Well-being at WorkEMPOWERNCT07742761
Emergency Medicine Practitioners Overall Well-being Enhancement With Ambient AI Scribes
A study testing Ambient AI Scribe in people with Well-being at Work and Burnout, run by Washington University School of Medicine at 1 site in United States.
Trial summary
The primary objective of the study is to investigate the impact of an ambient AI scribe on clinicians' wellness and well-being outcomes; additionally, the investigators will also explore how the use of the ambient AI scribe will lead to changes in documentation burden, clinical note characteristics and financial… Read more about this study
- Who is it for?
- Any age
- Conditions the study is for
- Well-being at Work, Burnout
- What is being tested?
- Ambient AI Scribe
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 55 participants
- Where
- 1 siteUnited States
- Study dates
- 29 Apr 2026to Jul 2027
- Phase
- Interventional
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
- Must be a Clinician (attendings and advanced practice practitioners) who is part of the Emergency Medicine Department
- Must be willing to use Ambient AI as apart of their clinical practice work
Who cannot join
- -Residents who are apart of the Emergency Medicine Department
Trial locations and contacts
1 site in 1 country.
United States1 site
Washington University School of Medicine
St Louis, Missouri, 63110
About this study
The primary objective of the study is to investigate the impact of an ambient AI scribe on clinicians' wellness and well-being outcomes; additionally, the investigators will also explore how the use of the ambient AI scribe will lead to changes in documentation burden, clinical note characteristics and financial productivity.
- Conditions
- Well-being at Work, Burnout
- Being tested
- Ambient AI Scribe
- Keywords
- Ambient AI Scribes, Wellness, well-being outcomes, Burnout
- Medical subject headings
- Burnout, Psychological
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Ambient AI ScribeOther
Clinicians will use an ambient AI scribe as part of routine clinical care. The AI scribe captures the patient-clinician conversation, generates a draft clinical note, and supports documentation in the electronic health record. Clinicians receive training before beginning use of the AI scribe. The intervention is introduced in three sequential waves using a stepped-wedge design, with all participants eventually receiving the intervention.
| Study group | Role | What they receive |
|---|---|---|
| Ambient AI Scribe Intervention, Wave 1 (Step-wedge design) | Experimental | Clinicians use an ambient AI scribe during patient encounters to assist with clinical documentation. Wave 1 participants receive the intervention for 18 weeks. Outcomes are compared before and after implementation. Receives: Other: Ambient AI Scribe |
| Ambient AI Scribe Intervention, Wave 2 (Step-wedge design) | Experimental | Clinicians use an ambient AI scribe during patient encounters to assist with clinical documentation. Wave 2 participants receive the intervention for 12 weeks. Outcomes are compared before and after implementation. Receives: Other: Ambient AI Scribe |
| Ambient AI Scribe Intervention Wave 3 (Step-wedge design) | Experimental | Clinicians use an ambient AI scribe during patient encounters to assist with clinical documentation. Wave 1 participants receive the intervention for 6 weeks. Outcomes are compared before and after implementation. Receives: Other: Ambient AI Scribe |
What is being measured
The primary outcome is the main question the study is designed to answer.
Impact of an ambient AI scribe on clinician well-being and professional fulfillment
A linear model will be used to describe the effect of Ambient tool introduction on our co-primary outcomes under the intention-to-treat framework with a random effects structure to describe within provider variability. While high or complete survey completion rates is expected, in the event that not all surveys are completed and returned, a primary analysis on completed surveys only will be performed, and perform sensitivity analyses accounting for potentially systematic survey non-response bias using a response weighting strategy, using provider, scheduling, and patient encounter characteristics to create survey response weights (within each survey time period), then reweighting observations to account for non-response patterns. In analyses for both co-primary outcomes, Wald type hypothesis tests for inferences on the overall Ambient treatment effect and compare p-values to 0.05 / 2 to conservatively account for multiple comparisons using Bonferroni's method will be performed.
Measured: From enrollment to the end of maintenance phase at 24 weeks
Secondary outcomes (1)
Assessment of the longitudinal changes in documentation burden
The investigators will fit Bayesian generalized linear models with appropriate link functions and likelihood choices (ie, log and Poisson for count data, logit and Bernoulli for binary responses) to for each encounter, while accounting for serial correlation overall through time (indexed to the day) using an autoregressive modeling structure, within provider patterns using a provider level random intercept, weekday, weekend, and holiday effects using fixed effects terms, and specify a flexible interrupted time series treatment effect using a semi-parametric, thin plate spline to describe how the introduction of Ambient AI affects the relationship between providers and notes, and how that relationship evolves over time. In sensitivity analyses, evaluation of how these effects and their evolution may differ by treatment wave will be assessed. All inferences will be based on describing the mean and 95% credible intervals for the treatment effect daily.
Measured: From enrollment to the end of maintenance phase at 42 weeks
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted3 Aug 2026
- Study start29 Apr 2026
- Primary completionDec 2026 (estimated)
- Study completionJul 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (30)Papers the sponsor has linked to this study.
- Other trials for Well-being at WorkStudies of the same condition, open and under way.
