The Affiliated Hospital of Guizhou Medical University
Guiyang, Guizhou, 550004, China
NCT Number: NCT07141420
The goal of this clinical trial is to evaluate whether Large Language Models (LLMs) combined with an optimized care program can effectively manage Post-Intensive Care Syndrome (PICS) in adult ICU survivors (aged ≥18 years) discharged from a tertiary hospital in China. The main questions it aims to answer are:
* Does the intervention (optimized program + LLMs) improve physical, psychological, cognitive, and social function recovery compared to standard care or the optimized program alone? * How do patients experience and perceive the utility of LLMs in PICS self-management during recovery?
Researchers will compare three groups:
1. Group A (routine care) 2. Group B (optimized program without LLMs) 3. Group C (optimized program + LLMs) to see if adding LLMs significantly enhances PICS symptom management, patient self-efficacy, and quality of life over 6 months post-discharge.
Participants will:
* Install and use the Kimi Smart Assistant LLM (Group C only) for health queries under nurse supervision. * Complete standardized questionnaires at discharge (baseline), 7 days, 1 month, 3 months, and 6 months post-discharge:
* PICS Symptom Questionnaire (PICSQ) * Pittsburgh Sleep Quality Index (PSQI) * Anxiety (GAD-7) and Depression (PHQ-9) scales * Self-Management Ability Scale (AHSMSRS) * Attend semi-structured interviews (Group C only) at 3 and 6 months to share experiences with LLM use.
Interested in participating?
Request Info18 year–100 year
All sexes
Interventional
Not applicable
Guiyang, Guizhou, 550004, China
Healthy volunteers accepted: No
Only the study team can determine whether someone qualifies for participation.
Inclusion criteria
Exclusion criteria
Hearing impairment Dysarthria Other conditions preventing follow-up assessments.
Participants receive standard post-ICU follow-up care according to hospital protocols . This includes routine health assessments and general rehabilitation guidance at designated intervals (discharge, 1/3/6 months post-discharge). No structured PICS management program or AI technology is provided.
An evidence-based, multidisciplinary rehabilitation protocol for Post-Intensive Care Syndrome (PICS) management, developed using the Health Promotion Model (HPM). It includes:
Personalized rehabilitation plans addressing physical, cognitive, and psychological recovery.
Structured follow-up at discharge, 1/3/6 months post-discharge. Components: Physical function training, cognitive exercises, anxiety/depression coping strategies, and sleep hygiene education.
Delivery: Clinician-guided (no AI/technology involved). Developed via literature review and validated by ICU physicians and nursing experts .
Combines the HPM-Based Optimized Program with Large Language Model (LLM) technology for dynamic personalization:
AI-generated rehabilitation plans: ChatGPT-4 synthesizes patient data (baseline + follow-ups) to create/update monthly plans, reviewed by a multidisciplinary expert team.
Patient-facing LLM tool: "Kimi Smart Assistant" installed for daily health queries under strict safety protocols (all outputs validated by nurses via WeChat).
Phased implementation:
Pre-discharge: LLM training + baseline plan generation. 1/3/6 months: Plan updates + outcome tracking. 3/6 months: Semi-structured interviews on LLM experience. Includes LLM usage guidelines and expert validation safeguards .
Time frame: Measured at baseline (pre-discharge), 1 month, 3 months, and 6 months post-discharge.
Domains: Physical function (6 items), cognitive impairment (6 items), psychological symptoms (6 items).
Scoring: 18 items × 0-3 points = 0-54 total; higher scores = worse symptoms.
Time frame: 1m, 3m, 6m post-discharge.
Scoring: 38 items × 1-3 points = 38-114 total; higher scores = poorer self-management.
Time frame: 3 months and 6 months post-discharge (Group C only).
Qualitative insights from semi-structured interviews based on the Technology Acceptance Model (TAM).
The Affiliated Hospital Of Guizhou Medical University
Other
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View the official ClinicalTrials.gov record (opens in a new tab)This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.
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