An AI medical scribe in emergency medicine can prepare draft documentation from a clinical conversation, but its usefulness depends on how well it fits a fragmented, time-sensitive emergency department workflow. The note may need to incorporate reassessment, new results, consultations, procedures, and a changing disposition plan. Clinician review remains essential.
The evidence is mixed. Some emergency department studies associate ambient AI with less documentation time than standard workflows. A comparison with human scribes found more time spent in the EHR notes section with AI. Those findings support a careful local evaluation, not a universal promise of shorter waits, higher throughput, or better patient outcomes.
This guide is for emergency clinicians and departmental leaders evaluating documentation tools. It discusses workflow and published research, not treatment decisions. Local clinical, privacy, and hospital requirements take precedence.
In this article
- What an emergency department scribe needs to capture
- What the emergency medicine evidence shows
- A practical workflow for emergency documentation
- Where capture can fail
- How to evaluate an emergency department pilot
What an emergency department scribe needs to capture
Emergency care rarely follows one uninterrupted conversation. A clinician may assess a patient, leave to review results, discuss the case with another service, and return later. Different clinicians may contribute to care during the same visit.
An AI medical scribe can only work with the information available through its supported capture and context inputs. It should not be assumed to know an unspoken examination finding, a newly returned result, or a discussion elsewhere in the department.
The documentation workflow should distinguish:
- The initial history and assessment from later reassessment.
- Patient statements from family, interpreter, or staff contributions.
- Tests being considered from tests ordered and results reviewed.
- The provisional plan from the final disposition decision.
- A request for another service’s advice from advice actually received.
The final note needs to reflect the encounter as it developed. A neatly organized first draft may still be incomplete when the patient leaves the department.
What the emergency medicine evidence shows
The comparator matters. Standard physician documentation, human-scribe support, and different ambient AI products are not equivalent starting points. Research should also distinguish active documentation time from overall visit duration.
An observational comparison with standard encounters
A 2026 study by Preiksaitis and colleagues examined 8,740 eligible adult emergency department encounters at one academic centre; 976 used an ambient AI scribe. Median on-shift documentation time was 2 minutes 45 seconds for ambient encounters versus 3 minutes 50 seconds for standard encounters.
Use was optional and concentrated among a smaller group of physicians, lower-acuity patients, and encounters not requiring interpreters. The retrospective findings show an association in that implementation. They do not establish the same benefit for high-acuity care, interpreted visits, every clinician, or Dorascribe.
A comparison with human scribes
A separate quality improvement pilot by Morey and colleagues, published in 2026, included five early-adopter physicians and 710 visits. Compared with human scribes, AI-assisted encounters involved more time in the EHR notes section and more physician contribution to the note. Adult note-quality scores were similar; pediatric scores were lower with AI.
This small pilot does not settle which approach is best for every department. It does show why an AI tool should be compared with the support a department actually uses, rather than assumed to outperform human scribes.
What these studies do not establish
Neither comparison justifies a blanket claim that an AI scribe reduces emergency department waiting times or prevents clinical errors. Documentation time is one component of a larger system that includes staffing, diagnostics, consultation availability, and bed capacity.
The practical question is whether the proposed tool produces a usable, accurate note with acceptable review effort in your setting. Keep vendor-specific claims separate from research on the wider product category.
A practical workflow for emergency documentation
The following sequence is an evaluation framework, not a universal hospital protocol. Adapt it with the departmental and information-governance teams before use.
Confirm the patient and the recording conditions
Verify the patient, encounter, and intended note type before capture starts. Follow the approved explanation and consent process, and check whether the location allows appropriate privacy. In a shared treatment area, a microphone may capture people unrelated to the encounter.
Ontario’s CPSO guidance on AI in clinical practice addresses patient information, consent before recording, and clinician accountability. Other jurisdictions and hospitals may set different or additional requirements.
Capture the initial assessment without inventing context
Use the supported recording method, but keep the interaction clinically natural. If a relevant observation was not spoken, add it through an approved clinician input rather than assuming the system inferred it.
The draft should distinguish what the patient reports from what the clinician observes. Do not accept a normal examination statement simply because it appears in a standard template.
Add reassessments and results through a defined process
Decide how later information enters the record. A tool may support an additional recording, a typed update, or a separate note; verify the actual functionality and local documentation rules.
