Clinician Burnout, Clinician Experience
Conditions
Keywords
Ambient AI, Clinician burnout
Brief summary
We are piloting a new technology (Abridge) that uses artificial intelligence to listen to clinician-patient office visits and then document the interaction in the electronic health record.
Detailed description
This study will evaluate pilot implementation of the Abridge Artificial Intelligence (AI) Platform (Abridge) in UCHealth clinics. Abridge licenses will be deployed to 116 physician and advanced practice clinicians to accomplish the following goals: 1. Determine if Abridge meaningfully impacts clinician documentation time and burnout or stress 2. Determine whether to scale Abridge beyond the pilot We will evaluate clinician experiences using Abridge, particularly with regard to documentation time and clinician burnout or stress. Study findings will explore value added in integrating Abridge into clinician practice, and guide decision-making for entering into a commercial agreement with Abridge. This pilot will also inform best practices for scaling Abridge to a broader user base across UCHealth outpatient practices.
Interventions
OTHERAmbient AI for clinician documentation
Ambient AI for clinician documentation
Sponsors
University of Colorado, Denver
Study design
Allocation
RANDOMIZED
Intervention model
PARALLEL
Primary purpose
OTHER
Masking
NONE
Intervention model description
Randomized controlled trial with waitlist
Eligibility
Sex/Gender
ALL
Healthy volunteers
Yes
Inclusion criteria
-clinician (physician, advance practice professional, or licensed mental health professional) employed by UCHealth * See clinic patients at least three half-day sessions per week. (Half-day sessions seeing or supervising patients with residents and fellows will NOT count toward the weekly total. Half-day sessions seeing patients with a medical student CAN count toward the weekly total, however, Abridge can only capture conversations where you are physically present with patients.) * Own an iOS smartphone and be willing to download and use the Epic Haiku smartphone app during clinic visits
Exclusion criteria
* Trainees including residents and fellows * PT/OT, ED, maternal/fetal, pediatric, dental, eye specialties
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Adoption | 2, 4 and 8 months | Percent of eligible visits in which intervention is used. Collected via EHR data |
| Burnout - High emotional exhaustion | Baseline, 2, 4 and 8 months | Validated measure collected by survey. Reference: https://pmc.ncbi.nlm.nih.gov/articles/PMC3475833/ |
| Burnout - High depersonalization | Baseline, 2, 4 and 8 months | Validated measure collected by survey. Reference: https://pmc.ncbi.nlm.nih.gov/articles/PMC3475833/ |
| Burnout - emotional exhaustion and depersonalization | Baseline, 2, 4 and 8 months | Validated measure collected by survey. Reference: https://pmc.ncbi.nlm.nih.gov/articles/PMC3475833/ |
| Time in notes per appointment | Baseline, 2, 4 and 8 months | Collected via EHR signal data |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Reach | 2, 4 and 8 months | Proportion and representativeness of eligible patients and visits intervention was used for |
| Clinician experience | Baseline, 2, 4 and 8 months | Likelihood to recommend via validated NPS, qualitative drivers of experience, likelihood to change jobs, Weiners feasiblity/implementability/acceptability, system usability scale |
| Time documenting | Baseline, 2, 4 and 8 months | Time spent documenting beyond appointment, average time in the EHR, time of last log out of the day, pajama time documenting |
| Operational efficiency | Baseline, 2, 4 and 8 months | Same day note closure, average note star rating, average note turn around time |
| Adoption | 2, 4 and 8 months | Abandonment rate and representativeness |
Countries
United States
Outcome results
None listed