Clinical trial NCT06792890: A Randomized Controlled Trial of Ambient Artificial Intelligence Scribe Technologies
Official title: A Randomized Controlled Trial of Two Ambient Artificial Intelligence Scribe Technologies to Improve Documentation Efficiency and Reduce Physician Burnout
Study type: INTERVENTIONAL
Overall status: ACTIVE_NOT_RECRUITING
Phase: NA
Conditions: Physician Workflow
Artificial Intelligence (AI)
Keywords: Artificial Intelligence Scribe
Randomized Controlled Trial
Documentation Efficiency
Physician Burnout
Lead sponsor: University of California, Los Angeles
Sponsor class: OTHER
First posted: 2025-01-27
Enrollment: 238
Sex: ALL
Standard ages: CHILD
ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Allocation: RANDOMIZED
Intervention model: SINGLE_GROUP
Primary purpose: HEALTH_SERVICES_RESEARCH

Brief 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). 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.

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.

Eligibility criteria: Inclusion Criteria:

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

Exclusion Criteria:

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

Primary outcomes:
measure: Change in the time in notes per note
timeFrame: Study month 2
Secondary outcomes:
measure: Provider Burnout Score
timeFrame: Study month 2
measure: Provider Task Load Score
timeFrame: Study month 2
measure: Provider Professional Fulfillment
timeFrame: Study month 2
measure: Provider Satisfaction Scores
timeFrame: Study month 2
measure: Change in clinic turnover rate
timeFrame: Study month 2
measure: Change in provider RVU
timeFrame: Study month 2
measure: Change in EHR Signal (Activity) Data - Pajama Time
timeFrame: Study month 2
measure: Change in EHR Signal (Activity) Data - Time outside scheduled hours
timeFrame: Study month 2
measure: Change in EHR Signal (Activity) Data - Number of unscheduled days
timeFrame: Study month 2
Other outcomes:
Locations:
city: Los Angeles
country: United States
facility: UCLA Health System
state: California
Study officials:
References:
citation: 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.
citation: 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.
citation: 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.
citation: 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.
citation: 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.
citation: 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.
citation: 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.
citation: 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.
citation: 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.
citation: 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.
citation: 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.
citation: 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.
citation: 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.
citation: 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.
citation: 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.
citation: Bates DW, Landman AB. Use of Medical Scribes to Reduce Documentation Burden: Are They Where We Need to Go With Clinical Documentation? JAMA Intern Med. 2018 Nov 1;178(11):1472-1473. doi: 10.1001/jamainternmed.2018.3945. No abstract available.
citation: Mishra P, Kiang JC, Grant RW. Association of Medical Scribes in Primary Care With Physician Workflow and Patient Experience. JAMA Intern Med. 2018 Nov 1;178(11):1467-1472. doi: 10.1001/jamainternmed.2018.3956.
citation: Steinkamp J, Kantrowitz JJ, Airan-Javia S. Prevalence and Sources of Duplicate Information in the Electronic Medical Record. JAMA Netw Open. 2022 Sep 1;5(9):e2233348. doi: 10.1001/jamanetworkopen.2022.33348.
citation: Sinsky C, Colligan L, Li L, Prgomet M, Reynolds S, Goeders L, Westbrook J, Tutty M, Blike G. Allocation of Physician Time in Ambulatory Practice: A Time and Motion Study in 4 Specialties. Ann Intern Med. 2016 Dec 6;165(11):753-760. doi: 10.7326/M16-0961. Epub 2016 Sep 6.
citation: Guille C, Sen S. Burnout, Depression, and Diminished Well-Being among Physicians. N Engl J Med. 2024 Oct 24;391(16):1519-1527. doi: 10.1056/NEJMra2302878. No abstract available.
citation: Lou SS, Lew D, Harford DR, Lu C, Evanoff BA, Duncan JG, Kannampallil T. Temporal Associations Between EHR-Derived Workload, Burnout, and Errors: a Prospective Cohort Study. J Gen Intern Med. 2022 Jul;37(9):2165-2172. doi: 10.1007/s11606-022-07620-3. Epub 2022 Jun 16.
citation: Moy AJ, Schwartz JM, Chen R, Sadri S, Lucas E, Cato KD, Rossetti SC. Measurement of clinical documentation burden among physicians and nurses using electronic health records: a scoping review. J Am Med Inform Assoc. 2021 Apr 23;28(5):998-1008. doi: 10.1093/jamia/ocaa325.
citation: Peccoralo LA, Kaplan CA, Pietrzak RH, Charney DS, Ripp JA. The impact of time spent on the electronic health record after work and of clerical work on burnout among clinical faculty. J Am Med Inform Assoc. 2021 Apr 23;28(5):938-947. doi: 10.1093/jamia/ocaa349.