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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:
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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Ambient AI Scribe Users | Experimental | This group will make use of the ambient AI scribe in their clinical visits to generate clinical notes for them over the duration of the study. |
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| Non-Ambient AI Users | No Intervention | This group will continue to generate their own clinical notes without the use of ambient AI scribes over the duration of the study. |
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Ambient AI Scribe | Device | Software that records audio of a clinical interaction and generates a clinical note. |
|
| Measure | Description | Time Frame |
|---|---|---|
| Physician Workload as Measured Using the NASA Task Load Index | The NASA-TLX is separated into six 100-point scales with 5-point steps that assess mental demand, physical demand, temporal demand, performance, effort, and frustration. A higher score indicates that they feel a greater degree of each of the domains. | From enrolment to the end of the study at 10 working days. |
| Physician Burnout as Measured Using the MBI - HSS (MP) | The MBI-HSS (MP) is created specifically for medical personnel and assesses emotional exhaustion (9 items), depersonalization (5 items), and personal accomplishment (8 items). | 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" | The CARE measure consists of a series of 10 questions asked of patients with our focus on the domain of whether the physician was "really listening (paying close attention to what you were saying; not looking at the notes or computer as you were talking)" scored on a 5 point Likert scale. | Throughout study completion at 10 working days. |
| Measure | Description | Time Frame |
|---|---|---|
| Documentation Quality as Measured by the PDQI-9 | Documentation is scored by two blinded reviewers on a scale from 1-5 (with 5 indicating a higher quality) across 9 domains: up-to-date, accurate, thorough, useful, organized, comprehensible, succinct, synthesized, and internally consistent. | From enrollment to the completion of the study at 10 working days. |
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Inclusion Criteria:
Exclusion Criteria:
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| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Scott Adams, MD, PhD, MEd, FRCPC | Contact | 306-655-2402 | scott.adams@usask.ca | |
| Sundus Zia | Contact | sundus.zia@usask.ca |
| Name | Affiliation | Role |
|---|---|---|
| Scott Adams, MD, PhD, MEd, FRCPC | University of Saskatchewan College of Medicine | Principal Investigator |
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| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| Background | 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 | ||
| 31363513 | Background | 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. | |
| 31073856 |
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Two blinded reviewers will independently review each clinical note and assign a PDQI-9 score according to the nine attributes that measure various aspects of documentation quality.
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| Time Spent Within the EMR for Each Clinical Note | The amount of time spent within the EMR for each clinical note and the amount of time spent editing the note on the ambient AI platform will be tracked using EMR logs and time-tracking tools. | From enrollment to the completion of the study at 10 working days. |
| Time Spent in the EMR After Hours | The amount of time spent using the EMR after regular working hours will be measured using EMR logs and time-tracking tools. | From enrollment to the completion of the study at 10 working days. |
| Background |
| 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. |
| Background | 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 |
| 27814840 | Background | 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. |