Remote Patient Monitoring in Oncology: Beyond the Digital Divide
Recently published in eClinicalMedicine (The Lancet Discovery Science), a multicentric study led by Gustave Roussy, Caen University Hospital, and the François Baclesse Center demonstrates the effectiveness of Remote Patient Monitoring (RPM) for patients identified as digitally illiterate.
April 29, 2026
2 minutes
Challenging Paradigms in Digital Healthcare Access
While the adoption of RPM tools is accelerating, the exclusion of patients less familiar with digital technologies has remained a major concern for clinicians. This new multicentre study (analysing over 4,700 patients) challenges this assumption by comparing healthcare professionals' perceptions with real-world Patient-Reported Outcomes (PROs).
Key Findings: Clinical Effectiveness in Real-World Practice
The study’s data reveal that digital illiteracy should no longer be considered a barrier to RPM programs:
- High adherence: Patients facing digital exclusion show an adherence rate of 81.3%, nearly equivalent to tech-savvy profiles.
- Clinical impact: An identical reduction in symptom severity (PRO-CTCAE score) was observed in both groups. Furthermore, patients with the lowest digital literacy initially presented with more severe symptoms, making them the primary beneficiaries of the service.
- Perception bias: The study highlights a bias among healthcare professionals, who tend to underestimate the ability of older or more vulnerable patients to engage in RPM tools.
- Scalability: These results, gathered from 33 centers across France and Belgium, support the feasibility of a large-scale rollout of Remote Patient Monitoring.
Towards Evidence-Based Digital Oncology
These findings support the relevance of integrating Remote Patient Monitoring into standard care pathways. They suggest that RPM, far from widening inequalities, can serve as a lever for healthcare equity to improve the management of the most vulnerable patients, regardless of their initial technological proficiency.
👉 Access the full data and detailed analysis here:


