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'You don't see what I see': Co-designing simulation to uncover and address cognitive bias in healthcare.

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Title: 'You don't see what I see': Co-designing simulation to uncover and address cognitive bias in healthcare.
Authors: Brand, Gabrielle1 Gabrielle.Brand@monash.edu, Bonnamy, James1, Dix, Samantha1, Morphet, Julia1, Molloy, Renee1, Davis, Joy2, Challis, Holly2,3, Watts, Alison2, Daniel, Michelle2, D'Astoli, Pauline2, Wise, Steve4, Sevenhuysen, Samantha1,2
Source: Medical Teacher. Jul2024, Vol. 46 Issue 7, p885-888. 4p.
Subject Terms: *Simulation methods in education, *Communication, *Cognition, Decision making & psychology, Health information services, Medical quality control, Patient safety, Medical care, Decision making in clinical medicine, Implicit bias, Adverse health care events, Medicine information services, Social stigma
Abstract: Each year, adverse events are reported in healthcare, of which many relate to healthcare workforce cognitive bias. The active involvement of workforce and consumers in the review and co-design of effective training for the healthcare workforce to recognise, monitor, and manage unconscious bias is required. We used participatory action research to co-design an innovative, interprofessional simulation based on 'real world' clinical incidents and lived experiences to improve the delivery of safe, high quality, consumer-focused healthcare. Following ethics approval, content analysis of serious adverse patient safety events involving cognitive bias was conducted. These data informed audio-recorded interviews with the healthcare workforce and consumers to explore their experiences of cognitive bias. Following thematic analysis, key themes of communication, stigma, diagnostic overshadowing, and fragmented systems were uncovered. Guided by consumers, these themes were interwoven into a simulation scenario that included real places, stories, and verbatim quotes delivered through mixed media artefacts. This heightened the immersive and experiential learning that aimed to uncover unconscious bias and help learners recognise its impact on clinical decisions and practice. To our knowledge, this is the first interprofessional, co-designed simulation to specifically address cognitive bias in current and future healthcare workforce. Plans to translate this research into a practical framework on how to work with key stakeholders (including consumers) to identify 'real-world' health service risks and co-design targeted simulations to address these gaps are described, including lessons learned. [ABSTRACT FROM AUTHOR]
Copyright of Medical Teacher is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: 'You don't see what I see': Co-designing simulation to uncover and address cognitive bias in healthcare.
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  Data: <searchLink fieldCode="JN" term="%22Medical+Teacher%22">Medical Teacher</searchLink>. Jul2024, Vol. 46 Issue 7, p885-888. 4p.
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  Data: Each year, adverse events are reported in healthcare, of which many relate to healthcare workforce cognitive bias. The active involvement of workforce and consumers in the review and co-design of effective training for the healthcare workforce to recognise, monitor, and manage unconscious bias is required. We used participatory action research to co-design an innovative, interprofessional simulation based on 'real world' clinical incidents and lived experiences to improve the delivery of safe, high quality, consumer-focused healthcare. Following ethics approval, content analysis of serious adverse patient safety events involving cognitive bias was conducted. These data informed audio-recorded interviews with the healthcare workforce and consumers to explore their experiences of cognitive bias. Following thematic analysis, key themes of communication, stigma, diagnostic overshadowing, and fragmented systems were uncovered. Guided by consumers, these themes were interwoven into a simulation scenario that included real places, stories, and verbatim quotes delivered through mixed media artefacts. This heightened the immersive and experiential learning that aimed to uncover unconscious bias and help learners recognise its impact on clinical decisions and practice. To our knowledge, this is the first interprofessional, co-designed simulation to specifically address cognitive bias in current and future healthcare workforce. Plans to translate this research into a practical framework on how to work with key stakeholders (including consumers) to identify 'real-world' health service risks and co-design targeted simulations to address these gaps are described, including lessons learned. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Medical Teacher is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1080/0142159X.2024.2313581
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      – Code: eng
        Text: English
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    Subjects:
      – SubjectFull: Simulation methods in education
        Type: general
      – SubjectFull: Communication
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      – SubjectFull: Cognition
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      – SubjectFull: Decision making & psychology
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      – SubjectFull: Medical quality control
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      – SubjectFull: Patient safety
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      – SubjectFull: Medical care
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      – SubjectFull: Decision making in clinical medicine
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      – SubjectFull: Implicit bias
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              Text: Jul2024
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