Language
English
Publication Date
4-1-2026
Journal
Critical Care Explorations
DOI
10.1097/CCE.0000000000001378
PMID
41860987
PMCID
PMC13008162
PubMedCentral® Posted Date
3-20-2026
PubMedCentral® Full Text Version
Post-print
Abstract
Objectives: To evaluate the feasibility of a large language model (LLM)-based chatbot for answering parental questions in the PICU and inform design of a randomized controlled trial (RCT).
Design: Prospective single-arm feasibility study conducted from August 2024 to December 2024.
Setting: Quaternary PICU.
Subjects: Fourteen parents of children admitted to the PICU.
Interventions: Parents engaged in 10-minute sessions with a HIPAA-compliant GPT-4o- (Generative Pretrained Transformer 4o, OpenAI, San Francisco, CA) based chatbot prompted with patient-specific electronic health record (EHR) data.
Measurements and main results: Feasibility was assessed through four criteria: parental engagement and satisfaction, provider perceptions, accuracy and safety, and recruitment. Of 16 eligible parents, 14 enrolled and completed all procedures (87.5% recruitment rate). Parents asked a median of six questions (range, 3-13) with 96% positive real-time satisfaction ratings. Post-interaction surveys demonstrated high perceived value (median, 5.0/6.0 across all domains; Net Promoter Score [NPS] +57). Of 1225 chatbot-generated sentences evaluated, 99.3% were accurate with all eight errors classified as minor (inter-rater reliability: Gwet's AC2, a chance-corrected inter-rater agreement coefficient, = 0.98; 95% CI, 0.97-0.99). Healthcare providers rated response quality highly (median, 5.0/6.0), although physicians expressed greater comfort with bedside use of the tool than nurses (5.0 vs. 4.0; p = 0.004). Sample size calculations using NPS as the primary endpoint suggest enrolling 135 participants would provide adequate power for a future RCT.
Conclusions: An EHR-informed LLM chatbot demonstrated high parental engagement and satisfaction, positive provider perception, and high accuracy and safety, supporting progression to a RCT.
Keywords
Humans, Large Language Models, Parents, Feasibility Studies, Intensive Care Units, Pediatric, Prospective Studies, Female, Male, Child, Preschool, Child, Infant, Adult, Electronic Health Records, Comprehension, artificial intelligence, electronic health records, large language models, patient education, pediatric intensive care unit
Published Open-Access
yes
Recommended Citation
Hunter, R Brandon; Thammasitboon, Satid; Rahman, Sreya; et al., "Feasibility of a Large Language Model Chatbot to Support Parental Understanding in the PICU" (2026). Faculty, Staff and Students Publications. 7531.
https://digitalcommons.library.tmc.edu/baylor_docs/7531