The Impact of Ambient AI Scribes on Physician Financial Productivity

Presentation Type: Oral Presentation - Regular
Presentation Time: 03:30 PM - 03:42 PM

Abstract Keywords: Generative AI in Clinical Workflow: Ambient Listening, Chart Summarization, Automated Response with LLM, Health Policy, Reimbursement and Affordability, and Sustainability, Clinician Well-Being
Primary Track: Advancing Wellness for Providers and Community with Consideration of Human Factors


Ambient AI scribes that automatically generate structured clinical notes are being rapidly adopted, but their financial impact is unclear. Using EHR and billing data from a large academic health system, we leveraged the staggered roll-out of AI scribes using a difference-in-differences framework to evaluate the impact of AI scribe adoption on ambulatory physician financial productivity across 4 outcomes: relative value units (RVUs) per week and per visit, visit volume, and claims denials. AI scribes were associated with higher RVUs and visit volume without increased denials, with effects that grew over time.

Speaker(s):
A J Holmgren, PhD
University of California, San Francisco

Author(s):
A J Holmgren, PhD - University of California, San Francisco; Julia Adler-Milstein, PhD, FACMI - UCSF School of Medicine; Jinoos Yazdany, MD MPH - UCSF;

---

Patient and Family Perspectives on Ambient AI Scribes in the Pediatric Setting

Presentation Type: Oral Presentation - Student
Presentation Time: 03:42 PM - 03:54 PM

Abstract Keywords: Generative AI in Clinical Workflow: Ambient Listening, Chart Summarization, Automated Response with LLM, Population Health, Digital Therapeutics, Remote Patient Monitoring (RPM), and Digital Engagement, Outcomes Improvement and Equity, Innovation Partnerships, Implementation Science, and Learning Health Systems, Ethics, Data Privacy, Cybersecurity, Reliability, and Security, Human Factors and Usability
Primary Track: Implementing Real-World Change, Digital Engagement, and Connected Health


This study explores parent and adolescent experiences with ambient listening technology during ambulatory visits (Stanford Medicine Children’s Health). Preliminary data (N=107) show 86% comfort, 81% perceived improvement in physician focus, and 71% willingness to reuse. Early qualitative findings underscore transparency and disclosure as critical priorities for both groups. Additional study is needed as adoption expands to fully elucidate the patient experience.

Speaker(s):
Bethel Mieso, MD
Stanford

Author(s):
Bethel Mieso, MD - Stanford; Holly Lung, MPH - Stanford University School of Medicine; Nymisha Chilukuri, MD - Stanford Medicine; Natalie Pageler, MD, MEd - Stanford University; Bonnie Halpern-Felsher, PhD - Stanford University School of Medicine; Ndidi Unaka, MD - Stanford University School of Medicine;

---

Patient Perspectives on Ambient AI: Insights from 50,000 Experience Surveys

Presentation Type: Oral Presentation - Regular
Presentation Time: 03:54 PM - 04:06 PM

Abstract Keywords: Human Factors and Usability, Generative AI in Clinical Workflow: Ambient Listening, Chart Summarization, Automated Response with LLM, Outcomes Improvement and Equity
Primary Track: Advancing Wellness for Providers and Community with Consideration of Human Factors


This mixed methods, large-scale observational study of ~50,000 patient experience surveys found no statistically significant association between likelihood to recommend scores and the use of ambient AI. Qualitative analysis of free-text patient experience survey responses revealed significant overlap in content between ambient and non-ambient encounters. Patient feedback from ambient encounters more frequently mentioned provider expertise.

Speaker(s):
Naveed Rabbani, MD
Sutter Health

Author(s):
Cheryl Stults, PhD - Sutter Health; Yian Guo, MS - Sutter Health; Meghan Martinez, MPH - Sutter Health; Nina Szwerinski, MS - Sutter Health; Joe Wilcox, AS - Sutter Health; Jackson Wilde, MS - Sutter Health; Sien Deng, PhD - Sutter Health; Richard Chapman, MS - Sutter Health; Veena Jones, MD - Sutter Health;

---

Mechanisms for Improvement in Patient Experience When Ambulatory Clinicians Use Ambient Intelligence Scribe Technology

Presentation Type: Oral Presentation - Regular
Presentation Time: 04:06 PM - 04:18 PM

Abstract Keywords: Generative AI in Clinical Workflow: Ambient Listening, Chart Summarization, Automated Response with LLM, Human Factors and Usability, Innovation Partnerships, Implementation Science, and Learning Health Systems
Primary Track: Advancing Wellness for Providers and Community with Consideration of Human Factors


Despite surveys reporting high patient satisfaction from clinicians’ use of ambient artificial intelligence scribes in clinical care, the mechanisms of how patient experience improves are unclear. We conducted 20 semi-structured interviews with patients, and found that improvements in patient experience were driven by improvements in perceived priority in conversations (e.g., more eye contact, less interrupted conversation flow), improved thoroughness and actionability of visit notes, and increased time spent answering patients’ questions and addressing concerns.

