Literature DB >> 31865827

Measuring the teamwork performance of operating room teams: a systematic review of assessment tools and their measurement properties.

Nicole Etherington1, Sarah Larrigan2, Henry Liu2, Michael Wu2, Katrina J Sullivan1, James Jung3, Sylvain Boet1,4.   

Abstract

Teamwork is fundamental to surgical patient safety but is inconsistently measured. While many tools have been developed for elective intraoperative situations, it is unclear which is the most robust. This systematic review aimed to identify tools to measure the teamwork of operating room teams. Studies were included if they examined the measurement properties of these tools. PsycINFO, Embase (via OVID), CINAHL, ERIC, Medline and Medline in Process (via OVID) were searched through to May 3, 2019, as were reference lists of included studies and previously published relevant reviews. Retrieved articles were screened and data extracted in duplicate by two independent reviewers. Quality was assessed using the COSMIN checklist. Of the 2121 references identified, 14 studies of six assessment tools were included. Tools were validated across various specialties, mostly in clinical rather than simulated settings. The Observational Teamwork Assessment for Surgery (OTAS) and Operating Theater Team Non-Technical Skills Assessment Tool (NOTECHS) were the most frequently investigated tools. Though acceptable for assessing teamwork, both NOTECHS and OTAS rely on the questionable assumption that the teamwork of a team is equivalent to the sum of individual performances. Future studies may investigate other assessment tools that assess the whole team as the unit of analysis along with the potential of these tools to provide healthcare providers with meaningful feedback in clinical practice.

Entities:  

Keywords:  Psychometrics; operating rooms; patient safety; teamwork

Year:  2019        PMID: 31865827     DOI: 10.1080/13561820.2019.1702931

Source DB:  PubMed          Journal:  J Interprof Care        ISSN: 1356-1820            Impact factor:   2.338


  1 in total

1.  Machine learning algorithm using publicly available echo database for simplified "visual estimation" of left ventricular ejection fraction.

Authors:  Michael Blaivas; Laura Blaivas
Journal:  World J Exp Med       Date:  2022-03-20
  1 in total

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