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Psych CASC / OSCEPublic and community psychiatry

Psych CASC / OSCE · Public and community psychiatry

The patient who brought the app, the chatbot and the amber flag — CASC digital psychiatry station

FRANZCP/MRCPsych-style station: respond to a patient's questions about an AI risk flag, a self-sourced mental health app and an LLM chatbot, while completing a safe remote assessment and establishing a crisis pathway to a human. Communication, risk and digital literacy in one station.

communication6 min readVerification in progress

Target exams

FRANZCPMRCPsychABPNMD-DNB
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Target exams

FRANZCPMRCPsychABPNMD-DNB
Prompt
A 29-year-old man with recurrent depression attends a video review from his car outside work. For six weeks he has been using a self-guided app he found by searching an app store, plus long nightly conversations with a general-purpose AI chatbot which he says listens better than his GP. He has brought a printout showing that the service's new risk dashboard has flagged him amber, and opens by asking whether the computer thinks he is going to kill himself. He is articulate, slightly defensive about the chatbot, and has not taken his sertraline for nine days.
What the examiner is scoring

Safe remote set-up before clinical content, an honest and non-defensive explanation of what the amber flag is and is not, curiosity rather than prohibition about the chatbot, a real risk formulation independent of the algorithm, a privacy-honest app conversation, and a crisis plan that routes to a person.[1][14][18][10]

The first ninety seconds — the set-up that is itself a mark

Do this before anything clinical, every remote consultation. Confirm his physical location, who else is within earshot, a call-back number that works if the connection drops, and which emergency service covers that address. Say why you are asking: "If we get cut off while we're talking about something serious, I want to already know how to reach you."[1][2]

Then notice the car. A consultation from a work car park is not a private space. Offer to reschedule to somewhere private, or to continue by phone from a quieter spot — and note that lack of a private space is one of the four digital-access barriers, not a sign of poor engagement.[19]

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References20ShowHide
  1. [1]Torous J, et al. The evolving field of digital mental health: current evidence and implementation issues for smartphone apps, generative artificial intelligence, and virtual reality World Psychiatry, 2025.PMID 40371757
  2. [2]Fernandez E, et al. Live psychotherapy by video versus in-person: A meta-analysis of efficacy and its relationship to types and targets of treatment Clin Psychol Psychother, 2021.PMID 33826190
  3. [3]Andersson G, et al. Guided Internet-based vs. face-to-face cognitive behavior therapy for psychiatric and somatic disorders: a systematic review and meta-analysis World Psychiatry, 2014.PMID 25273302
  4. [4]Karyotaki E, et al. Internet-Based Cognitive Behavioral Therapy for Depression: A Systematic Review and Individual Patient Data Network Meta-analysis JAMA Psychiatry, 2021.PMID 33471111
  5. [5]Erbe D, et al. Blending Face-to-Face and Internet-Based Interventions for the Treatment of Mental Disorders in Adults: Systematic Review J Med Internet Res, 2017.PMID 28916506
  6. [6]Linardon J, et al. The efficacy of app-supported smartphone interventions for mental health problems: a meta-analysis of randomized controlled trials World Psychiatry, 2019.PMID 31496095
  7. [7]Baumel A, et al. Objective User Engagement With Mental Health Apps: Systematic Search and Panel-Based Usage Analysis J Med Internet Res, 2019.PMID 31573916
  8. [8]Lagan S, et al. Mental Health App Evaluation: Updating the American Psychiatric Association's Framework Through a Stakeholder-Engaged Workshop Psychiatr Serv, 2021.PMID 33882716
  9. [9]Larsen ME, et al. Using science to sell apps: Evaluation of mental health app store quality claims NPJ Digit Med, 2019.PMID 31304366
  10. [10]Huckvale K, et al. Assessment of the Data Sharing and Privacy Practices of Smartphone Apps for Depression and Smoking Cessation JAMA Netw Open, 2019.PMID 31002321
  11. [11]Insel TR Digital Phenotyping: Technology for a New Science of Behavior JAMA, 2017.PMID 28973224
  12. [12]Adler DA, et al. Predicting Early Warning Signs of Psychotic Relapse From Passive Sensing Data: An Approach Using Encoder-Decoder Neural Networks JMIR Mhealth Uhealth, 2020.PMID 32865506
  13. [13]Chekroud AM, et al. The promise of machine learning in predicting treatment outcomes in psychiatry World Psychiatry, 2021.PMID 34002503
  14. [14]Belsher BE, et al. Prediction Models for Suicide Attempts and Deaths: A Systematic Review and Simulation JAMA Psychiatry, 2019.PMID 30865249
  15. [15]Large M, et al. Meta-Analysis of Longitudinal Cohort Studies of Suicide Risk Assessment among Psychiatric Patients: Heterogeneity in Results and Lack of Improvement over Time PLoS One, 2016.PMID 27285387
  16. [16]Obermeyer Z, et al. Dissecting racial bias in an algorithm used to manage the health of populations Science, 2019.PMID 31649194
  17. [17]Zhang Q, et al. Generative AI Mental Health Chatbots as Therapeutic Tools: Systematic Review and Meta-Analysis of Their Role in Reducing Mental Health Issues J Med Internet Res, 2025.PMID 41401240
  18. [18]Blease C, et al. ChatGPT and mental healthcare: balancing benefits with risks of harms BMJ Ment Health, 2023.PMID 37949485
  19. [19]Spanakis P, et al. The digital divide in people with severe mental illness: lessons learned and challenges lying ahead Br J Psychiatry, 2025.PMID 40114499
  20. [20]Parker L, et al. The "Hot Potato" of Mental Health App Regulation: A Critical Case Study of the Australian Policy Arena Int J Health Policy Manag, 2019.PMID 30980633
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