Related Experiment Video
Updated: Aug 8, 2026
Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
Published on: December 6, 2024
How large language models can be used for teamwork and communication in healthcare settings: A scoping review
Ilse Super1, Olya Rezaeian1, Onur Asan1
1Department of Systems Engineering, Stevens Institute of Technology, 1 Castle Point Terrace, Hoboken, NJ 07030, USA.
Background:
Generative artificial intelligence, particularly large language models (LLMs), has rapidly advanced and shows promise in healthcare for supporting teams through their ability to understand and generate medical text. While human-AI collaboration has been explored, the integration of LLMs into healthcare teams remains under-researched.
Objective:
This scoping review aims to examine how LLMs are currently used to support teamwork and communication in healthcare teams, including both solely professional teams and those involving patients.
Methods:
Following PRISMA-ScR guidelines, we registered our review with the Open Science Framework (July 30, 2025). We searched PubMed, Web of Science, and ScienceDirect for articles from 2014 to 2024. After screening 3,865 unique titles and abstracts, 127 full texts were reviewed.
Results:
Twenty studies were included, predominantly employing quantitative and simulation-based designs, with limited in situ evaluations. LLM use cases were categorized into decision support, communication, and administrative functions. Outcome measures primarily focused on accuracy and quality (15/20 studies), with fewer assessing safety (4/20), readability or empathy (5/20), workflow efficiency (3/20), and error modes (2/20). Across use cases, LLMs demonstrated potential to improve efficiency and communication, although performance and risks varied by task complexity and use context.
Conclusion:
LLMs hold substantial potential to enhance healthcare teamwork by supporting clinical decisions, streamlining administrative workflows, and improving patient communication. However, ethical, legal, and accountability concerns remain. Current studies largely evaluate model performance without considering the dynamics of human team members. Future research should examine LLMs' impact on trust, collaboration, and decision-making within clinical teams, while implementation efforts must address contextual and interdisciplinary factors to ensure responsible integration.
Related Concept Videos
01:15Methods of Documentation VI: Case Management Model
For example, a patient with a chronic illness...
01:21Barriers to Effective Communication II
Cultural barriers:
Differences in values, beliefs, religion, knowledge, and tradition can significantly impact communication. Awareness of nonverbal cues is critical, especially when conversing with a patient from a different culture. What appears appropriate in one culture may be inappropriate in another.
Semantic barriers:
As a result of their tendency to use...
01:14SBAR II: Application of SBAR
SBAR Report from a Nurse to a Health Care Provider
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...
01:29Documentation in Long-Term and Home Healthcare Setting
Long-Term Care Facilities
01:29Levels of Communication I: Intrapersonal, Interpersonal, and Small Group
We can participate in these relationships through verbal, nonverbal, and mediated communication. We engage in verbal communication when we use words during our interaction to convey specific meanings. On the other hand, nonverbal communication refers to various factors that can impact how we understand each other—for example, facial expressions.
We interact with others using mediated technologies like the...