Precision monitoring: Standardizing patient-reported signals to improve clinical decision-making using real-world data

Background:

Advances in cancer treatment have increased the complexity of toxicity monitoring while shifting much of patient management beyond traditional clinic visits. Although remote patient monitoring and electronic patient-reported outcomes (ePROs) improve symptom capture, their clinical value ultimately depends on the ability to translate patient-reported information into timely, standardized, and actionable clinical insights. Large-scale real-world implementation has demonstrated the feasibility of standardized patient monitoring across diverse cancer populations and treatment settings, while prospective randomized evidence demonstrates that integrating digitally supported symptom monitoring into routine oncology care can improve operational efficiency, facilitate earlier recognition of treatment-related toxicities, and optimize patient management. These findings support the evolution of precision monitoring frameworks that transform patient-reported signals into clinically actionable information, enabling clinicians to safely deliver increasingly complex oncology therapies.

Objectives:

• Describe how standardized patient signal frameworks translate patient-reported information into clinically actionable insights for oncology care.
• Evaluate the impact of implementing a standardized patient signal framework on clinical workflow, symptom prioritization, and patient monitoring using realworld and prospective clinical evidence.
• Identify key principles of precision monitoring that support earlier toxicity recognition and the delivery of increasingly complex oncology therapies.

Purpose:

To evaluate the role of a standardized patient signal framework in supporting precision monitoring through real-world implementation and prospective clinical evidence, enabling safer and more efficient delivery of advanced cancer therapies.

Methods:

Data from two complementary evaluations of the Cureety digital monitoring platform were reviewed. ARGOS was a retrospective real-world analysis conducted across French oncology centers from October 2019 through March 2025, evaluating implementation of treatment-specific digital remote monitoring using ePROs and algorithm-based clinical risk stratification in routine oncology practice. OPTIMACURE was a prospective, randomized, multicenter trial conducted across three French oncology centers in 2024. Adults initiating injectable anticancer therapy were randomized 2:1 to either standard remote monitoring with structured pretreatment telephone calls (OPTIMA-like care) or the Cureety digital remote monitoring platform, which incorporated patient-reported outcomes for pretreatment assessment and intercycle toxicity monitoring. Outcomes included operational efficiency, toxicity detection, healthcare utilization, and patient management over a 2-month follow-up period.

Results:

In ARGOS, between October 2019 and March 2025, 19,303 patients completed 409,868 remote patient-reported questionnaires across French oncology centers. The platform demonstrated successful implementation across diverse cancer types, treatment modalities, and healthcare settings, with sustained growth following national reimbursement. High patient and healthcare professional satisfaction supported the feasibility and scalability of digital remote monitoring, while algorithm-based clinical risk stratification enabled systematic prioritization of patients requiring timely clinical intervention. In OPTIMACURE, among 192 randomized patients, Cureety reduced the total number of outgoing telephone calls by 36% (P<0.001), including an 88% reduction in pretreatment calls required to prepare outpatient visits (3.35 versus 0.41 calls per patient; P<0.001). Conversely, intercycle toxicity-related calls increased (0.10 versus 1.72 calls per patient; P<0.001), reflecting earlier identification and proactive management of treatment-related toxicities. These workflow improvements were accompanied by significantly earlier detection of grade 2–4 adverse events (median, 4 versus 21 days; hazard ratio [HR], 3.23; 95% confidence interval [CI], 2.25–4.63; P<0.001), supporting both operational efficiency and enhanced toxicity management.

Conclusion:

Real-world implementation and prospective clinical evaluation support the integration of digital remote monitoring into routine oncology practice. By improving operational efficiency and enabling earlier toxicity detection and management, standardized digital monitoring frameworks may support clinicians in the safe delivery of increasingly complex cancer therapies.

Funding: Not applicable.