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Physician WorkflowAIScribe RCTNCT06792890

A Randomized Controlled Trial of Ambient Artificial Intelligence Scribe Technologies

A study testing Use Nabla AI Scribe tool provided and Use AI Scribe tool provided by Vendor B in people with Physician Workflow and Artificial Intelligence (AI), run by University of California, Los Angeles at 1 site in United States.

Completed

Finished. The study has ended and is not enrolling. Results may be posted on the official record.

Official record
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Trial summary

This is a three-arm pragmatic RCT of 238 outpatient physicians at a large academic health system, randomized 1:1:1 to one of two AI scribe tools or a usual-care control group. The two-month study will observe and compare the effects of each tool prior to system-wide roll out of selected tool (anticipated Spring 2025). Read more about this study

Who is it for?
Any age
What is being tested?
Use Nabla AI Scribe tool provided and Use AI Scribe tool provided by Vendor B
Could I receive a placebo?
No, everyone receives an active treatment
How many people
238 participants
Where
1 siteUnited States
Study dates
4 Nov 2024to 15 Jan 2025
Phase
Interventional
Are there results?
Yes, posted in the registrySummary in the doctors' view

Key requirements

Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.

  • Ages Any age
  • All sexes
  • No healthy volunteers
  • Child, Adult, Older adult

Who may be able to join

  • Ambulatory care physicians within the UCLA Health system who held at least one half-day of clinic per week

Who cannot join

  • Trainee providers (e.g., residents, medical students) and allied healthcare professionals (e.g., RNs, PAs)
  • Attendings who work exclusively with trainees

Trial locations and contacts

1 site in 1 country.

United States1 site

  • UCLA Health System

    Los Angeles, California, 90024

Other trials in United States

About this study

Official title: A Randomized Controlled Trial of Two Ambient Artificial Intelligence Scribe Technologies to Improve Documentation Efficiency and Reduce Physician Burnout

This is a three-arm pragmatic RCT of 238 outpatient physicians at a large academic health system, randomized 1:1:1 to one of two AI scribe tools or a usual-care control group. The two-month study will observe and compare the effects of each tool prior to system-wide roll out of selected tool (anticipated Spring 2025). We will use covariate-constrained randomization to balance the arms in terms of physician baseline time in notes, survey-measured level of burnout, and clinic days per week.

The primary purpose of the initiative is to improve quality, efficiency, and business operations at University of California, Los Angeles (UCLA) Health, and this initiative is not being done for research purposes. The results of this operational initiative will inform the widespread roll out of AI scribe tools across all providers within the UCLA Health System. Nevertheless, the UCLA study team plans to rigorously examine and publish the impact of this intervention across the health system, which is why the study team pre-registered the initiative.

Read the detailed description

This study will assess operational-oriented outcomes across all groups. Notably, all groups will eventually receive all interventions over time in this observational study of a randomized roll out of a QI initiative. Moreover, the primary purpose of this initiative is operational. In other words, based on the results of this initiative, one of these tools will be eventually selected and operationalized widely across the health system.

Enrolled participants are randomized to one of three groups. Randomization was needed to overcome secular trends, seasonal and holiday effects in December, and other factors confounding the relationship between exposure to the AI tools and the outcomes.

The primary aim of this study is to evaluate the impact of two ambient AI scribe technologies on clinician change from baseline time spent on EHR documentation, comparing each scribe to a control group. Secondary objectives include assessing the AI scribes' impact on clinician metrics such as burnout, physician satisfaction, and productivity. Additionally, the study team intends to perform an economic evaluation analysis of the tools to guide business decision making. The study team will also analyze physician reported effects of the AI tools on patient safety, equity, and any unintended consequences of the initiative.

Being tested
Use Nabla AI Scribe tool provided, Use AI Scribe tool provided by Vendor B
Keywords
Artificial Intelligence Scribe, Randomized Controlled Trial, Documentation Efficiency, Physician Burnout

What participants receive

What participants receive. In a randomised study each person is assigned to one group by chance.

  • Use Nabla AI Scribe tool providedOther

    AI Scribe technologies capture physician-patient conversations to create a transcript, then summarize the transcript in the form of a clinical notes. These tools are integrated into the EHR and automatically adds the generated text to the provider note. All physicians must inform patients about the recording and obtain their verbal consent, and instances of patients declining to consent are tracked.

    Nabla leverages its proprietary speech-to-text to transform the conversation into a written context, combined with HIPAA compliant Large Language Models (LLM) like Azure OpenAI's GPT-4. Nabla does not store any audio.

