Machine Learning–Based Audiovisual Phenotyping for Measuring Communication, Shared Decision-Making, and Trust

TL;DR

This paper explores using machine learning to analyze audio and video data to better understand patient communication and trust in healthcare.

Contribution

The novelty lies in using audiovisual phenotyping to objectively assess communication and decision-making in healthcare.

Findings

Machine learning can detect discrepancies between self-reported experiences and nonverbal cues.

This approach offers an objective way to evaluate communication quality in healthcare settings.

It has potential to promote health equity by improving shared decision-making.

Abstract

Machine learning–based audiovisual phenotyping can reveal hidden discrepancies between patients’ self-reported experiences and nonverbal expressions, offering a promising tool for objectively assessing communication quality and advancing health equity.