Role of large language models in improving provider-patient experience and interaction efficiency: A scoping review

Abstract

Large language models (LLMs) have rapidly emerged as transformative tools across multiple domains, including healthcare. The ability of LLMs to process vast amounts of data and generate human-like responses has facilitated their integration into patient care, particularly in enhancing communication, improving patient satisfaction, and streamlining administrative processes. Despite this potential, there are concerns regarding their accuracy, reliability, and ethical use in clinical settings. This scoping review aims to investigate and map the current literature on the use of LLMs in improving provider-patient experience and interaction efficiency. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews guidelines, we conducted a systematic search of Ovid MEDLINE, PubMed, and Google Scholar databases to identify relevant articles published between January 2015 and June 2024. Of the 3568 articles initially screened, 47 satisfied the inclusion criteria. These articles spanned 13 countries and encompassed diverse healthcare settings. Thematic areas of LLM utilization included improving communication between patients and healthcare providers, resolving patient inquiries, enhancing patient education, and increasing operational efficiency. Although numerous studies have yielded positive outcomes, significant challenges related to data accuracy, hallucinations, bias, and ethical concerns remain. LLMs can considerably improve patient experience in healthcare, particularly in areas of communication, education, and administrative efficiency. However, concerns regarding accuracy, ethical implications, and the need for rigorous safeguards to prevent misinformation impede their widespread adoption. Future research should focus on developing context-specific LLMs tailored to healthcare environments and addressing the identified limitations to optimize their implementation in clinical practice.

Keywords

Large language models / Patient experience / Artificial intelligence / Healthcare / Communication / Patient satisfaction / Patient interaction

Funding

None.

Conflict of interest

The authors declare they have no competing interests.

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