Specialism
Clinical safety and AI assurance for digital mental health and neurodevelopmental products
Mental health and neurodevelopmental services carry risks that generic hazard templates handle badly: disclosure, waiting lists, shared care, multi-informant assessment and long gaps between contacts. This is where we spend a large share of our clinical safety work.
Products we work on
Where this applies
ADHD assessment and management platforms
Structured assessment, titration support, monitoring and shared-care communication with primary care.
Autism and neurodevelopmental pathways
Screening, triage into assessment, multi-informant data collection and long waits between contacts.
Mental health assessment software
Structured instruments, scoring, risk questions and the interpretation placed on a number.
AI clinical documentation and ambient scribes
Consultations where nuance, attribution and disclosure carry unusual clinical weight.
Clinical decision support
Prompts on medication, dose, monitoring intervals and escalation.
Remote monitoring and digital therapeutics
Between-appointment data, self-report, and what happens when a signal deteriorates.
Patient questionnaires and digital assessments
Self-completed instruments used at scale, sometimes without a clinician in the loop.
Medication monitoring
Controlled drugs, physical health monitoring, shared care agreements and titration safety.
AI triage and prioritisation
Ranking who is seen first, where an error is invisible to the person affected.
Digital mental health is a secondary specialism. We work across digital health and healthcare AI generally — see our services for the full picture.
Hazard patterns
What we look for first
Risk questions answered into a void
A self-report instrument captures a disclosure of self-harm or suicidal ideation out of hours, and nothing in the product or the service defines who reads it, when, and what they do next.
Score treated as diagnosis
A screening or rating scale is designed to inform clinical judgement. Presented as a headline number, it starts to substitute for it — inside the service, and in the patient's understanding.
Attribution in multi-informant assessment
Neurodevelopmental assessment draws on the patient, family, school or employer. Recording an observation against the wrong source changes the clinical meaning.
Safeguarding content in generated notes
An ambient or summarising tool omits, softens or misattributes a safeguarding disclosure, and the omission is invisible on review.
Shared care and physical health monitoring
Medication pathways depend on timely physical health checks. If a monitoring prompt fails silently, nobody is looking for the absence.
Prioritisation error on long waiting lists
A triage or ranking feature moves someone down a list. The harm accrues quietly over months and never presents as an incident.
Digital exclusion and accessibility
The people most likely to be excluded by a design decision are often the ones the service most needs to reach.
Diagnostic overshadowing in the data
Physical symptoms recorded in a mental health context are discounted. Products that summarise or filter can reinforce the pattern.
Why familiarity helps
Hazard identification is a questioning exercise. The quality of a hazard log depends almost entirely on whether the right questions were asked about the workflow, the user and the patient.
Knowing how a titration clinic runs, what a shared care agreement expects of a GP, how a long neurodevelopmental waiting list behaves, and where a disclosure typically surfaces in a consultation means we arrive with better questions — and challenge the answers that sound tidy but would not survive a busy Tuesday.
That familiarity supplements, and does not replace, the clinicians who use your product. The strongest hazard workshops we run have your clinical users in the room.
What we do not claim
Assurance AI is led by a UK registered pharmacist and independent prescriber with digital clinical safety training. We do not hold specialist psychiatric credentials, and we do not present ourselves as a mental health clinical service. Where specialist clinical input is needed, we name it as a gap rather than covering it ourselves.
About the practice →How we work
From intended use to defensible evidence
The route is the same as any other clinical safety engagement, applied with the clinical context in mind. We start with intended use and the clinical pathway, run hazard identification with your clinical users, build a hazard log that names product-specific hazards rather than generic ones, and produce a clinical safety case that argues residual risk is acceptable — with controls your service can actually operate.
Where the product uses AI, the analysis extends to model behaviour, oversight design, change management and monitoring. That work is described in more detail on our AI clinical safety page, and the underlying process is set out in the DCB0129 guide.
If your NHS customer is running a baseline assurance process, the same evidence feeds your DTAC submission, and your named Clinical Safety Officer arrangements will be scrutinised alongside it.
FAQ
Frequently asked questions
Is this a separate service, or your normal clinical safety work?
Does Assurance AI provide psychiatric or specialist mental health clinical advice?
Why does clinical familiarity with this area matter for safety work?
Our product is not a medical device. Do we still need DCB0129?
We use an AI model to summarise or triage. What extra evidence will buyers want?
Keep going
Related reading
AI clinical safety
Failure modes, oversight design, monitoring and change management for AI-enabled products.
DCB0129 for manufacturers
The clinical risk management standard behind the evidence NHS buyers ask for.
Clinical Safety Officer support
Named CSO arrangements, including fractional cover for smaller suppliers.
NHS readiness check
Ten minutes to see where your evidence currently stands.