Counterforce Health CAIHL draft report

Counterforce Health: CAIHL reassessment

September 8, 2026. Published AI-assisted draft.

Counterforce Health offers free assistance with a patient’s chosen coverage-denial challenge. The policy discloses secondary data use. Patient control over final wording, evidence checking, submission, and training use remains incompletely verified.

Scope and agency posture

Counterforce Health offers free assistance with a patient’s chosen coverage-denial challenge. The policy discloses secondary data use. Patient control over final wording, evidence checking, submission, and training use remains incompletely verified.

Posture: Potentially agency-expanding, with AI-training and verification caveats.

Axis: 84/100, retained provisionally. No numerical recalibration was performed.

Sources and documented findings

• The homepage offers free appeal creation and describes arguments from clinical and policy sources. Homepage, freshly retrieved September 8. Does not verify the quality of any letter.

• The same page markets staff productivity and revenue recovery. Homepage, institutional metrics. These metrics do not establish who controls an individual appeal.

• The privacy policy includes training and validation among data uses, with de-identification qualified by feasibility. It lists third-party AI processing. Privacy policy, sections 3–4, recovered September 8. Actual training datasets and processing implementation are unverified.

• Access, correction, deletion, and some opt-outs depend on location. Privacy policy, section 7. No universal, specific training opt-out is established. Data correction is not proof of appeal editing.

Patient authority

Inference from the documented workflow: the individual service is directed toward a patient's chosen denial challenge. The baseline describes drafts that patients submit, but this pass did not establish an enforced final-approval gate, broad goal redirection, or independent output-use rights. Keep correction as partial rather than upgrading it from the marketing.

Critical capacity

Editorial assessment: assistance can reduce the work required to oppose a coverage decision. It remains unclear how well patients can inspect the argument's evidence, compare alternatives, or challenge the system itself. An advocacy mission does not demonstrate those capacities.

Informed control

Editorial assessment: training disclosure makes a consequential practice visible, while the scope of patient choice over that practice remains uncertain. This supports the existing caveat. It is not evidence that every patient's information is used in training.

Assessment and limits

Counterforce Health offers free assistance with a patient’s chosen coverage-denial challenge. The policy discloses secondary data use. Patient control over final wording, evidence checking, submission, and training use remains incompletely verified.

Confidence: Medium for published documentation, low for implementation.

Remaining uncertainty: Final submission authority, evidence traceability, output-use terms, training opt-out, and the reach of 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.