  • Use AI Scribe tool provided by Vendor BOther

    AI Scribe technologies capture physician-patient conversations to create a transcript, then summarize the transcript in the form of a clinical notes. These tools are integrated into the EHR and automatically adds the generated text to the provider note. All physicians must inform patients about the recording and obtain their verbal consent, and instances of patients declining to consent are tracked.

Study groups in NCT06792890
Study groupRoleWhat they receive
Nabla, Vendor of virtual AI scribe technologyOtherParticipants in this arm will utilize AI scribe tools from Nabla and will continue their usual clinical documentation processes, supported by the scribe software, which integrates with the EHR and automatically adds the generated text to the note. The Nabla AI scribe tool is transcriptional and does not provide clinical decision support.
Receives: Other: Use Nabla AI Scribe tool provided
Vendor B of virtual AI scribe technologyOtherParticipants in this arm will utilize AI scribe tools from Vendor B and will continue their usual clinical documentation processes, supported by the scribe software, which integrates with the EHR and automatically adds the generated text to the note. The AI scribe tool is transcriptional and does not provide clinical decision support.
Receives: Other: Use AI Scribe tool provided by Vendor B
No ScribeNo interventionParticipants in this arm will not have access to AI scribe tools and will continue their usual clinical documentation processes

What is being measured

The primary outcome is the main question the study is designed to answer.

  1. Change in the Time in Notes Per Note

    The primary outcome measure is the change in provider mean time in notes per note in the second month of the trial from the providers baseline mean time in notes per note for the six months prior to enrollment. This change will be computed on the natural log scale. No patient level information will be collected for this outcome measure.

    Measured: Study month 2

Secondary outcomes (8)

  1. Provider Burnout Score

    The Mini Z 2.0 Survey is a validated 10-item instrument designed to measure key factors influencing workplace satisfaction and burnout among healthcare professionals. Each item is scored on a Likert scale (1-5), with higher scores generally indicating more positive outcomes - greater job satisfaction, sufficiency of time for electronic medical record documentation, and lower levels of stress. For negatively framed items (e.g., stress due to the job or frustration with the electronic medical record), higher scores indicate lower levels of dissatisfaction. The total score ranges from 10 to 50, with scores ≥40 representing a joyful workplace. No patient level information will be collected for this outcome measure.

    Measured: Study month 2

  2. Provider Task Load Score

    Provider task load adapted from the NASA Task Load Index (TLX), a validated tool for assessing perceived workload across six sub-scales: mental demand, physical demand, temporal demand, performance, effort, and frustration. For this study, we adapted the TLX to focus on note-writing workload, including four sub-scales (mental demand, temporal demand, physical demand, and effort) as done previously. Each sub-scale is rated from 0 (low task load) to 100 (high task load) and summed together for a total score scale of 0 (low task load) to 400 (high task load), lower is better. No patient level information will be collected for this outcome measure.

    Measured: Study month 2

  3. Provider Professional Fulfillment

    The Professional Fulfillment Index (PFI) is a validated 16-item instrument that uses a 5-point Likert scale (0-4) to measure professional fulfillment, work exhaustion, and interpersonal disengagement. For this study, we utilize the 4-item work exhaustion subscale which is a mean of the 4-items within that subscale, where a low score (0) indicates a lower level of exhaustion and a high (4) score indicates greater level of exhaustion. No patient level information will be collected for this outcome measure.

    Measured: Study month 2

  4. Number of Physicians Who Are Considered Detractors, Passive, or Promoters

    Self-reported satisfaction survey that asks physicians to consider note accuracy, patient safety, equity, and other potential unintended consequences and rate their overall likelihood to recommend use of the tool on a 1-10 scale. Higher scores (10) indicate greater satisfaction and likelihood to recommend, whereas lower scores (1) indicate dissatisfaction and unlikelihood to recommend. Providers are grouped as "Promoters" if they respond 9-10, "Passive" if they respond 7-8, and "Detractors" if they respond with a value less than or equal to 6. This grouping matches commonly accepted "Net Promoter Score" groupings. No patient level information will be collected for this outcome measure.

    Measured: Study month 2

  5. Change in Provider RVU

    The study team will use physician-level billing information via RVU to determine their change in productivity from a retrospective baseline 6 months prior to enrollment. No patient level information will be collected for this outcome measure.

