COLORECTUM+ Digital System for Postoperative Quality Improvement in Colorectal Cancer

NCT06685497 | Not Yet Recruiting DMC

• 1 other identifier: LY2024-208-B
• Study Type: interventional
• Target: 236
• Locations: 0 countries
• Sites: N/A

Brief Summary

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.

Conditions

Colorectal Carcinoma

Keywords

colorectal carcinoma; follow-up; Ehealth; adherence

Outcome Measures

Primary Outcomes (1)

• three-month follow-up rate

  Our mobile application aims to monitor the three-month follow-up rate for patients, assessing whether they adhere to the follow-up schedule as outlined，providing a streamlined approach to track post-discharge appointments. By utilizing real-time notifications and easy scheduling features, the app encourages patients to attend their follow-up visits, ensuring continuity of care. The application records and analyzes data on scheduled and completed appointments within three months post-discharge, offering insights into adherence rates and identifying factors impacting follow-up compliance. This data-driven approach helps healthcare providers improve patient outcomes and optimize follow-up strategies.

  At 12 weeks after enrollment.

Secondary Outcomes (9)

• adherance

  From enrollment to the end of treatment at 52 weeks, patient adherence will be assessed at each visit.

• Quality of life

  From enrollment to the end of treatment at 52 weeks

• Average unplanned hospitalization days

  The 3rd, 6th, 9th, and 12th months after enrollment

• Potentially preventable emergency department visits

  The 3rd, 6th, 9th, and 12th months after enrollment

• Number and distribution of alerts

  The 3rd, 6th, 9th, and 12th months after enrollment

Study Arms (1)

experimental group

EXPERIMENTAL

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.

Device: Mobile application follow-up

Interventions

Mobile application follow-up DEVICE

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.

experimental group

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• 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.

You may not qualify if:

• 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.

Sponsors & Collaborators

• RenJi Hospital: lead
• Shanghai Shen Kang Hospital Development Center: collaborator

MeSH Terms

Conditions

Colorectal Neoplasms

Condition Hierarchy (Ancestors)

Intestinal Neoplasms; Gastrointestinal Neoplasms; Digestive System Neoplasms; Neoplasms by Site; Neoplasms; Digestive System Diseases; Gastrointestinal Diseases; Colonic Diseases; Intestinal Diseases; Rectal Diseases

Study Officials

• Jun Qin

  RenJi Hospital

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Xiaomiao Wang

CONTACT

+8617843321256; wangxm0109@163.com

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: NA
• Masking: NONE
• Purpose: OTHER
• Intervention Model: SINGLE GROUP
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: October 11, 2024
• First Posted: November 12, 2024
• Study Start: December 1, 2024
• Primary Completion: December 1, 2025
• Study Completion: May 31, 2026
• Last Updated: November 12, 2024

Record last verified: 2024-11