JMIR Publications

ABSTRACT

Background:

Effective communication is crucial for high-quality healthcare, but barriers still disrupt patient-provider interactions. This leads to misdiagnoses, treatment delays, increased patient frustration, and inefficiencies within the system. As research on communication barriers in healthcare grows, there is a need for a thorough review of the field.

Objective:

This study systematically reviews literature on healthcare communication barriers to (1) identify and categorize key barrier types, (2) evaluate their impacts on patient experience, clinical decision-making, and health outcomes, and (3) examine intervention strategies designed to mitigate these challenges. The findings aim to inform the development of adaptive, technology-enabled solutions to improve patient-provider communication in increasingly diverse and digitally integrated healthcare settings.

Methods:

After evaluating 1,064 peer-reviewed papers, 103 fit the criteria for this review and were included in the study.

Results:

This review divides communication barriers into four categories: linguistic, cultural, psychological, and mental model differences. It highlights four key findings. First, there has been a notable increase in scholarly interest in communication barriers over the last decade, showing greater awareness in clinical fields. Second, although the four barriers have been known, little is known about the specific communication habits or physiological signs defining each barrier. Third, while researchers have linked individual barriers to adverse outcomes, no studies consider the combined effects of multiple co-occurring barriers. Fourth, despite the introduction of promising solutions like interpreter services, cultural training, and AI tools, a lack of basic knowledge about real-time indicators slows the development of intelligent systems.

Conclusions:

Future research should examine how linguistic, cultural, psychological, and cognitive barrier indicators interact. With a solid understanding of these barriers, we can explore combining AI tools with real-time physiological monitoring to develop adaptive, patient-centered communication strategies.