Preserve the sequence when it matters. A symptom that resolved after an intervention should not disappear from the initial history. A result reviewed later should not be presented as known at the first assessment.
Review the final plan and disposition
Before signing, check that the draft reflects the actual decision, relevant reassessment, outstanding information, and the instructions that were given. Confirm who owns any next action under the department’s established process.
Review does not mean adding lengthy generic text. Remove boilerplate that obscures the patient-specific plan. Dorascribe’s guide to preventing note bloat explains how oversized templates can increase correction work.
Verify transfer and completion in the EHR
Check the destination chart and encounter, note type, author, and signing status. Copy-and-paste, document export, a browser extension, and a native integration have different failure modes.
Ask for a demonstration in the intended environment. The AI scribe integration guide provides questions about patient matching, supported fields, and approval. Do not assume that a supported integration automatically places orders or manages pending results.
Where capture can fail
Emergency departments create conditions that should be tested deliberately, rather than treated as rare exceptions.
| Situation | Documentation risk | What to test |
|---|---|---|
| Interruptions and repeat assessments | Separate events become one account | Time order and attribution of updates |
| Family members and interpreters | Statements are assigned to the wrong speaker | Who said what and about whom |
| Shared rooms and background speech | Unrelated information enters the note | Capture boundaries and stopping controls |
| Changing plans | A preliminary decision appears final | Whether revisions replace or explain earlier plans |
| Shift handover | Responsibilities become unclear | What remains pending and who receives it |
| Capture or network failure | The clinician assumes information was saved | Failure alerts and the manual fallback |
If capture is inappropriate or unreliable, use the approved alternative documentation process. Do not delay urgent care to troubleshoot a recording tool.
How to evaluate an emergency department pilot
Start with an approved scope, named clinical ownership, and clear stopping rules. Use fictional scenarios for initial testing before an authorized clinical pilot. Include the situations the tool will actually face; a quiet, single-speaker demonstration is not enough.
Measure the complete documentation task
Track time from information capture through correction and filing. Record active review time, substantial factual corrections, omitted information, unsuccessful capture, and transfer failures. Separate documentation during the shift from work completed afterward.
If the current workflow includes human scribes, include that comparison. If it does not, compare with usual documentation. Record the software configuration, note types, clinicians, visit mix, and actual use rate so results can be interpreted.
Check clinically meaningful errors
Review attribution, negation, medications, timing, and the distinction between proposed and completed actions. Include cases with evolving information and multiple speakers. A transcription percentage alone does not measure whether the final clinical record is faithful.
The CMPA AI scribe guidance discusses validation, ongoing monitoring, and review of entries. Use an appropriate departmental review process rather than treating one successful demonstration as validation.
Keep operational outcomes separate
If evaluating throughput or patient experience, measure them directly and consider other changes occurring during the pilot. Faster note completion alone does not demonstrate a shorter emergency visit. Report limitations and unsuccessful cases alongside useful outcomes.
For an outpatient walk-in or urgent-care setting, use the separate walk-in clinic and urgent-care workflow guide. Its setting should not be treated as interchangeable with a hospital emergency department.
Evaluating Dorascribe for an emergency workflow
Dorascribe provides consultation capture, structured draft notes, and editing functions. Whether those functions fit a specific emergency department requires an approved evaluation of capture, updates, review, and transfer. This article does not claim that Dorascribe was the product tested in the cited studies or that every hospital integration is available.
You can book a demonstration using a fictional emergency encounter with an initial assessment and a later update. Ask to see what the system misses, how the clinician corrects it, and how the final note reaches the intended record.
Common questions
Do AI scribes always save emergency physicians time?
No. Published results vary with the comparator and implementation. Evaluate review and filing time in the department’s real workflow, including after-shift documentation.
Can an ambient scribe replace a human emergency department scribe?
That decision requires more than comparing transcription. Human scribes may perform tasks outside the AI tool’s capture and drafting functions. Define the current role and test the proposed replacement against it.
Does the scribe know what happened after the recording stopped?
Only information supplied through supported and approved inputs can inform the draft. Verify how results, reassessments, and later decisions are added.
Can an AI generated note be signed without review?
Clinicians should not treat an unreviewed draft as a completed record. Review the content and confirm the correct patient and encounter before approval.
