Full review

Fight Health Insurance CAIHL draft report

Evidence-linked HugoScore draft report for a health AI tool that affects patients.

Fight Health Insurance: CAIHL reassessment

September 8, 2026. Published AI-assisted draft.

Fight Health Insurance encourages patients to revise appeal drafts and check citations. Its AI page and terms give conflicting training descriptions. Useful AI disclosures support scrutiny, but the current privacy policy could not be retrieved.

Scope and agency posture

Posture: Potentially agency-expanding, with unresolved data-use terms.

Axis: 90/100, retained provisionally. No numerical recalibration was performed.

Sources and documented findings

The AI page names model and literature sources and tells users to customize drafts and check citations. About Our AI, recovered September 8. Generated citations and actual editability were not tested.

The AI page says training uses synthetic examples from public decisions and excludes real patient appeal letters. About Our AI, training section. The terms separately describe training on submitted information. Actual practice cannot be resolved from these statements.

The terms describe training use and assign identifier removal to users. Terms, opening paragraphs. Not proof that every submission is trained on.

Terms require review before using or sharing generated content, alongside broad service and IP restrictions. Terms, content and license sections. This review makes no legal finding that ordinary appeal submission is prohibited.

The privacy-policy URL returned an internal retrieval error in both the prior run and this run's single targeted retry. Privacy-policy URL. This is an access uncertainty, not proof that the vendor removed its policy.

Patient authority

Inference from the documented workflow: the consumer workflow assigns the patient responsibility for deciding what to send. A specialty appeal goal is compatible with patient direction. Whether the system supports broader goal changes is untested.

Critical capacity

Editorial assessment: the concrete instructions to inspect evidence are more informative than a generic assurance of accuracy. They support a qualified positive finding. They do not prove lasting gains in understanding or an independently validated advantage over competitors.

Informed control

Editorial assessment: conflicting training descriptions prevent a clear account of the conditions under which a patient can rely on the service. Open source enables outside scrutiny, but is not evidence of production parity or effective patient contestability. The baseline's on-device OCR description must not be read as proof that appeal generation stays on-device.

Assessment and limits

Confidence: Medium for published documentation, low for implementation.

Remaining uncertainty: Actual training practice, current privacy terms, output reuse, production/code correspondence, and deletion.

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.