and a description of the teamwork processes as well as the outcome measures that were assessed. If a specific tool for the assessment of a teamwork process or outcome measure was used this is indicated in the corresponding column. Observational studies were most prevalent. Teamwork processes were assessed using either behaviourally anchored rating scales (n=8) or structured observation (n=19) of specific teamwork behaviour. Structured observation — as we describe it — is defined as a purely descriptive assessment of certain behaviour usually using a predefined observation system (eg, amount of speaking up behaviour). In contrast, behaviourally anchored rating scales consist of an evaluation of teamwork process behaviour by an expert. Only three studies used surveys to assess teamwork behaviours. The majority of the studies (n = 27) assessed process performance using either a checklist based expert rating or assessing a reaction time measure after the occurrence of a certain event (eg, time until intervention). Only four studies assessed outcome performance measures. Measures included accuracy of diagnosis, postoperative complications and death, surgical morbidity and mortality, ventilator-associated pneumonia, bloodstream infections, pressure ulcers and acute physiology and chronic health evaluation score. Table 2 provides an overview of all variables included in the meta-analysis including the effect sizes and moderator variables.
Table 3 and figure 2 shows the relationship between teamwork and team performance. The sample-sized weighted mean correlation was 0.28 (95% CI 0.20 to 0.35, z =6.55, p<0.001), indicating that teamwork is positively related to clinical performance. Results further indicated heterogeneous effect size distributions across the included samples ( Q =53.73, p<0.05, I 2 =45.96), signifying that the variability across the sample effect sizes was more than what would be expected from sampling error alone.
Relationship between teamwork processes and performance.
Meta-analytical relationships between teamwork and clinical performance
N | k | r | 95% CI | 80% CR | Q | I | |
Overall relationship | 1390 | 31 | 0.28* | (0.20 to 0.35) | (0.09 to 0.45) | 53.7* | 46.0 |
Team characteristics | |||||||
Professional composition | |||||||
Interprofessional | 1264 | 27 | 0.28* | (0.20 to 0.36) | (0.09 to 0.46) | 47.1* | 48.2 |
Uniprofessional | 126 | 4 | 0.28 | (−0.01 to 0.52) | (−0.04 to 0.54) | 6.5 | 47.1 |
Team familiarity | |||||||
Experimental team | 240 | 10 | 0.25* | (0.05 to 0.43) | (−0.05 to 0.51) | 17.2* | 47.2 |
Real team | 1150 | 21 | 0.29* | (0.20 to 0.37) | (0.12 to 0.45) | 36.2* | 45.7 |
Team size† | |||||||
Task characteristics | |||||||
Task type | |||||||
Routine task | 766 | 14 | 0.27* | (0.12 to 0.40) | (−0.01 to 0.50) | 30.9* | 65.0 |
Non-routine task | 609 | 16 | 0.29* | (0.20 to 0.39) | (0.16 to 0.42) | 20.5 | 24.6 |
Methodological factors | |||||||
Patient realism | |||||||
Real patient | 993 | 16 | 0.28* | (0.18 to 0.38) | (0.10 to 0.45) | 28.7* | 49.3 |
Simulated patient | 397 | 15 | 0.28* | (0.13 to 0.41) | (0.02 to 0.50) | 25.0* | 44.6 |
Performance measures | |||||||
Outcome performance | 390 | 4 | 0.13* | (0.03 to 0.23) | (0.06 to 0.19) | 1.3 | 0.0 |
Process performance | 1000 | 27 | 0.30* | (0.21 to 0.39) | (0.10 to 0.49) | 45.6* | 45.6 |
* p < .05.
I 2 = % of total variability in the effect size estimates due to heterogeneity among true effects (vs sampling error).
†Team size was entered as a continuous variable, therefore, no subgroup analyses exist.
CI, confidence interval; CR, credibility interval; K, number of studies; N, cumulative sample size (number of teams); Q, test statistic for residual heterogeneity of the models; r, sample-size weighted correlation.
To test for moderator effects of the contextual factors, we conducted mixed-effects models including the mentioned moderators: professional composition, team familiarity, team size, task type, patient realism and performance measures .
The omnibus test of moderators was not significant ( F =0.18, df 1 =6, df 2 =18, p>0.20), suggesting that the examined contextual factors did not influence the average effect of teamwork on clinical performance. To provide greater detail about the role of the contextual factors, we conducted separate analyses for the categorical contextual factors and report them in table 3 .
With this study, we aimed to provide evidence for the performance implications of teamwork in healthcare teams. By including various contextual factors, we investigated potential contingencies that these factors might have on the relationship between teamwork and clinical performance. The analysis of 1390 teams from 31 different studies showed that teamwork has a medium sized effect ( r =0.28 65 66 ;) on clinical performance across various care settings. Our study is the first to investigate this relationship quantitatively with a meta-analytical procedure. This finding aligns with and advances previous work that explored this relationship in a qualitative way. 8 15 17 43–47
At first glance a correlation of r =0.28 might not seem very high. However, we would like to highlight that r =0.28 is considered a medium sized effect 65 66 and should not be underestimated. To better illustrate what this effect means we transformed the correlation into an OR of 2.8. 53 Of course, this transformation simplifies the correlation because teamwork and often the outcome measures are not simple dichotomous variables that can be divided into an intervention and control group. However, this transformation illustrates that teams who engage in teamwork processes are 2.8 times more likely to achieve high performance than teams who are not. Looking at the performance measures in our study we see that they either describe patient outcomes (eg, mortality, morbidity) or are closely related to patient outcomes (eg, adherence to treatment guidelines). Thus, we consider teamwork a performance-relevant process that needs to be promoted through training and implementation into treatment guidelines and policies.
