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.
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.
Study Type
INTERVENTIONAL
Allocation
NA
Purpose
OTHER
Masking
NONE
Enrollment
236
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.
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.
Time frame: At 12 weeks after enrollment.
adherance
User adherence with the mHealth intervention will be measured by following approaches 1. User log-in data retrieved from app or website： * Number of log-ins to app * Length of time spent in app 2. Manual user data entry in app - The frequency at which users manually enter data in the app, such as blood glucose level, weight, date and time when medicine was taken, or blood pressure values.
Time frame: From enrollment to the end of treatment at 52 weeks, patient adherence will be assessed at each visit.
Quality of life
Quality of life (QoL) was captured by survey with the 27-Item Functional Assessment of Cancer Therapy-Colorectal (FACT-C), which consists of 27 items with physical component summary and mental component summary scores. The score range is 0 to 136, with higher scores indicating better quality of life.
Time frame: From enrollment to the end of treatment at 52 weeks
Average unplanned hospitalization days
Unplanned Hospitalization Days" refers to the duration of hospital stays that occur outside of a patient's scheduled follow-up plan. These unplanned admissions indicate instances where a patient's condition requires urgent or unexpected inpatient care, reflecting potential health complications or acute needs beyond the regular follow-up schedule. Monitoring unplanned hospitalization days can provide valuable insights into patient health outcomes, care management, and the effectiveness of follow-up programs.
Time frame: The 3rd, 6th, 9th, and 12th months after enrollment
Potentially preventable emergency department visits
"Potentially Preventable Emergency Visits" are defined as emergency visits where the primary diagnoses include conditions such as anemia, nausea, fever, dehydration, neutropenia, diarrhea, pain, pneumonia, sepsis, or vomiting. These visits represent cases where timely outpatient care, effective management, or preventive measures might have reduced the need for emergency intervention. Tracking potentially preventable emergency visits helps healthcare providers identify areas for improvement in outpatient management and preventive care, ultimately aiming to reduce avoidable strain on emergency services.
Time frame: The 3rd, 6th, 9th, and 12th months after enrollment
Number and distribution of alerts
"Number and Distribution of Alerts" involves collecting patient-reported symptoms, medication information, and health data such as tumor markers and imaging results through symptom questionnaires and patient self-reports. When risks or abnormalities are detected, alerts are automatically triggered. Alerts are categorized by urgency and severity into yellow, orange, and red levels. Descriptive statistical methods are used to analyze alert occurrences, with frequencies and percentages summarizing the number and reasons for each alert type. The average number of alerts triggered per patient, along with the standard deviation, is calculated. Correlation or regression analyses are applied to explore relationships between the number of alerts and other variables.
Time frame: The 3rd, 6th, 9th, and 12th months after enrollment
Number and distribution of proactive reports
Time frame: The 3rd, 6th, 9th, and 12th months after enrollment
Patient satisfaction
The FACIT-TS-PS Questionnaire (Functional Assessment of Chronic Illness Therapy - Treatment Satisfaction - Patient Satisfaction) is a tool designed to evaluate patients' satisfaction with their overall treatment and the use of specific applications. It assesses various aspects of treatment satisfaction, including the effectiveness, convenience, and overall experience of therapy, as well as the patient's satisfaction with digital applications used as part of their care. The insights gained from this questionnaire help healthcare providers improve treatment approaches and enhance the patient-centered design of healthcare applications.
Time frame: At the end of patient follow-up
System usability of the digital health quality management system
The System Usability Scale (SUS) is a standardized questionnaire used to assess the usability of a system. It consists of 10 items with a five-point Likert scale, allowing users to express their level of agreement or disagreement with statements about the system's functionality and ease of use. The SUS provides a quick and reliable measure of overall usability, enabling evaluators to identify potential improvements and gain insights into users' experiences.
Time frame: The 3rd and 12th months after enrollment
Nutritional status
The nutritional status of the selected patients was evaluated using the Patient-Generated Subjective Global Assessment (PG-SGA) questionnaire. The PG-SGA questionnaire consists of two parts: the PG section, which is completed by the patient through self-assessment and includes information on recent weight changes, recent dietary intake, symptoms and signs related to diet, activity, and function; and the SGA section, which is filled out by the investigator after assessment, including the patient's age at onset of disease, metabolic stress status, and physical examination findings. The total score from both sections constitutes the PG-SGA score, with higher scores indicating worse nutritional status. A PG-SGA score of 4 or above is indicative of malnutrition
Time frame: From enrollment to the end of treatment at 52 weeks
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