Assessing the Efficacy and Impact of Ambient AI Scribes in Healthcare
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- The protocol lists: Ambient AI Scribe.
- Who it may be relevant to
- Registry conditions: Use of Ambient AI Scribes, Patient-Phyisican Interaction, Physician Workload, Physician Burnout. Basic parameters: No limits · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- Center list to be confirmed — check the primary protocol.
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
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Official title
Assessing the Efficacy and Impact of Ambient AI Scribes in Healthcare: A Randomized Controlled Trial
Overview
The goal of this clinical trial is to assess the impacts of ambient AI scribes on the workload and burnout in physicians who see patients in a clinic setting at least twice in a week, as well as the impacts on patient-physician interaction. The main questions it aims to answer are: * What is the impact of ambient AI scribe use on physician workload and burnout? * What is the impact of ambient AI scribe use on quality of patient-physician interaction? Researchers will compare the group of physicians using the ambient AI scribes to the group not using ambient AI scribes to see if there are any significant differences. Participants randomly assigned to Group A will make use of the AI scribe and participants randomly assigned to Group B will not use any AI scribe for the 10 working day duration of the study. They will be asked to complete a survey assessing workload and burnout immediately prior to the commencement of the study and at the end of each week of the study or 5 full working days for part time physicians. They will also invite their patients to complete a survey assessing their experience after each clinical interaction.
Interventions
- Device Ambient AI Scribe
Software that records audio of a clinical interaction and generates a clinical note.
Primary outcome measures
- Physician Workload as Measured Using the NASA Task Load Index [Time frame: From enrolment to the end of the study at 10 working days.]
- Physician Burnout as Measured Using the MBI - HSS (MP) [Time frame: From enrolment to the end of the study at 10 working days.]
- Quality of Patient-Physician Interaction As Measured Using the CARE Patient Feedback Measure Domain of "Really Listening" [Time frame: Throughout study completion at 10 working days.]
Secondary outcome measures (3)
- Documentation Quality as Measured by the PDQI-9 [Time frame: From enrollment to the completion of the study at 10 working days.]
- Time Spent Within the EMR for Each Clinical Note [Time frame: From enrollment to the completion of the study at 10 working days.]
- Time Spent in the EMR After Hours [Time frame: From enrollment to the completion of the study at 10 working days.]
Eligibility criteria
Inclusion criteria
- Physicians from family medicine or any specialty
- Physicians who regularly see patients in a clinic setting at least 2 days per week
Exclusion criteria
- Physicians who are planning to leave their practice during the study period
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: Yes
Study design
- Allocation
- Randomized
- Model
- Parallel assignment
- Masking
- Open label
- Primary purpose
- Health services research
Study locations
Center list to be confirmed — check the primary protocol.
Publications
- Li B, Crampton N, Yeates T, Xia Y, Tian X, Truong KN. Automating Clinical Documentation with Digital Scribes: Understanding the Impact on Physicians. In: CHI '21: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems. Association for Computing Machinery; 2021. Accessed July 1, 2024. https://doi.org/10.1145/3411764.3445172
- Davenport T, Kalakota R. The potential for artificial intelligence in healthcare. Future Healthc J. 2019 Jun;6(2):94-98. doi: 10.7861/futurehosp.6-2-94. PMID 31363513
- Saag HS, Shah K, Jones SA, Testa PA, Horwitz LI. Pajama Time: Working After Work in the Electronic Health Record. J Gen Intern Med. 2019 Sep;34(9):1695-1696. doi: 10.1007/s11606-019-05055-x. No abstract available. PMID 31073856
- Tierney AA, Gayre G, Hoberman B, et al. Ambient Artificial Intelligence Scribes to Alleviate the Burden of Clinical Documentation. NEJM Catal. 2024;5(3). doi:10.1056/cat.23.0404
- Shanafelt TD, Dyrbye LN, West CP, Sinsky CA. Potential Impact of Burnout on the US Physician Workforce. Mayo Clin Proc. 2016 Nov;91(11):1667-1668. doi: 10.1016/j.mayocp.2016.08.016. No abstract available. PMID 27814840
Identifiers
NCT: NCT07113938 · 5336