Speaker(s):
Oliver Nguyen, MSHI
University of Wisconsin at Madison

Author(s):
Majid Afshar, MD, MSCR - University of Wisconsin - Madison; Michael Jaeb, PhD, RN - University of Wisconsin at Madison; Mary Ryan Baumann, PhD - University of Wisconsin at Madison; Felice Resnik, PhD - University of Wisconsin at Madison; Anne Gravel Sullivan, PhD - University of Wisconsin at Madison; Graham Wills, PhD - UW Health; Jason Dambach, MD - University of Wisconsin at Madison; Leigh Ann Mrotek, PhD - University of Wisconsin at Madison; Mariah Quinn, MD, MPH - University of Wisconsin at Madison; UW Health; Kirsten Abramson, MD - University of Wisconsin at Madison; Peter Kleinschmidt, MD - University of Wisconsin at Madison; Thomas Brazelton, MD - University of Wisconsin at Madison; Margaret Leaf, BS - UW Health; Heidi Twedt, MD - University of Wisconsin at Madison; Brian Patterson, MD - University of Wisconsin at Madison; Frank Liao, PhD - UW Health; Joel Gordon, MD - UW Health;

---

EHR Time Decreases Are Not Associated with Burnout Changes Among AI Scribe Users

Presentation Type: Oral Presentation - Regular
Presentation Time: 04:18 PM - 04:30 PM

Abstract Keywords: Generative AI in Clinical Workflow: Ambient Listening, Chart Summarization, Automated Response with LLM, Clinician Well-Being, Workforce Automation, Communication, and Workflow Efficiency
Primary Track: Advancing Wellness for Providers and Community with Consideration of Human Factors


This study examines the relationship between reductions in electronic health record (EHR) time and changes in physician burnout among users of ambient documentation technology, commonly referred to as AI scribes. The research was conducted at Mass General Brigham, focusing on attending physicians who gained access to AI scribes between February and June 2024. Burnout was measured using the Professional Fulfillment Index before and after AI scribe adoption, while EHR activity metrics, including total EHR time, documentation time, and work outside scheduled hours, were tracked per patient hour. Among 67 physicians, the majority were medical specialists, and most used Nuance Dax as their AI scribe. While mean EHR time, documentation time, and work outside scheduled hours showed modest decreases after AI scribe implementation, burnout scores also declined significantly. However, analyses revealed that reductions in EHR time, documentation time, and work outside scheduled hours were not significantly associated with changes in burnout. This suggests that the mechanism by which AI scribes reduce burnout may be independent of their time-saving effects. Our findings imply that benefits, such as improved patient engagement, enhanced active listening, and reduced cognitive load during clinical visits, may contribute to lower burnout, rather than simply saving time. The study highlights the need for further qualitative research to identify the underlying factors driving burnout reduction among AI scribe users, beyond the benefits of EHR time savings.

Speaker(s):
Erik Holbrook, MD
Mass General Brigham

Author(s):
Lisa Rotenstein, MD, MBA, MSc - UCSF; Erik Holbrook, MD - Mass General Brigham; Michelle Frits, BA - Brigham and Women's Hospital; Christine Iannaccone, MPH - Brigham and Women's Hospital; Christopher Toretsky, MPH - University of California, San Francisco; Julia Adler-Milstein, PhD, FACMI - UCSF School of Medicine; Rebecca Mishuris, MD, MS, MPH - Mass General Brigham;

---

From Pilot to System Wide: Implementing Ambient Listening Across a Large Health System

Presentation Type: Oral Presentation - Regular
Presentation Time: 04:30 PM - 04:42 PM

Abstract Keywords: Generative AI in Clinical Workflow: Ambient Listening, Chart Summarization, Automated Response with LLM, Quality Informatics and Lean, Education and Training, Change Management, Clinician Well-Being
Primary Track: Driving Change at Scale through Effective Leadership and Governance


A large healthcare system implemented ambient listening technology using a staged rollout. A nine-month ambulatory pilot of 300 physicians, APPs, and psychologists was followed by a smaller pilot with residents and inpatient, urgent care, and emergency medicine clinicians. High-performing EMR users and waitlisted participants received early access before the system-wide go-live, which aligned with an EMR upgrade. This presentation outlines implementation strategies, key successes, challenges, and lessons learned from this large-scale deployment.

Speaker(s):
Jennifer Simpson, MD
University of Colorado

Author(s):
CT Lin, MD - UCHealth - Colorado; Kathryn McCaffrey, MD - UCHeatlh; Karen Chacko, MD - University of Colorado, School of Medicine; Pamela Martinez Villarreal, BS - UCHealth, CARE Innovation Center;

---