Precision ICU Care: Evaluating an AI-Driven Nursing Decision Support System for Delirium Prevention

Delirium affects up to 83% of mechanically ventilated patients in the Intensive Care Unit (ICU), often leading to longer hospital stays and long-term memory or cognitive problems. While standard care protocols (such as the ABCDEF bundle) exist, they are often difficult to implement fully due to their complexity and the heavy workload on nursing staff. To address these challenges, this study introduces a 'Precision Nursing' approach by integrating Artificial Intelligence (AI) and Virtual Reality (VR). We will implement an AI-driven system to assist nurses in making personalized care decisions more efficiently. Additionally, interactive VR technology will be used to stimulate patients' cognitive function and encourage early mobility. Our goal is to reduce the clinical burden on healthcare providers while significantly improving recovery outcomes for ICU patients.

Inclusion Criteria: * Patients aged 18 years * High risk of developing delirium are defined as PRE-DELIRIC (PREdiction of DELIRium in ICu patients) with a score of ≥40% * Sedation assessment form (Richmond Agitation-Sedation). Scale, RASS) is assessed as between +1 and -1 * Patient is clearly aware and able to communicate with Chinese and Taiwanese customers. Exclusion Criteria: * Those who have developed delirium before joining the study (assessed by the Intensive Care Delirium Screening Checklist (ICDSC) \>4 points) * Expected to stay in the ICU for less than 24 hours * APACHE II score \> 25 (meaning \> 50% mortality) * Delirium cannot be evaluated, such as those with severe acute brain injury, audio-visual impairment, speech problems, psychotic disorders, aphasia, or in the entire ICU During this period, patient remained comatose.