Clinical trial NCT06949462: Effectiveness of Large Language Model for Anaesthesia and Procedural Consent
Official title: Evaluating the Effectiveness of Large Language Models in Anaesthesia and Procedural Consent: A Comparative Analysis With Traditional Patient Consent Methods
Study type: INTERVENTIONAL
Overall status: NOT_YET_RECRUITING
Phase: NA
Conditions: Consent Forms
Anesthesia
Artificial Intelligence (AI)
Keywords: Anaesthesia
Informed Consent
Large Language Model
Artificial Intelligence
Lead sponsor: Singapore General Hospital
Sponsor class: OTHER
First posted: 2025-04-29
Enrollment: 120
Sex: ALL
Minimum age (years): 21
Maximum age (years): 99
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Allocation: RANDOMIZED
Intervention model: PARALLEL
Intervention model description: This study uses a parallel-group, randomized controlled trial (RCT) design to evaluate the effectiveness of PEAR, a conversational AI chatbot for anaesthesia risk counselling.

Participants are randomly assigned in a 1:1 allocation ratio to one of two arms:

Intervention Group: Receives anaesthesia risk information through the PEAR chatbot prior to their consultation with an anaesthetist.

Control Group: Receives anaesthesia counselling through the standard, face-to-face verbal consent process by a clinician.

Randomisation is stratified by surgical specialty to ensure balanced representation across different clinical settings.
Primary purpose: OTHER

Brief summary: 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.

Detailed description: 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.

Eligibility criteria: Inclusion Criteria:

- 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)

Exclusion Criteria:

* 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)

Primary outcomes:
measure: Patient Understanding of Anaesthesia Risks
timeFrame: Immediately post-consent (within 30 minutes of completing the counselling session)
Secondary outcomes:
measure: Reduction in Patient Anxiety Related to Anaesthesia
timeFrame: Immediately pre-consent and post-consent (within the same clinic visit)
measure: Usability of the PEAR Chatbot Assessed via System Usability Scale (SUS)
timeFrame: Immediately post-chatbot use (same clinic visit)
Other outcomes:
Locations:
city: Singapore
country: Singapore
facility: Singapore General Hospital
Study officials:
References:
citation: 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.