    Measured: Study month 2

  6. Change in EHR Signal (Activity) Data - Pajama Time

    We will examine change from a retrospective baseline 6 months prior to enrollment in Signal metrics including pajama time per scheduled day. Using this data will determine how a providers time is utilized in the EHR. No patient level information will be collected for this outcome measure.

    Measured: Study month 2

  7. Change in EHR Signal (Activity) Data - Time Outside Scheduled Hours

    We will examine change from a retrospective baseline 6 months prior to enrollment in Signal metrics including time outside scheduled hours per scheduled day. Using this data will determine how a providers time is utilized in the EHR. No patient level information will be collected for this outcome measure.

    Measured: Study month 2

  8. Change in EHR Signal (Activity) Data - Time on Unscheduled Days

    We will examine change from a retrospective baseline 6 months prior to enrollment in Signal metrics including time spent in the system on unscheduled days where . Using this data will determine how a providers time is utilized in the EHR. No patient level information will be collected for this outcome measure.

    Measured: Study month 2

Timeline

From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.

  1. First posted27 Jan 2025
  2. Study start4 Nov 2024
  3. Primary completion3 Jan 2025
  4. Study completion15 Jan 2025

Resources

More details

For doctors and researchers: how the study is designed, who runs it, and its registry IDs.

Study design
Study type
Interventional
Phase
Phase N/A
Primary purpose
Health services research
Allocation
Randomised
Intervention model
Single group
Masking
Single blind
Who is masked
Participant
Enrolment
238 participants (actual)
IDs, dates and publications
ClinicalTrials.gov ID
NCT06792890
Sponsor's protocol ID
NPR 40703
Acronym
AIScribe RCT
Status last verified
Apr 2026
First submitted
9 Dec 2024
Last updated
24 Apr 2026
Data monitoring committee
No
FDA-regulated drug
No
FDA-regulated device
No
Individual data sharing
No

Publications

  • Sittig DF, Singh H. Recommendations to Ensure Safety of AI in Real-World Clinical Care. JAMA. 2025 Feb 11;333(6):457-458. doi: 10.1001/jama.2024.24598.

    PubMed 39602298

  • Gianfrancesco MA, Tamang S, Yazdany J, Schmajuk G. Potential Biases in Machine Learning Algorithms Using Electronic Health Record Data. JAMA Intern Med. 2018 Nov 1;178(11):1544-1547. doi: 10.1001/jamainternmed.2018.3763.

    PubMed 30128552

  • Linzer M, McLoughlin C, Poplau S, Goelz E, Brown R, Sinsky C; AMA-Hennepin Health System (HHS) burnout reduction writing team. The Mini Z Worklife and Burnout Reduction Instrument: Psychometrics and Clinical Implications. J Gen Intern Med. 2022 Aug;37(11):2876-2878. doi: 10.1007/s11606-021-07278-3. Epub 2022 Jan 19. No abstract available.

    PubMed 35048290

  • Trockel M, Bohman B, Lesure E, Hamidi MS, Welle D, Roberts L, Shanafelt T. A Brief Instrument to Assess Both Burnout and Professional Fulfillment in Physicians: Reliability and Validity, Including Correlation with Self-Reported Medical Errors, in a Sample of Resident and Practicing Physicians. Acad Psychiatry. 2018 Feb;42(1):11-24. doi: 10.1007/s40596-017-0849-3. Epub 2017 Dec 1.

    PubMed 29196982

  • Garcia P, Ma SP, Shah S, Smith M, Jeong Y, Devon-Sand A, Tai-Seale M, Takazawa K, Clutter D, Vogt K, Lugtu C, Rojo M, Lin S, Shanafelt T, Pfeffer MA, Sharp C. Artificial Intelligence-Generated Draft Replies to Patient Inbox Messages. JAMA Netw Open. 2024 Mar 4;7(3):e243201. doi: 10.1001/jamanetworkopen.2024.3201.

    PubMed 38506805

  • Cruz Rivera S, Liu X, Chan AW, Denniston AK, Calvert MJ; SPIRIT-AI and CONSORT-AI Working Group; SPIRIT-AI and CONSORT-AI Steering Group; SPIRIT-AI and CONSORT-AI Consensus Group. Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension. Nat Med. 2020 Sep;26(9):1351-1363. doi: 10.1038/s41591-020-1037-7. Epub 2020 Sep 9.

    PubMed 32908284

  • McCoy LG, Manrai AK, Rodman A. Large Language Models and the Degradation of the Medical Record. N Engl J Med. 2024 Oct 31;391(17):1561-1564. doi: 10.1056/NEJMp2405999. Epub 2024 Oct 26. No abstract available.

