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Psych MEQs / SAQsPublic and community psychiatry

Psych MEQs / SAQs · Public and community psychiatry

Digital mental health, apps and AI in a community service (MEQ)

FRANZCP-style modified essay on digital psychiatry: app evaluation and prescribing, the guided versus unguided iCBT evidence, real-world app retention, the suicide prediction paradox, LLM chatbot harms and guardrails, privacy behaviour versus policy, and the digital divide. FRANZCP-primary, globally tagged.

20 marks20 min4 min readVerification in progress

Target exams

FRANZCPMRCPsychABPNMD-DNB
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Study tools

Target exams

FRANZCPMRCPsychABPNMD-DNB
Prompt
You are the consultant in a community mental health service. (a) A 29-year-old man with recurrent depression asks whether he should keep using a self-guided app he found in an app store, and tells you he now has long nightly conversations with a general-purpose AI chatbot instead of contacting the service. (b) Your executive proposes replacing six-weekly clinic reviews for moderate depression with an unguided digital programme, and has separately purchased an AI suicide risk dashboard that has flagged this patient amber. (c) A rural patient in the same programme has no reliable data credit. Address: (i) how you evaluate and prescribe an app; (ii) the evidence for internet-delivered and app-based interventions and what it does not support; (iii) the AI risk dashboard and the prediction paradox; (iv) chatbot use, privacy and equity.
What the marker is looking for

A named evaluation framework applied in order (privacy failure ends it), the guided-versus-unguided iCBT evidence quoted with the severity modifier, real-world retention distinguished from trial efficacy, the prediction paradox stated numerically, and chatbot use explored rather than forbidden. Equity and privacy must appear unprompted.[4][7][8][14]

(i) Evaluating and prescribing the app — 5 marks

Use the APA app evaluation framework and climb it in order. Background and access (developer, funding, cost, does it run on his device), then privacy and security, then clinical foundation, then engagement style, then whether any output reaches your clinical decision-making. Assessment stops at the first failed rung.[8]

Expect the clinical foundation rung to fail. Of 73 top-ranking consumer apps audited, 64% claimed effectiveness, scientific language supported 44% of claims, a third of ostensibly scientific techniques could not be validated, and exactly one app cited a published paper.[9]

Prescribe it like a prescription: indication, named product, frequency and duration, a review date, the outcome that would make you stop, and a documented consent conversation about data. Then ask at every review whether he is still using it — patients abandon apps silently.[8][7]

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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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