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Barriers to Effective Communication II

Barriers to Effective Communication II

The barriers to effective communication also include cultural barriers, semantic barriers, gender barriers, and time constraints.
Cultural barriers:
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Semantic barriers:
As a result of their tendency to use...

SBAR II: Application of SBAR

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SBAR is an effective communication tool used by healthcare professionals to communicate patient information accurately. SBAR stands for Situation, Background, Assessment, and Recommendation. For a better understanding, an example is given below.
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S: "Hello, Dr. Smith. This is Jane, RN, from the Med Surg unit. I am calling to tell you about Ms. White in Room 210, who is experiencing increased pain and redness at her incision site. Her recent...

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Innovations in Deaf Health Care Communication: Systematic Review of Sign Language Recognition Systems.

Milena Soriano Marcolino^1,2,3, Lucca Fagundes Ramos de Oliveira^2,4, Lucas Rocha Valle^1,2

¹Medical School, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil.

Journal of Medical Internet Research

|April 9, 2026

Summary

This review found that current sign language communication systems for healthcare settings have variable accuracy and often overlook crucial features like bidirectional communication and facial expressions. Further research is needed to improve usability and integration for deaf patients.

Keywords:

PRISMA Preferred Reporting Items for Systematic Reviews and Meta-Analyses artificial intelligence biomedical technology communication barriers computer neural network deafness gestures hearing loss sign language

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Area of Science:

Human-Computer Interaction
Health Informatics
Assistive Technology

Background:

Deaf individuals face significant communication barriers in healthcare.
These barriers can lead to patient safety risks, including misdiagnosis and treatment errors.

Purpose of the Study:

To systematically review human-computer interaction systems designed for sign language communication between deaf patients and hearing healthcare professionals.
Focus on systems tested with human participants in healthcare contexts.

Main Methods:

Comprehensive literature search across multiple databases (MEDLINE, Web of Science, ACM, IEEE Xplore, Scopus, Google Scholar) in March 2025.
Inclusion criteria: sign language recognition systems for healthcare, tested with human users.
Screening by two independent investigators, with senior researcher resolving disagreements.

Main Results:

23 studies met eligibility criteria; 65.2% used image-based systems, 34.8% used sensors.
Systems applied to general hospital care (43.5%), emergencies (34.8%), and primary care (17.4%).
Accuracy varied widely (25%-100% image-based, 72%-99.7% sensor-based); bidirectionality and facial expressions were largely ignored.

Conclusions:

Current sign language recognition systems show variable accuracy and lack essential features for effective healthcare communication.
No system fully meets healthcare integration requirements, highlighting the need for research on implementation, usability, and impact on care quality for deaf patients.