How regulated healthcare environments evaluate AI vendors

Healthcare AI vendor evaluation starts with the workflow. Before discussing model architecture, the hospital should be able to name the task, the users, the handoff, the data involved, the expected output, and the decision that changes if the tool works.

The first question is scope. Is the system helping staff find information, summarize context, draft text, prioritize work, detect missing data, recommend a next step, or trigger an action? Each of those scopes carries a different review burden. A vendor that uses one phrase for all of them is making the buyer do too much interpretation.

The second question is evidence. What evidence does the vendor have, and what evidence still has to be generated locally? Published validation, internal benchmarks, reference deployments, and demo performance are not interchangeable. A hospital should define local acceptance criteria before the vendor story fills the vacuum.

The third question is integration. Clinical AI fails quietly when it sits outside the real work system. Who sees the output? Where does it appear? Does it write back anywhere? Can it be reviewed before it changes a record or queue? Does it create a new inbox? Can the workflow continue if the tool is turned off?

The fourth question is governance. The hospital needs model and application ownership, release review, access control, audit logging, data-retention policy, incident response, and a process for reviewing complaints or unexpected behavior. These are not enterprise checkboxes; they determine whether clinicians and operators can trust the system.

The fifth question is clinical boundary. Some tools support operations around care. Some support documentation. Some may influence clinical judgment. The evaluation has to say where that boundary sits, who reviews output, and what training or labeling helps staff understand the system limits.

The sixth question is economics, but not only purchase price. The hospital should include integration work, security review, workflow redesign, training, governance overhead, support load, and the cost of stopping the tool if it does not meet the acceptance criteria.

A credible vendor can describe limits as clearly as capabilities. The most useful answer in a regulated evaluation may be a precise no: no autonomous action, no use of PHI for shared model training, no unsupported workflow, no silent updates in a clinical path.