Background Around the world, the number of acute in-patient psychiatry beds has decreased while patients presenting to Emergency Departments have increased. Mental health professionals must assess patients in crisis while balancing the costs/benefits of several possible treatment options. However, clinicians’ decision-making processes are ambiguous, and clinicians do not always agree. We developed a course that explores subjectivity and bias in mental health settings, and to reinforce evidence-based guidelines for clinical practice. This poster presents the design and data from the pilot programme, which ran five times.
Methodology The course was developed by senior psychiatric clinicians with experience in both community and acute mental health. Participants were from a wide range of mental health professions. The course simulated three two-part scenarios, in which participants were asked to take a targeted history and perform a risk assessment. In each scenario, the first simulation was the initial evaluation of a psychiatric case. This was followed by group debrief which culminated in a vote on whether to admit the patient to or discharge from the hospital. This was followed by a second simulation which was the treatment pathway chosen by the group, followed by another debrief and decision around ongoing treatment options.
Results/outcomes Anonymous feedback was collected from all participants (n = 47) consisting of Likert-style questions and open responses. All participants reported that they would recommend the course to colleagues; most said it was useful for work with their client group (mean 4.61/5). Qualitative feedback was similarly very positive and focused on realism and applicability to practice.
Conclusions and recommendations Word-of-mouth from participants has resulted in ongoing demand and there are many possibilities for further development of simulation of clinical decision making, including to other clinical specialties. Further research into the different aspects of the clinical decision making, including risk assessment and bias, are clearly indicated.
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- Category: Course or curriculum evaluation/innovation/integration
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