CAIHL read · Jun 11, 2026
STAT+: Your sepsis algorithm shouldn't require a time machine
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
patient-aligned
Interest structure prioritizes patients. Operates on a philanthropic, public-service, or advocacy footing.
Agency
constraining
Channels patients toward predetermined pathways or substitutes for patient capabilities.
Editor's CAIHL read
One-sentence synthesis
Critique of AI model evaluation paradigm in high-stakes deployment; constraining agency where the model is asked to do what the evaluation didn't measure.
In the scan
How this item appeared in the daily scan
Editor's note: Sepsis algorithms are the highest-stakes deployment surface where the model-evaluation paradigm and the clinical-deployment paradigm meet. The 'time machine' framing names the gap explicitly: the model is tested on a future the clinician cannot see.
Summary: STAT: First Opinion piece arguing that current AI sepsis-prediction algorithms are evaluated against retrospective ground-truth in ways that don't translate to the prospective clinical environment — the temporal validity gap the field has not yet closed.
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.