[Application Practice of AI Empowering Post-discharge Specialized Disease Management in Postoperative Rehabilitation of the Lung Cancer Patients Undergoing Surgery].

BACKGROUND

Lung cancer is the leading malignancy in China in terms of both incidence and mortality. With increased health awareness and the widespread use of low-dose computed tomography (CT), early diagnosis rates have been steadily improving. Surgical intervention remains the primary treatment option for early-stage lung cancer, and video-assisted thoracoscopic surgery (VATS) has become a common approach due to its minimal invasiveness and rapid recovery. However, post-discharge recovery remains incomplete, underscoring the importance of postoperative care. Traditional follow-up methods, lack standardization, consume significant medical resources, and increase the burden of the patients. Artificial intelligence (AI)-driven disease management platforms offer a novel solution to optimize postoperative follow-up. This study followed 463 lung cancer surgery patients using an AI-based platform, aiming to identify common postoperative issues, propose solutions, improve quality of life, reduce recurrence-related costs, and promote AI integration in healthcare.

METHODS

Using the AI disease management platform, this study integrated educational videos, collaboration between healthcare teams and AI assistants, daily health logs, health assessment forms, and personalized interventions to monitor postoperative recovery. The postoperative rehabilitation status of the patients was assessed by the Leicester Cough Questionnaire (LCQ-MC). Two independent t-test and one-way ANOVA were used to analyze the causes of postoperative cough in lung cancer.

RESULTS

Most issues occurred within 7 d post-discharge, significantly declined on 14 d post-discharge. Factors such as gender, smoking history, and surgical approaches were found to influence cough recovery. The incidence of cough on 7 d post-discharge in females was higher than that in males (P<0.01), while the incidence of cough on 14 d post-discharge in elderly patients was lower than that in young patients (P=0.03). The AI-based platform effectively addressed cough, pain, and sleep disturbances through phased interventions.

CONCLUSIONS

The AI-based platform significantly enhanced postoperative management efficiency and the self-care capabilities of the patients, particularly in phased cough management. Future integration with wearable devices could enable more precise and personalized postoperative care, further advancing the application of AI technology across multidisciplinary healthcare domains.