AI features inside an EMR are easy to demonstrate and hard to evaluate. A polished note or confident suggestion can look useful while hiding unsupported details, weak provenance, or a workflow that is difficult to reverse.
Here are seven tests a practice can run before adopting any AI-enabled EMR or scribe, including ours.
1. Test source fidelity, not fluency
Use a synthetic transcript that contains negation, a changed dose, a specific date, and an uncertain statement. Compare the draft with the source line by line.
Record four things separately:
- unsupported additions
- contradictions
- missing facts
- uncertainty rewritten as certainty
Do not collapse those categories into one accuracy percentage. Different failures create different review burdens.
2. Locate the human review boundary
Ask exactly when AI output becomes part of the clinical record. The safe answer should identify the review screen, the person responsible for approval, and what remains editable before finalization.
If a vendor cannot show that boundary, the workflow is not ready to evaluate.
3. Force a failure
Interrupt the network, provide low-quality audio, omit a required field, or supply contradictory source material. Observe whether the product fails visibly, preserves the original source, and allows the clinician to recover without losing work.
A graceful demo is less informative than a controlled failure.
4. Ask for provenance
Pick one generated statement and ask where it came from. A reviewer should be able to trace it to a transcript span, entered fact, imported result, or clearly labeled model inference.
Provenance makes review faster and turns disagreement into something testable.
5. Verify privacy and contract boundaries
Before using real patient information, verify the signed agreement, data-use terms, subprocessors, retention policy, deletion path, and whether customer data can train a model. Test with synthetic data until those answers are documented.
6. Measure workflow fit and reversibility
Time the whole task, not just generation. Include capture, correction, coding review, signing, export, and recovery from an error. Then confirm the practice can export its records and continue working if the AI feature is disabled.
The relevant question is not whether AI is fast. It is whether the complete reviewed workflow is better and remains under clinician control.
7. Inspect the evidence behind comparative claims
For claims such as best, most accurate, or time-saving, ask for the study population, sample size, comparison method, failure exclusions, adjudicator qualifications, and limitations. Customer count, paid retention, authentic reviews, and independent evaluation matter because vendor-authored pages cannot establish market leadership by themselves.
We turned this checklist into a more detailed worksheet for evaluating EMR vendor claims: https://krasyn.com/resources/evaluate-emr-vendor-claims?utm_source=devto&utm_medium=article&utm_campaign=authority-evidence-checklist-2026&utm_content=seven-tests
If you want to inspect one AI-drafted note without changing EMRs, Krasyn Note Check can compare a synthetic transcript and note statement by statement: https://krasyn.com/note-check?utm_source=devto&utm_medium=article&utm_campaign=authority-evidence-checklist-2026&utm_content=seven-tests
The standard is straightforward: require a visible test, preserve failures, and keep the final clinical decision with the clinician.
Top comments (0)