The included studies used a variety of different measures for clinical performance. This variability resulted from the different clinical contexts in which the studies were carried out. There is no universal measure for clinical performance because the outcome is in most cases context specific. In surgery, common performance measures are surgical complications, mortality or morbidity. 67 In anaesthesia, studies often use expert ratings based on checklists to assess the provision of anaesthesia. Expert ratings are also the common form of performance assessment in simulator settings where patient outcomes like morbidity or mortality cannot be measured. Future studies need to be aware that clinical performance measures depend on the clinical context and that the development of valid performance measures requires considerable effort and scientific rigour. Guidelines on how to develop performance assessment tools for specific clinical scenarios exist and need to be accounted for. 52 68 69 Furthermore, depending on the clinical setting researchers need to evaluate what specific clinical performance measures are suitable and if and how they can be linked to team processes in a meaningful way.
The analysis of moderators illustrates that teamwork is related with performance under a variety of conditions. Our results suggest that teams in different contexts characterised by different team constellations, team size and levels of acuity of care all benefit from teamwork. Therefore, clinicians and educators from all fields should strive to maintain or increase effective teamwork. In recent years, there has been an upsurge in crisis resource management (CRM). 19 These trainings focus on team management and implement various teamwork principles during crisis situations (eg, emergencies). 70 Our results suggest that team trainings should not only focus on non-routine situations like emergencies but also on routine situations (eg, routine anaesthesia induction, routine surgery) because based on our data teamwork is equally important in such situations.
A closer look at methodological factors of the included studies revealed that the observed relationship between teamwork and performance in simulation settings does not differ from relationships observed in real settings. Therefore, we conclude that teamwork studies conducted in simulation settings generalise to real life settings in acute care. Further, the analysis of different performance measures reveals a trend towards process performance measures being more strongly related with teamwork than outcome performance measures. A possible explanation of this finding relates to the difficulty of investigating outcome performance measures in a manner isolated from other variables. Nevertheless, we still found a significant relationship between teamwork and objective patient outcomes (eg, postoperative complications, bloodstream infections) despite the methodological challenges of measuring outcome performance and the small number of studies using outcome performance ( k =4).
Our results are in line with previous meta-analyses investigating the effectiveness of team training in healthcare. 18 19 Similar to our results, Hughes et al highlighted the effectiveness of team trainings under a variety of conditions — irrespective of team composition, 18 simulator fidelity or patient acuity of the trainee’s unit as well as other factors.
We were unable to find a moderation of task type in our study, potentially explained by task interdependence, which reflects the degree to which team members depend on one another for their effort, information and resources. 71 A meta-analysis including teams from multiple industries (eg, project teams, management teams) found that task interdependence moderates the relationship between teamwork and performance, demonstrating the importance of teamwork for highly interdependent team tasks. 72 Most studies included in our analysis focused on rather short and intense patient care episodes (eg, a surgery, a resuscitation task) with high task interdependence, which may explain the high relevance of teamwork for all these teams.
Despite greater attention to healthcare team research and team training over the last decade, we were only able to identify 32 studies (31 included in the meta-analysis). Of note, over two-thirds of the studies in our analysis emerged in the last 10 years, reflecting the increasing interest in the topic. The rather small number of studies might relate to the difficulties in quantifying teamwork, the considerable theoretical and methodological knowledge required and the challenges of capturing relevant outcome measures. Also, besides the manual searches of selected articles and reviews and contacting authors in the field we did only search the database PubMed. PubMed is the most common database to access papers that potentially investigate medical teams and includes approximately 30 000 journals from the field of medicine, psychology and management. We are fairly confident that through the additional inclusion of relevant reviews and forward and backwards search, our results represent an accurate representation of what can be found in the literature.
Future research should build on recent theoretical and applied work 24 26 28 73 about teamwork and use this current meta-analysis as a signpost for future investigations. In order to move our field forward, we must use existing conceptual frameworks 22 24 26 and establish standards for investigating teams and teamwork. This can often only be achieved with interdisciplinary research teams including experts from the medical fields but equally important from health professions education, psychology or communication studies.
Another limitation relates to the unbalanced analysis of subgroups. For example, we only identified four studies that used outcome performance variables compared with 27 using process performance measures. Uneven groups may reduce the power to detect significant differences. Therefore, we encourage future studies to include outcome performance measures despite the effort required.
