AI-driven Mental Health Decision Support Enhances Clinician Resilience and Preparedness

Abstract

Abstract Objectives Mental health services are facing unprecedented demand, placing significant pressure on clinicians to conduct timely and effective patient assessments. Rising staff turnover and burnout threatens service quality across many countries. This study examined whether providing clinical information, collected via an AI-enabled decision support tool for mental health assessments in the UK’s National Health Service (NHS), could improve clinician wellbeing and patient assessment performance. Method We surveyed mental health clinicians (N=131) from nine NHS Mental Health Talking Therapies services on how the information provided by an AI-based decision-support tool impacted their experience with conducting clinical assessments. Clinicians reported on assessments where information from the AI tool was available, as well as when it was not (e.g., GP referrals or telephone intakes). Outcomes included clinician wellbeing, task performance, and cognitive load during assessments, with additional analyses assessing the influence of moderating factors, such as clinician experience, workload, and exposure to the tool. Results Relative to traditional methods, assessments supported by information provided by the AI tool were associated with significantly higher clinician wellbeing and task performance, and significantly lower cognitive load, irrespective of the clinician’s experience. These benefits were magnified by workload. Conclusion These findings provide evidence that AI-powered pre-assessment tools can enhance clinician experience by improving wellbeing, boosting task performance, and reducing cognitive burden. By addressing systemic drivers of burnout, such tools offer a scalable intervention to support workforce sustainability and service quality in mental health care.

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