Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

Related Experiment Video

Updated: Jul 9, 2026

Setting Up a Stroke Team Algorithm and Conducting Simulation-based Training in the Emergency Department - A Practical Guide

Setting Up a Stroke Team Algorithm and Conducting Simulation-based Training in the Emergency Department - A Practical Guide

Published on: January 15, 2017

Feasibility of Tailoring Artificial Intelligence-Assisted Ambient Scribes for Intensive Care Unit Rounds: Algorithm

Ritchie Verma¹, Sandeep S Bains², Sai Harshith Reddy Muthani³

¹Department of Medicine, Yale School of Medicine, 1450 Chapel St, New Haven, CT, 06511, United States, 1 (203) 789-3000.

JMIR Medical Informatics

|July 7, 2026

Summary

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Please document the nature of the medical emergency.

BMJ quality & safety·2026

Same author

Translating innovation into payment: A.I. reimbursement in U.S. health care.

Journal of the American Medical Informatics Association : JAMIA·2026

Same author

Ethical considerations for clinical adoption of ambient digital scribe technology.

Journal of the American Medical Informatics Association : JAMIA·2025

Same author

Copy Tools in the Electronic Health Record: Perceptions, Implications, and Future Directions.

JMIR medical informatics·2025

Same author

Investigating Information Visualization to Combat Information Overload in Electronic Health Records: Protocol for a Randomized Controlled Trial.

JMIR research protocols·2025

Same author

Investigation of Information Overload in Electronic Health Records: Protocol for Usability Study.

JMIR research protocols·2025

Same journal

The Orphanet Nomenclature and Classification of Rare Diseases for Improved Patient Recognition and Data Interoperability: Qualitative and Quantitative Analysis.

JMIR medical informatics·2026

Same journal

Determinants of Acute Kidney Injury After Endoscopic Retrograde Cholangiopancreatography in Patients With Liver Cirrhosis: Retrospective Observational Study.

JMIR medical informatics·2026

Same journal

Multimodule Human-Artificial Intelligence Collaboration Pipeline for Large Language Model-Assisted Thematic Analysis Across Digital Health Interview Studies: Comparative Evaluation Study.

JMIR medical informatics·2026

Same journal

Graph Network Feature Space Fusion for Predicting Irregularly Sampled Medical Time-Series Data: Deep Learning Model Development and Validation Study.

JMIR medical informatics·2026

Same journal

Intrasystem Repeatability of S-Detect for Breast Ultrasound Classification With Identical Static Images: Single-Center Retrospective Repeatability Study.

JMIR medical informatics·2026

Same journal

Clinician Perspectives on Ambient AI Scribes in the Intensive Care Unit: Qualitative Interview Study.

JMIR medical informatics·2026

See all related articles

This summary is machine-generated.

Artificial intelligence (AI) ambient scribes show promise for reducing physician documentation burden in intensive care units (ICUs). This pilot study found AI improved documentation efficiency and clinician satisfaction, demonstrating feasibility for future healthcare applications.

Area of Science:

Medical Informatics
Artificial Intelligence in Healthcare
Clinical Documentation Improvement

Background:

Physician burnout is exacerbated by increasing documentation demands.
Current solutions for reducing documentation burden in inpatient and intensive care unit (ICU) settings are limited.
Artificial intelligence (AI) offers a potential solution for clinical documentation challenges.

Purpose of the Study:

To explore the feasibility of AI-assisted ambient scribes for capturing interprofessional ICU rounds.
To synthesize round transcripts into a single document to enhance documentation efficiency and clinician satisfaction.
To evaluate the effectiveness of customized prompts for large language models (LLMs) in generating daily progress notes from simulated ICU cases.

Main Methods:

Keywords:

large language model ambient scribe artificial intelligence clinical documentation intensive care unit prompt engineering

Related Experiment Videos

Last Updated: Jul 9, 2026

Setting Up a Stroke Team Algorithm and Conducting Simulation-based Training in the Emergency Department - A Practical Guide

Setting Up a Stroke Team Algorithm and Conducting Simulation-based Training in the Emergency Department - A Practical Guide

Published on: January 15, 2017

A two-phase pilot study involving simulated ICU cases.
Phase 1: Iterative prompt refinement for LLMs using audio transcripts, selecting the best model (M1).
Phase 2: Evaluation of M1 and an upgraded model (M6) using refined prompts, assessing accuracy, error rates, error severity (Harm scale), and usability (Physician Documentation Quality Instrument).

Main Results:

Iterative prompt improvements enhanced LLM accuracy and reduced errors.
Model M6 achieved 80% accuracy, significantly outperforming M1 (69%).
Errors were predominantly omissions; both models showed low error severity, with minimal harm potential. M6 also showed better usability scores.

Conclusions:

AI-assisted scribes are feasible for ICU documentation, improving efficiency and satisfaction.
Prompt engineering and LLM advancements are key drivers of AI performance in clinical settings.
This study provides a foundation for further research into AI applications to optimize healthcare documentation.