Teamwork in healthcare: Key discoveries enabling safer, high-quality care.
Open accessAbstract
Few industries match the scale of health care. In the United States alone, an estimated 85% of the population has at least 1 health care encounter annually and at least one quarter of these people experience 4 to 9 encounters annually. A single visit requires collaboration among a multidisciplinary group of clinicians, administrative staff, patients, and their loved ones. Multiple visits often occur across different clinicians working in different organizations. Ineffective care coordination and the underlying suboptimal teamwork processes are a public health issue. Health care delivery systems exemplify complex organizations operating under high stakes in dynamic policy and regulatory environments. The coordination and delivery of safe, high-quality care demands reliable teamwork and collaboration within, as well as across, organizational, disciplinary, technical, and cultural boundaries. In this review, we synthesize the evidence examining teams and teamwork in health care delivery settings in order to characterize the current state of the science and to highlight gaps in which studies can further illuminate our evidence-based understanding of teamwork and collaboration. Specifically, we highlight evidence concerning (a) the relationship between teamwork and multilevel outcomes, (b) effective teamwork behaviors, (c) competencies (i.e., knowledge, skills, and attitudes) underlying effective teamwork in the health professions, (d) teamwork interventions, (e) team performance measurement strategies, and (f) the critical role context plays in shaping teamwork and collaboration in practice. We also distill potential avenues for future research and highlight opportunities to understand the translation, dissemination, and implementation of evidence-based teamwork principles into practice. (PsycINFO Database Record
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A typical clinical team composition is represented by 3 to 4 different agents.
“we constructed a simulation using 3 to 4 different agents, representing a typical clinical team composition [31].”
From Mitigating cognitive biases in clinical decision-making through multi-agent conversations using large language models: simulation study (2024) · cites this paper as [31]
The “Teamwork Competencies in Nonepisodic Care Contexts” section describes a core bedside team of nurses, technicians, and an attending physician, supporting a simulation with several distinct roles. But the paper does not say that three to four agents represent a typical clinical team; “Health Care as a Vehicle to Advance the Science of Teams” instead describes team sizes ranging from dyads to extensive multiteam systems. Page numbers were not available in our copy. (LLM, conf=0.82)
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From Awareness to Action: A Review of Efforts to Reduce Disparities in Breast Cancer Screening
Nayyar, S., Chakole, S. V., Taksande, A. B., Prasad, R., Munjewar, P. K., & Wanjari, M. B. (2023) · Cureus · doi:10.7759/cureus.40674 · Cited by 29 · see citing works
A mental models approach for defining explainable artificial intelligence
Merry, M., Riddle, P., & Warren, J. (2021) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-021-01703-7 · Cited by 29 · see citing works
Yousaf, M., Khan, M. M., & Paracha, A. T. (2022) · Healthcare · doi:10.3390/healthcare10112258 · Cited by 29 · see citing works
Kongkar, R., Ruksakulpiwat, S., Phianhasin, L., Benjasirisan, C., Niyomyart, A., Ahmed, B. H., Puwarawuttipanit, W., Chuenkongkaew, W. L., & Adams, J. (2025) · Journal of Multidisciplinary Healthcare · doi:10.2147/jmdh.s497846 · Cited by 28 · see citing works
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Wang, Z., Liang, Q., Yan, Z., Liu, J., Liu, M., Wang, X., Wang, J., Huang, J., & Luan, X. (2023) · BMC Nursing · doi:10.1186/s12912-023-01216-w · Cited by 28 · see citing works
Understanding roles in health care through interprofessional educational experiences
Furr, S. B., Lane, S. H., Martin, D. R., & Brackney, D. E. (2020) · British Journal of Nursing · doi:10.12968/bjon.2020.29.6.364 · Cited by 28 · see citing works
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Leadership and Followership in Military Interprofessional Health Care Teams
Barry, E. S., Bader-Larsen, K. S., Meyer, H. S., Durning, S. J., & Varpio, L. (2021) · Military Medicine · doi:10.1093/milmed/usab118 · Cited by 27 · see citing works
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- Authors
- Rosen, M. A., DiazGranados, D., Dietz, A. S., Benishek, L. E., Thompson, D., Pronovost, P. J., & Weaver, S. J.
- Affiliations
- Johns Hopkins University (US)
- Virginia Commonwealth University (US)
- National Cancer Institute (UA)
- Venue
- American Psychologist
- Published
- 2018-05
- Volume
- 73
- Issue
- 4
- Pages
- 433-450
- ISSN
- 0003-066X (ISSN-L)
- 1935-990X
- PMC
- PMC6361117
- OpenAlex
- W2803733393
- Cited by
- 1,283
- References
- 106
- Citations by year
- 2026: 167 · 2025: 218 · 2024: 249 · 2023: 182 · 2022: 174 · 2021: 125 · 2020: 124 · 2019: 29 · 2018: 13 · 2017: 1
- FWCI
- 111.09
- Type
- journal article
- Language
- en
- Open access
- green
- europepmc.org
- Locations
- American Psychologist (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health (UL1TR002649)
- National Center for Advancing Translational Sciences
- Topics
- Health Sciences › Health Professions › General Health Professions › Interprofessional Education and Collaboration
- Social Sciences › Psychology › Social Psychology › Team Dynamics and Performance
- Health Sciences › Health Professions › Emergency Medical Services › Patient Safety and Medication Errors
- Keywords
- healthcare teamwork, care coordination, patient safety, quality of care, teamwork behavior, teamwork skills, team performance measurement, organizational context, multidisciplinary collaboration, evidence-based practice implementation
- MeSH terms
- Cooperative Behavior, Delivery of Health Care, Group Processes, Humans, Interprofessional Relations, Patient Care Team, Quality of Health Care
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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