An AI-Based Smart Nursing Ward Model for Enhanced Recovery After Thoracic Surgery: A Historical Controlled Trial (Preprint)

BACKGROUND Due to surgical trauma and the impact of the disease, patients undergoing thoracic surgery often experience a series of postoperative symptom burdens, which affect their recovery. Traditional perioperative care has drawbacks. OBJECTIVE To evaluate the impact of an AI-based personalized smart nursing ward management model on postoperative recovery outcomes in patients undergoing thoracic surgery. METHODS According to patients' admission sequence, patients who met the inclusion criteria were divided into a control group (n=303) and an intervention group (n=240). The control group adopted the routine nursing mode of general wards, while the intervention group implemented the AI-based personalized smart nursing ward management model on the basis of the routine nursing provided to the control group. RESULTS Data from all 543 enrolled patients were analyzed. Compared with the control group (n=303) receiving routine care, the intervention group (n=240) had a significantly shorter median hospital stay (9.0 days vs 12.0 days) and chest tube indwelling time (5.0 days vs 7.0 days), as well as lower total hospitalization costs (¥61,032.87 vs ¥72,859.90) (all P < .001). The postoperative pulmonary complication rate was also significantly lower in the intervention group (3.8% vs 12.2%, P < .001). Furthermore, patient satisfaction was higher (98.53% vs 91.28%), and nurses' daily step count was reduced (12,359.52 vs 18,692.74 steps) in the intervention group (both P < .001) CONCLUSIONS The AI-based smart nursing model effectively promotes postoperative recovery and offers an innovative management approach for thoracic surgery.