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UCLA Health

Verified evidenceCreator to judge

Randomized deployment of DAX and Nabla ambient AI scribes

Ambient tools can draft notes in practice, but vendor-specific efficiency and error results require comparison.

Healthcare screening · California, United States

Collections: Human still decides · Regulated autonomy · Negative results

An editorial scene for UCLA Health contrasts conducts the visit and writes the note in the ehr. with records the conversation and generates a draft note. in the randomized deployment of dax and nabla ambient ai scribes workflow.

Executive brief

The operating-model shift, in one view.

Treat ambient scribes as distinct products with measurable differences, not as one uniform category.

AI value · Time writing each note

Verified

Nabla arm fell from 4:30 to 3:49; a 9.5% greater reduction than control.

DAX did not significantly improve time versus control.

Before

Conducts the visit and writes the note in the EHR. → Completes and reviews documentation.

After

Records the conversation and generates a draft note. → Reviews, corrects, and signs the draft.

Human boundary

Physicians decide whether any generated content enters the record.

Why it matters

Ambient tools can draft notes in practice, but vendor-specific efficiency and error results require comparison.

How the work changed

Before

How the work ran before the change.

  1. Step 1 of 2

    Outpatient physician

    Conducts the visit and writes the note in the EHR.

    ControlPhysician signature.

  2. Step 2 of 2

    Physician

    Completes and reviews documentation.

    ControlClinical accountability.

What changed

Ambient tools can draft notes in practice, but vendor-specific efficiency and error results require comparison.

Decision rightHuman moves from creator to judge

After

How the same work runs now.

  1. Step 1 of 2

    Ambient AI scribe

    Records the conversation and generates a draft note.

    ControlTwo-month randomized clinical deployment.

  2. Step 2 of 2

    Physician

    Reviews, corrects, and signs the draft.

    ControlPhysician vigilance for clinically significant inaccuracies.

Process model built from the published workflow evidence for UCLA Health. Every step, actor, and control appears in full below.
Every step, actor, and control

Exception path

Inaccurate output is corrected or discarded and documented manually; adverse events follow clinical safety reporting.

Work removed

  • Some initial note composition

Decision authority

Physicians decide whether any generated content enters the record.

Before

  1. 01

    Outpatient physician

    Conducts the visit and writes the note in the EHR.

    Control: Physician signature.

  2. 02

    Physician

    Completes and reviews documentation.

    Control: Clinical accountability.

After

  1. 01

    Ambient AI scribe

    Records the conversation and generates a draft note.

    Control: Two-month randomized clinical deployment.

  2. 02

    Physician

    Reviews, corrects, and signs the draft.

    Control: Physician vigilance for clinically significant inaccuracies.

Work that left the path

  • Some initial note composition

Human role before

Physicians composed notes directly.

Human role after

Physicians edit and validate drafts while retaining complete clinical and signature responsibility.

AI roleDecision mode: draft documentation only; no diagnosis, order, or signature authority.

Outcomes

Time writing each note

Verified

Control arm fell from 4:22 to 4:04 per note.Nabla arm fell from 4:30 to 3:49; a 9.5% greater reduction than control.

November 4, 2024-January 3, 2025. · 238 physicians, 14 specialties, about 72,000 encounters.

DAX did not significantly improve time versus control.

Safety and accuracy

Verified

Usual physician-authored documentation.One mild adverse event; clinically significant inaccuracies were reported occasionally for both products.

Two-month randomized trial. · Two scribe products across 238 physicians.

Requires continuing physician vigilance; product performance was not uniform.

What leaders can reuse

Anti-pattern

Scaling a category-wide claim when one tested product did not beat control.

Questions

  1. 01Which product-specific metric matters?
  2. 02How are inaccuracies sampled?
  3. 03Do savings persist at scale?

Portability conditions

  • Physician review
  • Safety reporting
  • EHR telemetry
  • Product-specific evaluation

Reputation risk

medium

Evidence and authority

What the public record supports.

Current · updated

1 peer reviewed; publication outcomes are verified.

Bundle 1.0.0 · reviewed 2026-08-23 · stable ID 868ad97f92d5e208

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Sources

Read the evidence, freshness, caveat, and version policy.