The Development and Evaluation of a Retrieval-Augmented Generation Large Language Model Virtual Assistant for Postoperative Instructions.

ABSTRACT:

Background

During postoperative recovery, patients and their caregivers often lack crucial information, leading to numerous repetitive inquiries that burden healthcare providers. Traditional discharge materials, including paper handouts and patient portals, are often static, overwhelming, or underutilized, leading to patient overwhelm and contributing to unnecessary ER visits and overall healthcare overutilization. Conversational chatbots offer a solution, but Natural Language Processing (NLP) systems are often inflexible and limited in understanding, while powerful Large Language Models (LLMs) are prone to generating "hallucinations".

Objective

To combine the deterministic framework of traditional NLP with the probabilistic capabilities of LLMs, we developed the AI Virtual

SUBMITTER: Haider SA 

PROVIDER: S-EPMC12649765 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature