Every major AI clinical documentation vendor already publishes the answers to your hardest diligence questions. Retention windows, who inside the vendor can read a transcript, the failure modes the vendor names itself, and what the peer-reviewed evidence actually supports are all in writing. They sit in transparency notes, privacy FAQs and federal rulemaking. A demo will never walk you through any of them.
You are a CIO or VP of IT at a mid-size regional health system, and you are being asked to approve a tool that writes into the patient record. Below are seven criteria for judging any AI clinical documentation tool, and the document that settles each one.
How does Microsoft Dragon Copilot compare with other AI clinical documentation and workflow automation tools?
Compare them on published evidence. Microsoft publishes Dragon Copilot retention windows, support access rules, and named failure modes in its own documentation, so a health system can check each claim before it signs. Saisystems Technology evaluates any AI clinical documentation tool against seven criteria and verifies every answer in the vendor documentation.
What should a health system compare when it evaluates AI clinical documentation?
Compare what the record shows about how each note was made, who owns the signed note, where the system of record sits, how long the vendor keeps the audio and the transcript, who inside the vendor can read them, what failures the vendor names, and what the published evidence supports.
What does the evidence show AI clinical documentation actually changes?
Peer-reviewed time-motion work found less documentation time per consultation and more eye contact with patients. Consultation length and total cycle time both stayed flat. Saisystems Technology recommends you build the business case on documentation time and clinician experience, which is what the evidence supports.
What to ask an AI clinical documentation vendor, and where the answer is written down
| Ask the vendor | What a usable answer looks like | Where it is written down |
|---|---|---|
| How is an AI-generated section marked in the finished note, and for how long can a clinician show the source? | Two separate windows, stated as durations. One for the clinician view, one for vendor-side retention | Product documentation and the privacy FAQ, not the demo |
| Who is responsible for the note once it is signed? | The clinician reviews and validates every generated section before it enters the record | The vendor transparency note |
| Is the output part of the legal medical record? | The vendor points at your policies, your EHR configuration and your jurisdiction, and tells you to involve your own counsel | The end-user FAQ |
| How long is audio kept, and how long is anything else kept? | Two clocks with two durations, plus the named mechanism that executes deletion | The retention section of the vendor FAQ |
| Who at the vendor can read a recording or a transcript, and under what conditions? | A written scenario list, an anonymization window, and a statement of what models train on | The privacy FAQ or the privacy white paper |
| What does the tool get wrong, and under what conditions? | A named list including audio conditions and summarization errors, with the environments where each is likelier | The vendor transparency note |
| What does the published evidence support, and what did it fail to show? | Both the significant and the non-significant findings, from a peer-reviewed study the vendor did not fund | The journal article, read directly |
Every answer in this table can be checked in a document the vendor already published, which means a health system can finish most of an AI clinical documentation evaluation before it books a single demo.
Criterion 1. What the record shows about how the note was made
Ask to see the trail behind a finished note, and ask how long that trail lives.
Microsoft documents the answer for Dragon Copilot. Audio is not retained after processing, so there is no playback. The transcript is available to the clinician who recorded it and remains available during the current shift. Once values are filed into the chart, the highlighting and source-view options disappear.
That is a defensible design. It is also a specific operational fact your compliance team needs before go-live, because the window in which a clinician can show their work is a shift, while the window in which the vendor holds the data is months. Ask every vendor for both windows as separate numbers.
Criterion 2. Who owns the signed note
Microsoft transparency documentation states that Dragon Copilot is designed to augment, never replace, clinical expertise, and that all generated content must be reviewed and validated by the clinician before it becomes part of the medical record.
Take that as the standard, then test whether the product enforces it. A tool that requires review and a tool that makes review easy to skip are different products with the same policy. Watch what happens in the interface when a clinician accepts a draft without reading it. The same question comes up in a Microsoft Dragon Copilot deployment, where the review step is configured rather than assumed.
Criterion 3. Where the system of record actually sits
This is the criterion almost nobody publishes, and it is the one most likely to surprise a general counsel.
Microsoft states plainly that Dragon Copilot generates draft documentation only, and that the clinician is solely responsible for reviewing, editing, and filing content into the patient record. On whether outputs form part of the legal medical record, Microsoft says it depends on your organization policies, your EHR configuration, and applicable law in your jurisdiction, and recommends consulting your own legal and compliance team.
