Comparison of Conventional Anesthesia Nurse Education and an Artificial Intelligence Chatbot (ChatGPT) Intervention on Preoperative Anxiety: A Randomized Controlled Trial

焦虑 随机对照试验 聊天机器人 医学 物理疗法 干预(咨询) 理解力 护理部 外科 精神科 人工智能 计算机科学 语言学 哲学
作者
Musashi Yahagi,Rie Hiruta,C M Miyauchi,Shoko Tanaka,A. Taguchi,Yuichi Yaguchi
出处
期刊:Journal of PeriAnesthesia Nursing [Elsevier BV]
卷期号:39 (5): 767-771 被引量:34
标识
DOI:10.1016/j.jopan.2023.12.005
摘要

ABSTRACT

Purpose

This study aimed to evaluate the effects of an artificial intelligence (AI) chatbot (ChatGPT-3.5, OpenAI) on preoperative anxiety reduction and patient satisfaction in adult patients undergoing surgery under general anesthesia.

Design

The study used a single-blind, randomized controlled trial design.

Methods

In this study, 100 adult patients were enrolled and divided into two groups: 50 in the control group, in which patients received standard preoperative information from anesthesia nurses, and 50 in the intervention group, in which patients interacted with ChatGPT. The primary outcome, preoperative anxiety reduction, was measured using the Japanese State-Trait Anxiety Inventory (STAI) self-report questionnaire. The secondary endpoints included participant satisfaction (Q1), comprehension of the treatment process (Q2), and the perception of the AI chatbot's responses as more relevant than those of the nurses (Q3).

Findings

Of the 85 participants who completed the study, the STAI scores in the control group remained stable, whereas those in the intervention group decreased. The mixed-effects model showed significant effects of time and group-time interaction on the STAI scores; however, no main group effect was observed. The secondary endpoints revealed mixed results; some patients found that the chatbot's responses were more relevant, whereas others were dissatisfied or experienced difficulties.

Conclusions

The ChatGPT intervention significantly reduced preoperative anxiety compared with the control group; however, no overall difference in the STAI scores was observed. The mixed secondary endpoint results highlight the need for refining chatbot algorithms and knowledge bases to improve performance and satisfaction. AI chatbots should complement, rather than replace, human health care providers. Seamless integration and effective communication among AI chatbots, patients, and health care providers are essential for optimizing patient outcomes.
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