Wysa CAIHL draft report

Wysa: CAIHL reassessment

September 8, 2026. Published AI-assisted draft.

Wysa’s consumer self-help, institutional referral, and clinician-linked channels have different authority and data-sharing arrangements. This profile distinguishes those channels. The former combined score of 55 is historical, rather than a current score for every deployment.

Scope and agency posture

Consumer Conversation Space, institutional Digital Front Door and referral collection, plus clinician-linked services. Their identity and reporting arrangements differ.

Posture: Mixed, channel-dependent.

Axis: Unscored in current comparisons. Historical axis: 55/100. Consumer, institutional and clinician-linked channels require distinct assessments. This is a scope change, not a numerical downgrade.

Sources and documented findings

• The current policy documents conversation redirection when users say AI is not helping, reset controls, institutional usage/safety sharing and configurable clinician access. It says referral submission collects information for the institution, which decides acceptance and care level. https://legal.wysa.io/privacy-policy

• The same policy distinguishes provider zero retention from Wysa retention, permits some anonymous chats to improve its AI, and routes submitted institutional-data corrections to the institution. These are vendor policy statements. https://legal.wysa.io/privacy-policy

Patient authority

Inference from the documented workflow: Self-help users can redirect conversation. Institutional pathways constrain the offered resources, but the policy does not establish that the AI itself denies care or that declining it loses access.

Critical capacity

Editorial assessment: Exercises can support reflection and action. Clinical improvement is not a substitute for user control over routing, and formal data rights do not establish a direct appeal against a clinical decision.

Informed control

Editorial assessment: Nickname-based consumer use must not imply anonymity across referral, WhatsApp or clinical deployments. External LLM no-training statements do not cover all internal improvement uses.

Assessment and limits

One Mixed score obscures materially different control arrangements. Avoid unsupported assertions that the institution's purchase means the patient did not choose use.

Confidence: Moderate for documented service distinctions.

Remaining uncertainty: Actual institutional signup notices, alternatives and review routes require deployment evidence

Published documentation is evidence of stated conditions, not proof of actual implementation. Unknowns did not receive automatic negative points. Funding, sponsorship, and public code do not determine agency by themselves.

Review provenance

• Reviewer/model: OpenAI Codex / GPT-6.
• Method: Focused public-source reassessment using CAIHL: patient authority, critical capacity, and informed control. Existing evidence plus one focused primary-source pass and at most one targeted follow-up. No live product testing. Numerical scores remain provisional editorial placements, not a new calculation.
• Human review: Hugo Campos authorized publication of these AI-assisted draft reassessments on September 8, 2026. This does not claim comprehensive human verification of every finding.
• Earlier review: 2026-06-10. Historical assessment. Earlier claims are not automatically reverified by this publication.