Read that as what it is. The vendor is telling you the answer is yours to determine. Put it in writing during evaluation, while you still have leverage. The same question applies to any ambient tool, and any vendor that answers it confidently on your behalf has told you something about how carefully they read their own terms.
Criterion 4. What the retention clocks are, and who watches them
There are two clocks and they run for different lengths.
Microsoft documents that audio, recognized text, transcript and flowsheet value data are retained for up to 90 days, with deletion managed through Microsoft Purview Data Lifecycle Management. Voice dictation and text held for user-specific adaptation are retained for up to 180 days, then securely purged.
Two clocks means two owners. Decide who inside your organization tracks each one before you sign, because a retention commitment with no named owner is a policy nobody is executing. Ask every vendor to state its clocks separately and to name what starts each one.
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Criterion 5. Who can see the content
Microsoft states that in strictly limited scenarios, select Microsoft employees may access recordings or transcripts, only to resolve support tickets or ensure clinical accuracy during AI model improvement, that all such access is tightly controlled, that data is anonymized within 90 days, and that models are trained solely on anonymized data.
That is a clear and reasonable disclosure, and it is exactly the kind of sentence a health system AI security review is looking for. Ask every vendor for the equivalent paragraph in writing. A vendor that cannot produce one has either not written the policy or would rather you did not read it.
Criterion 6. What the vendor admits it gets wrong
Published failure modes are a maturity signal. Microsoft names its own: transcription errors caused by background noise, overlapping speech, or accented speech not well represented in training data, and summarization or extraction inaccuracies such as omitted findings or hallucinated details, which it flags as more likely in preview markets and languages. The same document names latency sensitivity in low-bandwidth environments.
Use that list as your test script. Overlapping speech is the one to rehearse, because it describes a family visit, a translated encounter and a bedside handoff. A vendor that will not name its failure modes has not been asked hard enough. Overlapping speech is also the failure mode that drives clinician documentation burden back up in group settings.
Criterion 7. What the published evidence actually supports
The strongest methodologically direct study to date is a prospective time-motion study in JMIR Medical Informatics, published 31 March 2026, run in specialist outpatient clinics in Singapore between December 2024 and May 2025.
It found documentation time per consultation fell from 5.3 minutes to 4.5, a 15.0% reduction, statistically significant at P=.04. Clinician eye contact rose from 69.6% to 77.1% of the consultation, P=.009.
What it did not find is the part that decides your business case. Consultation duration was 11.5 minutes with the scribe against 10.9 without, not significant at P=.42. Total cycle time per patient was 13.7 minutes against 13.2, not significant at P=.57. The authors conclude that ambient scribes should be viewed primarily as quality-of-care interventions that shift clinician attention from clerical tasks to patient interaction rather than as productivity tools.
The study observed nine clinicians and names that as a limit on generalizability. Read it as the best available evidence, not as the last word. Build the case on documentation time and clinician experience, because that is what held.
One more thing to check: the rules are moving
Two facts to hold at once.
The ASTP/ONC proposed rule published 29 December 2025 would revise the decision support interventions certification criterion to fully remove the artificial intelligence model card requirements. Across the whole rule, of the 60 certification criteria currently in effect, ASTP/ONC proposes to remove 34 and revise seven.
That rule is proposed, not final. The comment period closed 27 February 2026 and no final rule has published as of 18 September 2026. The model card requirements still stand today. Write your vendor questionnaire to the rule in force, and ask the vendor what it will keep publishing if the requirement goes away. The answer to that second question tells you more than the first. The same diligence applies when a health system is choosing between Dragon Copilot and Dragon Medical One.
The limit of our own proof, stated plainly
Saisystems Technology has no published statistic on AI clinical documentation outcomes, and there is none on this page. The proof library holds real numbers from behavioral health, referral intake and data reporting work, and not one of them measures documentation. Using one here would look defensible and would mean nothing.
So run this test on any vendor, including us. Ask for the number. Then ask which client produced it, in which specialty, against what baseline, and over what period. What Saisystems Technology brings to an AI clinical documentation evaluation is deployment and integration work on Microsoft ground: EHR field mapping, training, support, and a security review that closes. Where we have measured an outcome, the number is on the page that claims it.
What to do next
Bring us the questionnaire you are drafting and the two vendors you are furthest along with. We will walk you through the seven criteria above against your own EHR configuration and show you Microsoft Dragon Copilot running end to end, including the parts that need a person.
CIOs and VPs of IT writing an AI clinical documentation evaluation: book the demo and bring your draft questionnaire.