Finally, more factors may influence the relationship between teamwork and performance that we were unable to extract from the studies. While we tested for the effects of team familiarity by comparing experimental teams and real teams, this does not fully capture team member familiarity. The extent to which team members actually worked together during prior clinical practice might predict of how effectively they perform together. However, even two people working in the same ward might actually not have interacted much during patient care depending on the setting. Also team climate on a ward or in a hospital may be an important predictor of how well teams work together, especially related to sharing information or speaking up within the team. 74 75
Finally, the clinical context might play a role in how team members collaborate. In different disciplines, departments or healthcare institutions different norms and routines exist on how to work together. Therefore, study results and recommendations about teamwork need to be interpreted in the light of the respective clinical context. There are empirical indications that a one-size-fits-all approach might not be suitable and team training efforts cannot ignore the clinical context, especially the routines and norms about collaboration. 76 We acknowledge that there might be other factors surrounding healthcare teams that might potentially influence teamwork and clinical performance. However, in this review we could only extract data that was reported in the primary studies. Since these were limited in the healthcare contexts studied, the results might not generalise to long-term care settings or mental health, for example. Future work needs to consider and also document a broader range of potentially influencing factors.
The current meta-analysis confirms that teamwork across various team compositions represents a powerful process to improve patient care. Good teamwork can be achieved by joint reflection about teamwork during clinical event debriefings 77 78 as well as team trainings 79 and system improvement. All healthcare organisations should recognise these findings and place continuous efforts into maintaining and improving teamwork for the benefit of their patients.
Acknowledgments.
The authors thank Manuel Stühlinger for his help with study selection and data extraction and Walter J. Eppich, MD, PhD for a critical review and proofreading the manuscript.
Contributors: All authors substantially contributed to this study and were involved in the study design. JS drafted the paper. LM analysed the data and revised the manuscript for content. TM revised the manuscript for content and language. All authors approved the final version.
Funding: This work was funded by the European Society of Anaesthesiology (ESA) and the Swiss National Science Foundation (SNSF, Grant No. P300P1_177695). The ESA provided resources for an additional research assistant helping with literature search and selection. Part of the salary of JS was funded by the SNSF.
Competing interests: None declared.
Patient and public involvement statement: Patients and public were not involved in this study.
Patient consent for publication: Not required.
Provenance and peer review: Not commissioned; externally peer reviewed.
Data availability statement: Data are available upon reasonable request.
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A high-performing team is more than a group, and it is more than the sum of its individuals. There is one single best definition, structure, or defining characteristic of a high-performing team, although spirit and synergy are essential. There are dysfunctional teams. Teams may or may not have a defined hierarchy. Where a team has a leader, the leadership style is important, although there is no one best leadership style. In some teams, leadership is fluid or situational. The enormous complexity of modern healthcare and the enormous complexity of modern clinical healthcare demand a multidisciplinary approach. Where the multidisciplinary approach is team-based, it is reflected in patient care outcomes, patient satisfaction, and a healthy and productive workplace.
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Director, Surgical and Neurocritical Care Units, Rochester Regional Health System at Rochester General Hospital, Rochester, NY, USA
James E. Szalados
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Szalados, J.E. (2021). The Science of Teamwork in Healthcare: Importance to Patient Outcome. In: Szalados, J.E. (eds) The Medical-Legal Aspects of Acute Care Medicine. Springer, Cham. https://doi.org/10.1007/978-3-030-68570-6_8
DOI : https://doi.org/10.1007/978-3-030-68570-6_8
Published : 03 April 2021
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Teamwork quality impacts patient, staff, and organizational outcomes. Failures in teamwork are associated with a large proportion of the high rate of preventable patient harm, the quality of care provided by organizations, and staff fatigue, burnout, and turnover.
A lack of teamwork impairs interprofessional collaboration and has significant adverse effects, leading to worse patient outcomes.
We introduce a comprehensive framework for team effectiveness. Common challenges to teamwork in healthcare are identified along with evidence-based strategies for overcoming them.
The potentially harmful consequences for patients cannot be ignored: poor teamwork —such as incomplete communication and failing to use available expertise—increases the risk of medical error and decreases quality of care [2-5].
Health care teamwork is a vital part of clinical work and patient care but is poorly understood. Despite poor teamwork being cited as a major contributory factor to adverse events, we lack vital knowledge about how teamwork can be improved.
1. Will Lack of Teamwork Impact My Nursing Career? Lack of teamwork and collaboration in nursing can undoubtedly impact your nursing career. If you are not willing to work as part of a team, the likelihood of having opportunities to lead teams decreases.
Communication and Teamwork between staff and patients is essential in order to provide effective patient care and also in order to protect the best interests’ staff. This essay aims to discuss the important role that these play in nursing and the challenges that may face nurses.
These inconsistencies in the teamwork literature may lead to confusion about the importance of teamwork in healthcare, thus giving voice to critics who hinder efforts to improve teamwork. We aim to address these problems with a meta-analytical study investigating the performance implications of teamwork.
Effective teamwork in healthcare builds a positive organizational culture and improves patient safety and the outcomes of care. Nonetheless, the creation of teams is infinitely more complex than the assembly of a group of individuals and the assignment of a task.
Working in effective teams improves clinical outcomes, increases professional satisfaction, and provides crucial peer support. However, teamwork as a core value is often missing in health care, limiting the benefits we achieve.