Application of Large Language Models Techniques to Post-ICU Syndrome Management in Critically Ill Patients: A Fully Longitudinal Mixed Study

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.