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Related Experiment Video

Updated: Jul 9, 2025

Enactive Phenomenological Approach to the Trier Social Stress Test: A Mixed Methods Point of View

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Published on: January 7, 2019

I'm not burned out. This is how I write notes.

Thomas H Payne^1,2, Grace K Turner²

¹Department of Medicine, University of Washington School of Medicine, Seattle, WA 98104-2499, United States.

|November 30, 2023

Summary

This summary is machine-generated.

This study introduces an automated transcription system to streamline clinical documentation. The innovative approach integrates voice recordings and AI, allowing providers to complete notes within scheduled hours, enhancing patient interaction.

Keywords:

documentation electronic health records physicians

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Area of Science:

Medical Informatics
Artificial Intelligence in Healthcare

Background:

Clinical documentation is a significant burden on healthcare providers.
Existing methods often lead to errors and interfere with patient care.

Purpose of the Study:

To describe an automated transcription system designed to alleviate documentation challenges.
To enable efficient completion of clinical notes within scheduled hours.

Main Methods:

Providers record voice notes post-patient visit via smartphone.
An AI system utilizes general and provider-specific language models for transcription.
Physician edits refine the provider-specific model iteratively.

Main Results:

Automated transcription system completes voice files within a median of 3.4 minutes.
Generated clinical notes average 363 words, formatted to provider preference.
Documentation is completed within scheduled clinical hours.

Conclusions:

The automated system addresses common documentation problems effectively.
This method minimizes copy-forward errors and preserves patient-provider interaction.
The approach offers a viable solution for many physicians facing documentation burdens.