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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

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Physician Perspectives on Ambient AI Scribes.

Shreya J Shah^1,2, Trevor Crowell², Yejin Jeong²

¹Department of Medicine, Stanford University School of Medicine, Stanford, California.

JAMA Network Open

|March 24, 2025

Summary

Ambient AI scribe tools can improve physician workload and work-life integration, but require further development for accuracy and broader accessibility. These AI scribes show promise in reducing clinician burden.

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Area of Science:

Medical Informatics
Artificial Intelligence in Healthcare
Qualitative Research Methods

Background:

Limited qualitative research exists on ambient artificial intelligence (AI) scribe tools.
Such studies offer valuable insights into AI implementation through user experiences.

Purpose of the Study:

To evaluate physician perspectives on ambient AI scribe tools.
To identify facilitators and barriers to AI scribe adoption and sustainability.

Main Methods:

Qualitative study utilizing semistructured interviews.
The RE-AIM/PRISM framework guided the study.
Thematic analysis employed inductive and deductive approaches.

Main Results:

Physicians reported positive impacts on cognitive demand, work-life integration, and patient engagement.
Ease of use and editing were key facilitators; limited functionality and device access were barriers.
Concerns were raised regarding accuracy, note length, and editing requirements.

Conclusions:

Ambient AI scribes positively influence physician workload and patient engagement.
Addressing identified barriers and incorporating user suggestions can enhance AI scribe adoption and sustainability.
User-centered recommendations can guide improvements for future AI scribe iterations.