The positive impact of an internet-based post-diagnosis disease management model on patients undergoing opportunistic screening for upper gastrointestinal cancer: A randomized controlled trial.

Abstract

This study aimed to establish a management pathway and evaluate the efficacy of an Internet-based post-diagnosis model for upper gastrointestinal cancer patients in improving compliance, self-management, disease control, and reducing readmission, recurrence, and healthcare resource utilization compared to traditional methods. This study assembled a multidisciplinary team to establish a personalized management pathway. A randomized controlled trial was conducted with 166 patients screened and biopsied at Lianyungang Hospital, who were randomly assigned to either an Internet-based intervention group or a traditional management group for a 6-month period. The primary outcome was self-management efficacy measured by the SUPPH scale, with secondary outcomes including follow-up punctuality, readmission rates, and timely pathology results. The intervention group's mean self-management efficacy score (102.08 ± 12.95) was significantly higher than the traditional group's (84.35 ± 12.22). Intragroup comparisons revealed significant improvements in self-management efficacy, follow-up punctuality, and timely acquisition of pathological results within the intervention group (p < 0.001). In contrast, intergroup comparisons showed that the intervention group significantly outperformed the traditional group regarding patient satisfaction and acceptance (p < 0.001 and p = 0.003). The Internet-based post-diagnosis management model improves compliance, self-management, and timely pathology results for high-risk gastrointestinal cancer patients, who report high satisfaction and acceptance. This study supports promoting this model for broader use in post-diagnosis care of high-risk diseases. This model can support healthcare systems in delivering continuous, personalized care for high-risk patients, improving outcomes while reducing healthcare resource strain.