An Evidence-based Evaluation of AI Voice Follow-up System's Impact on Postoperative Care and Prognosis in Hepatocellular Carcinoma Patients

Article type
Authors
HUANG D1, Xu N2, Tao X1, Lu X, Qiao S, LIU Y
1Chinese Academy of Medical Sciences & Peking Union Medical College, BEIJING, CHINA, CHINA
2The First Affiliated Hospital of the University of Science and Technology of China (Anhui Provincial Hospital)
Abstract
Background: Hepatocellular carcinoma (HCC) poses a significant global health challenge, particularly in China due to the high prevalence of hepatitis B. The 5-year recurrence rate in HCC patients after hepatectomy ranges from 15%-70%, with early recurrence linked to poor prognosis and low survival. Traditional manual follow-up methods face challenges like inefficiency, limited coverage, and lack of personalisation. To address this, an AI-powered voice follow-up system and app were launched at Anhui Provincial Hospital in 2021, having already served 140,000 patients. A prospective study is underway for postoperative HCC patients to generate empirical evidence.

Objectives: This study rigorously applies evidence-based medicine to evaluate how the AI voice follow-up system improves follow-up efficiency, patient adherence, and prognosis.

Methods: In a randomised matched design, recently discharged postoperative HCC patients were assigned to either traditional manual or AI voice follow-up groups. Response rates served as a key metric to differentiate patient engagement and adherence. International standard scales—MDASI-PeriOp-Hep for symptom severity and EQ-5D-5L for quality of life—were utilised to collect and compare patient data across both groups regarding adverse symptoms, quality of life, recurrence, and survival, with a deep dive into influencing factors.

Results: Ongoing data collection for the prospective follow-up will culminate in reporting the 6-month early recurrence rate for 1,000 postoperative HCC patients at a September conference, providing more evidence for the AI follow-up system.

Conclusions: This research constructs an evidence-based foundation for the AI voice follow-up system, aiming to demonstrate its effectiveness in enhancing follow-up efficiency, optimising resource use, and improving patient prognosis.