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Capacity, not novelty, is the first AI outcome to measure

The opening question for a practice should be simple: where can technology return dependable time without weakening care?

The strongest current case studies focus on defined work—chart review, documentation or drafting—not vague transformation. That distinction matters because a broad promise cannot be measured or governed.

What practices should take from this

Start with a workflow that occurs often, has a clear beginning and end, and is currently owned by identifiable staff. Measure active minutes, waiting time, rework and after-hours completion before changing anything.

Then run a limited pilot with explicit human review. If the tool saves drafting time but doubles correction time, the capacity was not recovered. If it shifts work from a physician to an already overloaded medical assistant, the practice has moved the burden rather than removed it.

The most credible AI plan is a capacity plan: a baseline, a safe intervention, a measured result and a decision about whether to scale.