CAIHL read · Jun 13, 2026
MIS Quarterly Executive: Insights from Leading AI Transformation at HCA 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
commercial
Prioritizes vendor or platform commercial interests (advertising, data, retention).
Agency
neutral
Neither clearly expanding nor constraining patient agency.
Editor's CAIHL read
One-sentence synthesis
Large-system AI transformation case study; agency direction depends on which operational protocols downstream institutions adopt.
In the scan
How this item appeared in the daily scan
Editor's note: HCA is the largest for-profit US hospital system; whichever AI deployment pattern they normalize is the pattern many smaller systems will adopt. The published case study is the version of the pattern HCA wants to be cited.
Summary: HCA Healthcare Today: First-person case study from HCA's AI transformation leadership published in MIS Quarterly Executive — the largest US for-profit hospital system documenting its operational AI deployment pattern.
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.