🤖 AI Mental Health Screening Conversational Simulator
AI conversational mental health screening simulator using validated questionnaires to assess symptoms of anxiety and depression.
Conversation Begins
A screening bot starts like a person would, not a form.
- Open-ended: First message type (no checkbox in sight)
- Hidden: Clinical framing (questionnaire stays invisible)
- Warm: Tone target (reduces screening dropout)
- PHQ-9: Underlying instrument (9-item depression scale)
Why not just show the form
Rigid forms feel clinical and get abandoned early.
Greeting design
One open question invites a natural, unscripted reply.
Setting expectations
The bot signals safety before asking anything sensitive.
Questionnaire Items Woven In
Each PHQ-9 item is rephrased as a casual follow-up question.
- 9: PHQ-9 items (depression screening core)
- 7: GAD-7 items (anxiety screening variant)
- Conversational: Rephrasing style (not verbatim clinical wording)
- Adaptive: Item order (follows conversation flow)
Rewriting clinical language
"Little interest in things" becomes a plain, human question.
Pacing the items
One item per turn keeps the exchange feeling unhurried.
Staying on the standardized scale
Wording changes, but the measured construct never does.
Natural Language Responses
Patients type real sentences, not multiple-choice answers.
- Free text: Response format (no fixed answer options)
- 5–15 words: Typical reply length (short, conversational)
- Yes: Ambiguity handled (via gentle clarifying follow-up)
- Preserved: Language nuance kept (tone, hedging, intensity)
Why free text matters
People disclose more naturally than in dropdown menus.
Clarifying vague answers
The AI asks a soft follow-up when intensity is unclear.
Capturing nuance
Hedges like "kind of" still carry scoring signal.
Response Mapping
Free text is translated into the standardized 0–3 PHQ-9 scale.
- 0–3: Score range per item (not-at-all to nearly-every-day)
- NLP classifier: Mapping method (frequency + sentiment cues)
- Re-ask: Low confidence handling (clarifying question triggered)
- Logged: Mapping transparency (clinician-reviewable trail)
From words to numbers
"Most days" maps toward the higher frequency scores.
Confidence scoring
Ambiguous phrasing lowers mapping confidence automatically.
Human oversight
Every mapped answer stays traceable back to its source text.
Score Computed & Shared
All nine mapped scores sum into one standardized severity result.
- 0–27: Score total range (full PHQ-9 scale)
- 4: Severity bands (minimal / mild / moderate / severe)
- Conversational: Result delivery (no raw number dumped)
- Care routing: Next step (handed to clinical workflow)
Summing the scale
Nine item scores add into one total severity number.
Sharing results gently
The category is explained in plain, supportive language.
Where scoring hands off
Severity output feeds a separate triage or care step.
AI conversational mental health screening simulator using validated questionnaires to assess symptoms of anxiety and depression.
2D · HTML5 Canvas 2D · 60 FPS target · runs fully client-side, no install