COLORECTUM+ Digital System for Postoperative Quality Improvement in Colorectal Cancer

Shanghai Jiao Tong University

Status: Not yet enrolling

Conditions: Colorectal Carcinoma

Treatments: Device: Mobile application follow-up

Study type: Interventional

Funder types: Other

Identifiers

NCT06685497

LY2024-208-B

Details and patient eligibility

About

This is a single-center, prospective, interventional study. A total of 236 colorectal cancer patients who underwent surgery will be enrolled and followed for 52 weeks. The digital healthcare quality management system, based on the COLORECTUM+ model, will be used for post-treatment quality evaluation and continuous improvement.

Patients will be managed using an Internet+ post-treatment healthcare management platform. The platform integrates AI technology for real-time symptom analysis and alerts. Patients will report symptoms and health data through the platform, which will generate alerts based on symptom severity to guide appropriate interventions. Follow-up assessments will include patient adherence, satisfaction, quality of life, and healthcare utilization.

The study expects to demonstrate that the digital healthcare quality management system improves follow-up rates, enhances patient adherence, reduces unplanned hospital visits, and increases overall patient satisfaction. The findings aim to provide evidence for the implementation of digital management systems in colorectal cancer post-treatment care, potentially leading to improved long-term outcomes for patients.

Full description

The aim of this study is to evaluate the adherence of postoperative colorectal cancer patients using a digital follow-up platform. The primary endpoint is follow-up rate at 3 months after surgery. The secondary endpoints are: follow-up rate at 6, 9, and 12 months, adherence during 12 months, medication adherence (MMS-4), the number and reasons for alerts triggered by patients, the frequency and reasons for patient-initiated report, quality of life (FACT-C), patient satisfaction (FACIT-TS-PS), the system's usability (SUS), the monitoring rates of imaging exams, colonoscopies, and CEA markers at 3, 6, 9, and 12 months will be analyzed. Differences in clinical outcomes: progression-free survival, overall survival, adverse events, the incidence of complications, hospital admissions (unplanned hospital visit rates, average unplanned hospital stay duration, and potentially preventable emergency visits) are additional outcomes.

Enrollment: 236 estimated patients

Sex: All

Ages: 18+ years old

Volunteers: No Healthy Volunteers

Inclusion criteria

• Over 18 years old.
• Pathologically confirmed colorectal cancer patients.
• Underwent surgery at the Colorectal Cancer Diagnosis and Treatment Center, Gastrointestinal Surgery, Renji Hospital, Shanghai Jiao Tong University School of Medicine.
• Signed informed consent form.

Exclusion criteria

• Unable to access electronic devices that can connect to the internet or mobile communications.
• Uncontrolled psychiatric illness.
• The ECOG score is greater than or equal to 3.
• Deemed unsuitable for participation by the investigator.

Trial design

Primary purpose: Other

Allocation: N/A

Interventional model: Single Group Assignment

Masking: None (Open label)

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236 participants in 1 patient group

experimental group

Experimental group

Description:

Colorectal cancer patients enrolled in the 'Internet Plus' post-treatment management platform use the digital medical quality management system based on the 'COLORECTUM+' model for quality evaluation and continuous improvement. The platform integrates AI, using natural language processing and machine learning to analyze patient-reported symptoms, automatically assess severity, and generate alerts. Alerts are classified as yellow, orange, or red. Yellow indicates mild issues with self-care recommendations; consecutive yellow alerts prompt doctor contact within 24 hours. Orange indicates moderate severity, requiring doctor intervention within 24 hours. Red alerts signify serious symptoms or high-risk medication errors, prompting immediate notification of the doctor and emergency team. The system monitors symptom changes and updates alerts to support treatment optimization.

Treatment:

Device: Mobile application follow-up