    PubMed 39465898

  • Hendrix N, Veenstra DL, Cheng M, Anderson NC, Verguet S. Assessing the Economic Value of Clinical Artificial Intelligence: Challenges and Opportunities. Value Health. 2022 Mar;25(3):331-339. doi: 10.1016/j.jval.2021.08.015. Epub 2021 Oct 9.

    PubMed 35227443

  • Rotenstein L, Melnick ER, Iannaccone C, Zhang J, Mugal A, Lipsitz SR, Healey MJ, Holland C, Snyder R, Sinsky CA, Ting D, Bates DW. Virtual Scribes and Physician Time Spent on Electronic Health Records. JAMA Netw Open. 2024 May 1;7(5):e2413140. doi: 10.1001/jamanetworkopen.2024.13140.

    PubMed 38787556

  • Cao DY, Silkey JR, Decker MC, Wanat KA. Artificial intelligence-driven digital scribes in clinical documentation: Pilot study assessing the impact on dermatologist workflow and patient encounters. JAAD Int. 2024 Feb 20;15:149-151. doi: 10.1016/j.jdin.2024.02.009. eCollection 2024 Jun. No abstract available.

    PubMed 38571698

  • Owens LM, Wilda JJ, Grifka R, Westendorp J, Fletcher JJ. Effect of Ambient Voice Technology, Natural Language Processing, and Artificial Intelligence on the Patient-Physician Relationship. Appl Clin Inform. 2024 Aug;15(4):660-667. doi: 10.1055/a-2337-4739. Epub 2024 Jun 4.

    PubMed 38834180

  • Haberle T, Cleveland C, Snow GL, Barber C, Stookey N, Thornock C, Younger L, Mullahkhel B, Ize-Ludlow D. The impact of nuance DAX ambient listening AI documentation: a cohort study. J Am Med Inform Assoc. 2024 Apr 3;31(4):975-979. doi: 10.1093/jamia/ocae022.

    PubMed 38345343

  • Liu TL, Hetherington TC, Stephens C, McWilliams A, Dharod A, Carroll T, Cleveland JA. AI-Powered Clinical Documentation and Clinicians' Electronic Health Record Experience: A Nonrandomized Clinical Trial. JAMA Netw Open. 2024 Sep 3;7(9):e2432460. doi: 10.1001/jamanetworkopen.2024.32460.

    PubMed 39240568

  • Blackley SV, Huynh J, Wang L, Korach Z, Zhou L. Speech recognition for clinical documentation from 1990 to 2018: a systematic review. J Am Med Inform Assoc. 2019 Apr 1;26(4):324-338. doi: 10.1093/jamia/ocy179.

    PubMed 30753666

  • Heckman J, Mukamal KJ, Christensen A, Reynolds EE. Medical Scribes, Provider and Patient Experience, and Patient Throughput: a Trial in an Academic General Internal Medicine Practice. J Gen Intern Med. 2020 Mar;35(3):770-774. doi: 10.1007/s11606-019-05352-5. Epub 2019 Dec 5.

    PubMed 31808131

Questions about this trial

What is this clinical trial studying?

A study testing Use Nabla AI Scribe tool provided and Use AI Scribe tool provided by Vendor B in people with Physician Workflow and Artificial Intelligence (AI), run by University of California, Los Angeles at 1 site in United States.

The full registered description is in the About this study section and the official record.

Who may be eligible to take part?

The sponsor lists 1 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.

Is this clinical trial currently recruiting?

The record says: finished, as of the sponsor's last update on 24 Apr 2026. Status can change between updates, so confirm with the study contact before making plans.

Where is the study taking place?

The record lists 1 site in United States. Whether a site is taking part right now should be confirmed with that site. See every site.

What are the possible benefits and risks?

They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.

Are results available for this study?

Yes. Results are posted in the registry (first posted 24 Apr 2026). A summary is in the For doctors view, and the full results are on ClinicalTrials.gov.

How can a doctor refer a patient or contact the research team?

The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.

What does the primary outcome measure?

Change in the Time in Notes Per Note (time frame: Study month 2). It is what the study was designed to measure, not a finding.

Where can I read the original study information?

On the ClinicalTrials.gov record for NCT06792890, which also lists 25 linked publications.

Does a listing mean the treatment is approved or proven to work?

No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.

How often is this information updated?

AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 24 Apr 2026.

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