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Therapeutic Communication

Therapeutic Communication

Communication is a lifelong learning process. Through therapeutic communication, nurses can collect relevant assessment data, provide education and counseling, and interact during nursing interventions. Sending and receiving messages occur through verbal and nonverbal communication techniques and can happen separately or simultaneously.
Verbal communication depends on language or a prescribed way of using words so that people can share information effectively. The critical aspects of verbal...

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

Shely Khaikin¹, Vineet Tiruvadi^2,3, Jeffrey Brooks³

¹Shared Decision Making Laboratory, Temple University, Philadelphia, PA, United States.

JMIR Formative Research

|March 13, 2026

Summary

This summary is machine-generated.

Machine learning analyzes audiovisual data to detect differences between patient reports and nonverbal cues. This technology offers objective communication assessment and promotes health equity.

Keywords:

AI artificial intelligence audiovisual digital phenotyping depression natural language processing primary care shared decision-making

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Area of Science:

Computational linguistics
Health informatics
Biomedical engineering

Background:

Effective patient-provider communication is crucial for accurate diagnosis and treatment.
Subjective patient self-reports may not always align with objective clinical observations.
Nonverbal communication provides valuable insights into a patient's experience.

Purpose of the Study:

To investigate the utility of machine learning-based audiovisual phenotyping.
To identify discrepancies between patients' self-reported experiences and their nonverbal expressions.
To explore the potential for objective assessment of communication quality and advancement of health equity.

Main Methods:

Development and application of machine learning algorithms to analyze audiovisual data.
Comparison of patient self-reported data with nonverbal cues captured through video and audio recordings.
Statistical analysis to quantify discrepancies and assess communication quality.

Main Results:

Machine learning models successfully identified significant discrepancies between patient self-reports and nonverbal expressions.
Audiovisual phenotyping provided objective measures of communication quality.
The approach demonstrated potential for identifying communication barriers impacting health equity.

Conclusions:

Machine learning-based audiovisual phenotyping is a promising tool for objective communication assessment.
This technology can reveal subtle patient experiences missed by traditional methods.
Advancing health equity through more accurate and equitable patient communication assessment is feasible.