Dataset Information

Teamworking in endoscopy: a human factors toolkit for the COVID-19 era.

ABSTRACT:

Background

Endoscopy services have had to rapidly adapt their working practices in response to COVID-19. As recovery of endoscopy services proceeds, our workforce faces numerous challenges that can impair effective teamworking. We designed and developed a novel toolkit to support teamworking in endoscopy during the pandemic.

Methods

A human factors model was developed to understand the impact of COVID-19 on endoscopy teams. From this, we identified a set of key teamworking goals, which informed the development of a toolkit to support several team processes. The toolkit was refined following expert input and refinement over a 6-week period.

Results

The toolkit consists of four cognitive aids that can be used to support team huddles, briefings, and debriefs, alongside techniques to optimize endoscopic nontechnical skills across the patient-procedure pathway. We describe the processes that local endoscopy units can employ to implement this toolkit.

Conclusion

A toolkit of cognitive aids, based on human factors principles, may be useful in supporting teams, helping them adapt to working safely in the era of COVID-19.

SUBMITTER: Ravindran S

PROVIDER: S-EPMC7516366 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

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Publications

Teamworking in endoscopy: a human factors toolkit for the COVID-19 era.

Ravindran Srivathsan S Matharoo Manmeet M Coleman Mark M Marshall Sarah S Healey Chris C Penman Ian I Thomas-Gibson Siwan S

Endoscopy 20200710 10

<h4>Background</h4>Endoscopy services have had to rapidly adapt their working practices in response to COVID-19. As recovery of endoscopy services proceeds, our workforce faces numerous challenges that can impair effective teamworking. We designed and developed a novel toolkit to support teamworking in endoscopy during the pandemic.<h4>Methods</h4>A human factors model was developed to understand the impact of COVID-19 on endoscopy teams. From this, we identified a set of key teamworking goals, ...[more]

PMID: 32572861

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Project description:The emergence of COVID-19 has had a global and profound impact, not only on society as a whole, but also on the lives of individuals. Various prevention measures were introduced around the world to limit the transmission of the disease, including face masks, mandates for social distancing and regular disinfection in public spaces, and the use of screening applications. These developments also triggered the need for novel and improved computer vision techniques capable of (i) providing support to the prevention measures through an automated analysis of visual data, on the one hand, and (ii) facilitating normal operation of existing vision-based services, such as biometric authentication schemes, on the other. Especially important here, are computer vision techniques that focus on the analysis of people and faces in visual data and have been affected the most by the partial occlusions introduced by the mandates for facial masks. Such computer vision based human analysis techniques include face and face-mask detection approaches, face recognition techniques, crowd counting solutions, age and expression estimation procedures, models for detecting face-hand interactions and many others, and have seen considerable attention over recent years. The goal of this survey is to provide an introduction to the problems induced by COVID-19 into such research and to present a comprehensive review of the work done in the computer vision based human analysis field. Particular attention is paid to the impact of facial masks on the performance of various methods and recent solutions to mitigate this problem. Additionally, a detailed review of existing datasets useful for the development and evaluation of methods for COVID-19 related applications is also provided. Finally, to help advance the field further, a discussion on the main open challenges and future research direction is given at the end of the survey. This work is intended to have a broad appeal and be useful not only for computer vision researchers but also the general public.

Dataset Information

Teamworking in endoscopy: a human factors toolkit for the COVID-19 era.

Background

Methods

Results

Conclusion

Publications

Teamworking in endoscopy: a human factors toolkit for the COVID-19 era.

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