Effectiveness of Large Language Model for Anaesthesia and Procedural Consent

Официальное название

Evaluating the Effectiveness of Large Language Models in Anaesthesia and Procedural Consent: A Comparative Analysis With Traditional Patient Consent Methods

Обзор

Patient understanding of anaesthesia risks remains inconsistent due to time constraints, language barriers, and variable clinician communication styles. Traditional verbal consent may not consistently ensure comprehension or reduce preoperative anxiety. PEAR (Patient Education of Anesthesia Risks) is a multilingual, AI-driven chatbot developed to enhance patient education and improve the quality of anaesthesia risk counselling. Study Objective: To compare PEAR's performance in delivering anaesthesia risk consent against the standard face-to-face verbal method.

Подробное описание

This study evaluates the effectiveness of PEAR (Patient Education of Anaesthesia Risks), a conversational AI-based chatbot designed to deliver anaesthesia risk education to patients in a personalized, interactive, and multilingual format. The goal is to support informed consent by improving patient comprehension, satisfaction, and reducing anxiety, while also streamlining clinician workflow.

Participants undergoing elective surgery will be randomly assigned to either receive anaesthesia counselling via PEAR before their consultation with the anaesthetist (intervention group) or undergo the standard face-to-face verbal consent process (control group). The PEAR chatbot is accessed through a secure digital interface and presents information aligned with institutional anaesthesia protocols.

The study will be conducted at hospitals within the SingHealth cluster in Singapore. Following the consent process, patients will complete a short quiz to assess understanding, a survey to evaluate satisfaction, and an anxiety scale. Clinicians will record time taken and perceived workload.

All patients will still meet their anaesthetist, ensuring clinical oversight is maintained. This study does not alter standard care but evaluates a digital adjunct to enhance it. Data will be collected electronically, anonymised, and stored securely. Insights from this trial may inform the wider implementation of digital tools in perioperative patient education.

Вмешательства

• Другое PEAR
  Participants in the intervention arm will receive anaesthesia risk counselling through the PEAR (Patient Education of Anaesthesia Risks) chatbot prior to their face-to-face consultation with an anaesthetist. PEAR is a multilingual, AI-powered conversational tool designed to provide personalized, interactive education on anaesthesia-related procedures, risks, and safety information. The chatbot delivers content aligned with institutional guidelines and allows patients to explore topics at their

Первичные конечные точки

• Patient self-reported understanding of anaesthesia risks [Срок оценки: Immediately post-interaction with the PEAR Chatbot]

Вторичные конечные точки (5)

• Perceived Usefulness [Срок оценки: Immediately pre-consent and post-consent (within the same clinic visit)]
• Cost effectiveness [Срок оценки: Immediately post-chatbot use (same clinic visit)]
• Perceived Ease of Use (PEOU) [Срок оценки: Immediately pre-consent and immediately post-consent (within the same clinic visit).]
• Attitude Toward Using (ATT) [Срок оценки: Immediately pre-consent and immediately post-consent (within the same clinic visit).]
• Behavioral Intention to Use (BI) [Срок оценки: Immediately pre-consent and immediately post-consent (within the same clinic visit).]

Критерии участия

Критерии включения

\- Adults (≥21 years old) undergoing elective surgery requiring anaesthesia

Classified as ASA Physical Status I to III

• Able to provide informed consent
• Able to communicate effectively in English, Chinese (Mandarin), Malay, or Tamil
• Willing and able to complete questionnaires and interact with the PEAR chatbot (intervention arm)

Критерии исключения

• ASA Physical Status IV or above
• Cognitive impairment or psychiatric conditions that may limit comprehension or communication
• Non-literate patients or those unable to understand English, Chinese, Malay, or Tamil
• Emergency surgery cases
• Prior participation in the study (to prevent bias)

Критерии приведены из реестра в оригинале (на английском). Окончательную оценку соответствия проводит исследовательский центр.

Здоровые добровольцы: Да

Дизайн исследования

Распределение

Рандомизированное

Модель

Параллельные группы

Маскирование

Простое слепое

Основная цель

Другое

Центры проведения

Сингапур · 2 центра

• Singapore General Hospital — Singapore
• Singapore General Hospital — Singapore

Публикации

• Ke YH, Jin L, Elangovan K, Abdullah HR, Liu N, Sia ATH, Soh CR, Tung JYM, Ong JCL, Kuo CF, Wu SC, Kovacheva VP, Ting DSW. Retrieval augmented generation for 10 large language models and its generalizability in assessing medical fitness. NPJ Digit Med. 2025 Apr 5;8(1):187. doi: 10.1038/s41746-025-01519-z. PMID 40185842

Идентификаторы

NCT: NCT06949462 · 2025-04-22

Первоисточники (государственные реестры)

• ClinicalTrials.gov (NIH, США)

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