- More trials run by Washington University School of MedicineThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
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
- Parallel groups
- Masking
- Single blind
- Who is masked
- Care provider
- Enrolment
- 55 participants (actual)
Sponsor and investigators
- Lead sponsor
- Washington University School of Medicine
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Philip R.O. Payne, Washington University School of Medicine
- Investigators
- Thomas Kannampallil, PhD, Principal investigator, Washington University in Saint Louis
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07742761
- Sponsor's protocol ID
- 202602161
- Acronym
- EMPOWER
- Status last verified
- Aug 2026
- First submitted
- 21 Jul 2026
- Last updated
- 6 Aug 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
Tierney, Aaron A., et al. "Ambient artificial intelligence scribes to alleviate the burden of clinical documentation." NEJM Catalyst Innovations in Care Delivery
Nguyen, H., Bundy, H., Carroll, T., McWilliams, A., Dharod, A., Isreal, M., Cleveland, J., Bundy, R., Hetherington, T., & Liu, T.-L. (2024). Does AI-Powered Clinical Documentation Enhance Clinician Efficiency? A Longitudinal Studynejm ai, 1(12). https://doi.org/10.1056/AIoa2400659
Yu, H., Li, F., Gallis, J. A., & Turner, E. L. (2019). cvcrand: A package for covariate-constrained randomization and the clustered permutation test for cluster randomized trials. The R Journal, 11(2), 191-204. DOI: 10.32614/RJ-2019-027.
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 29196982Shin HS, Williams H, Braykov N, Jahan A, Meller J, Orenstein EW. The Influence of Artificial Intelligence Scribes on Clinician Experience and Efficiency among Pediatric Subspecialists: A Rapid, Randomized Quality Improvement Trial. Appl Clin Inform. 2025 Aug;16(4):1041-1052. doi: 10.1055/a-2657-8087. Epub 2025 Jul 17.
PubMed 40675605Shah SJ, Devon-Sand A, Ma SP, Jeong Y, Crowell T, Smith M, Liang AS, Delahaie C, Hsia C, Shanafelt T, Pfeffer MA, Sharp C, Lin S, Garcia P. Ambient artificial intelligence scribes: physician burnout and perspectives on usability and documentation burden. J Am Med Inform Assoc. 2025 Feb 1;32(2):375-380. doi: 10.1093/jamia/ocae295.
PubMed 39657021Albrecht M, Shanks D, Shah T, Hudson T, Thompson J, Filardi T, Wright K, Ator GA, Smith TR. Enhancing clinical documentation with ambient artificial intelligence: a quality improvement survey assessing clinician perspectives on work burden, burnout, and job satisfaction. JAMIA Open. 2025 Feb 21;8(1):ooaf013. doi: 10.1093/jamiaopen/ooaf013. eCollection 2025 Feb.
PubMed 39991073Pelletier JH, Watson K, Michel J, McGregor R, Rush SZ. Effect of a generative artificial intelligence digital scribe on pediatric provider documentation time, cognitive burden, and burnout. JAMIA Open. 2025 Jul 3;8(4):ooaf068. doi: 10.1093/jamiaopen/ooaf068. eCollection 2025 Aug.
PubMed 40620477Galloway JL, Munroe D, Vohra-Khullar PD, Holland C, Solis MA, Moore MA, Dbouk RH. Impact of an Artificial Intelligence-Based Solution on Clinicians' Clinical Documentation Experience: Initial Findings Using Ambient Listening Technology. J Gen Intern Med. 2024 Oct;39(13):2625-2627. doi: 10.1007/s11606-024-08924-2. Epub 2024 Jul 9. No abstract available.
PubMed 38980463Ma SP, Liang AS, Shah SJ, Smith M, Jeong Y, Devon-Sand A, Crowell T, Delahaie C, Hsia C, Lin S, Shanafelt T, Pfeffer MA, Sharp C, Garcia P. Ambient artificial intelligence scribes: utilization and impact on documentation time. J Am Med Inform Assoc. 2025 Feb 1;32(2):381-385. doi: 10.1093/jamia/ocae304.
PubMed 39688515Guo Y, Wang J, Hu D, Tam S, Gilman C, Chow E, Perret D, Pandita D, Zheng K. Evaluating ambient artificial intelligence documentation: effects on work efficiency, documentation burden, and patient-centered care. J Am Med Inform Assoc. 2026 Feb 1;33(2):273-282. doi: 10.1093/jamia/ocaf180.
PubMed 41100159Lukac PJ, Turner W, Vangala S, Chin AT, Khalili J, Shih YT, Sarkisian C, Cheng EM, Mafi JN. A Randomized-Clinical Trial of Two Ambient Artificial Intelligence Scribes: Measuring Documentation Efficiency and Physician Burnout. medRxiv [Preprint]. 2025 Jul 11:2025.07.10.25331333. doi: 10.1101/2025.07.10.25331333.
PubMed 40672471Afshar M, Baumann MR, Resnik F, Hintzke J, Sullivan AG, Wills G, Lemmon K, Dambach J, Ann Mrotek L, Quinn M, Abramson K, Kleinschmidt P, Brazelton TB, Leaf MA, Twedt H, Kunstman D, Patterson B, Liao F, Rasmussen S, Burnside ES, Goswami C, Gordon J. A Pragmatic Randomized Controlled Trial of Ambient Artificial Intelligence to Improve Health Practitioner Well-Being. NEJM AI. 2025 Dec;2(12):10.1056/aioa2500945. doi: 10.1056/aioa2500945. Epub 2025 Nov 26.
PubMed 41625485Stults CD, Deng S, Martinez MC, Wilcox J, Szwerinski N, Chen KH, Driscoll S, Washburn J, Jones VG. Evaluation of an Ambient Artificial Intelligence Documentation Platform for Clinicians. JAMA Netw Open. 2025 May 1;8(5):e258614. doi: 10.1001/jamanetworkopen.2025.8614.
PubMed 40314951Kanaparthy NS, Villuendas-Rey Y, Bakare T, Diao Z, Iscoe M, Loza A, Wright D, Safranek C, Faustino IV, Brackett A, Melnick ER, Taylor RA. Real-World Evidence Synthesis of Digital Scribes Using Ambient Listening and Generative Artificial Intelligence for Clinician Documentation Workflows: Rapid Review. JMIR AI. 2025 Oct 10;4:e76743. doi: 10.2196/76743.
PubMed 41071988