CAIHL read · Jun 11, 2026
Four Scenarios of AI Scribe Adoption in Healthcare
Framework
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.
The four dimensions
How this item reads through CAIHL
Primary user
clinician
Clinicians or care teams are the primary users. Patients are affected downstream.
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.
Editor's CAIHL read
One-sentence synthesis
Scenario-mapping framing of ambient-scribe deployment; patient-agency direction depends on which scenario the institution ends up in.
In the scan
How this item appeared in the daily scan
Editor's note: Scenario mapping is the right form for a layer where the deployment is racing the consent envelope. The patient question that runs through all four scenarios is the same: do I know what the recording is for, and does my answer change anything.
Summary: Medical Futurist: Bertalan Meskó publishes a scenario-mapping piece on AI scribe adoption — four trajectories for what the ambient-scribe layer looks like inside the clinical encounter at 2-, 5-, and 10-year horizons.
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.