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Learn how a clinical AI scribe differs from transcription and how to evaluate structure, source fidelity, review effort, governance, and workflow fit.
In plain language
A clinical AI scribe should support the path from an encounter to a structured, reviewable document. Unlike raw transcription, the output is organised for a clinical purpose. That extra transformation creates value, but it also makes source fidelity and human review essential.
What matters
Inspect both what was captured and how it was reorganised so a polished structure cannot hide a source error.
Count material corrections, section moves, missing facts, unsupported statements, and time to approval across representative encounters.
Evaluate templates, copy or export behaviour, identifiers, audit history, and the route into the authoritative clinical record.
Pilot method
Define the expected answer or evidence before the demonstration so the result can be assessed consistently.
Use representative examples, record what happens, and measure the work required to reach an acceptable final state.
Assign an owner to verify the current evidence, resolve gaps, and record any conditions before adoption.
Topic-specific review
These checks are specific to this decision and should be evidenced separately from the generic product demonstration.
Confirm whether a source transcript exists, whether it is retained, and how reviewers can trace a structured statement back to the encounter when a discrepancy appears.
Test how the system handles missing sections, uncertainty, contradictory statements, unmentioned negatives, multiple problems, and a request to change document format.
Check edit history, reviewer identity, timestamps, finalisation state, export record, and whether downstream users can distinguish a draft from an approved document.
Decision record
Keep the test cases, rubric, output corrections, evidence pack, unresolved risks, and approval conditions together. A later reviewer should be able to understand why the product was accepted, limited, or rejected.
Continue exploring
These pages add the operational, documentation, and trust context around this topic.
Next step
Use a representative workflow, a pre-agreed rubric, and current vendor evidence before deciding whether to adopt.