CAIHL read · Jun 8, 2026

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High Patient Willingness to Grant Broad Consent for Real-World Data Use in Rheumatology — Implications for Real-World Data Platform Governance: Cross-Sectional Study

What CAIHL does

Critical AI Health Literacy (CAIHL) is an analytical lens — Hugo Campos and Liz Salmi's 2025 National Academy of Medicine commentary, "Critical AI Health Literacy as Liberation Technology." It applies Paulo Freire's theory of critical literacy to health AI.

The central question CAIHL asks is whose interests does this AI actually serve? Four dimensions answer it: who is the primary user, where is it hosted, whose interests does it advance, and does it expand or constrain patient agency.

This deep-read separates the four dimensions on a single item from the day's scan, so you can see the specific structural shape of the AI in question — not just the bucket it landed in.

How this item reads through CAIHL

Primary user

patient

Patients, families, and care partners are the primary users of this AI.

Hosting

institutional

Hosted inside a health system, insurer, or large employer. Access controlled by the institution.

Interests

mixed

Multiple stakeholder interests in tension; the alignment is not stable.

Agency

neutral

Neither clearly expanding nor constraining patient agency.

One-sentence synthesis

Consent-willingness measurement decoupled from comprehension; the governance layer the survey informs is not the layer the patient sees.

How this item appeared in the daily scan

Editor's note: Patient willingness to consent does not equal patient understanding of what they are consenting to. The governance gap the JMIR governance review names is the same one the consent-willingness paper opens.

Summary: JMIR: Cross-sectional survey in rheumatology finding high patient willingness to grant broad consent for real-world data use — with governance implications for the platforms that intermediate that consent.

Read the original source →

methodology

Limitations

CAIHL is a lens, not a verdict. The four dimensions are conditions of use — reassess them when a tool's business model, deployment context, or patient behavior changes. See the NAM commentary for the full framework.