AI-Enhanced PROMs and Drug-Drug Interaction Screening in Advanced Breast Cancer: Protocol of a Prospective Observational Pilot Study

Abstract

Background
In advanced breast cancer, treatment pathways are becoming increasingly prolonged and complex across different biological subtypes. New systemic and targeted therapies require long-term monitoring and careful personalization according to patient-specific factors that demand increasingly individualized therapeutic pathways, such as comorbidities, lifestyle factors, quality of life (QoL), and polypharmacy risks like drug-drug interactions (DDIs). Current real-world pathways often suffer from delays in personalization, suboptimal adherence, and unmet needs in QoL. Remote monitoring platforms like Cureety TechCare may help clinicians to transform reactive care into proactive management. The integration of patient-reported data with DDI screening may further support personalized monitoring in patients receiving long-term systemic treatments.

Objective
This pilot study proposes a digital workflow framework to optimize diagnostic-therapeutic paths: Cureety for QoL/lifestyle monitoring and DDI integration for comorbidity-adjusted alerts. The primary objective is to assess the feasibility of the procedure by measuring patient compliance with ePRO questionnaires and completeness of DDI screening during the first month of monitoring. Secondary objectives include describing early patient-reported needs, monitoring QoL and lifestyle-related information over time, and exploring whether the integration of comorbidities and concomitant medications may support more individualized patient monitoring.

Methods
This is a prospective, non-interventional observational pilot study. Eligible patients will be adults with advanced breast cancer receiving systemic therapy in routine clinical practice. Patients will be monitored through Cureety TechCare for ePROs, QoL, symptoms, and lifestyle-related information. Cureety TechCare is an AI-driven digital remote monitoring platform specifically designed for oncology patients, enabling remote PRO collection through CTCAE-based questionnaires and four-level clinical classification. In parallel, a DDI module will be used to document concomitant medications and identify potential pharmacological interaction risks in the context of each patient’s clinical history, comorbidities, and reported symptoms. The primary endpoint will be Composite Platform Compliance, defined as the proportion of patients achieving both at least 70% of expected ePRO questionnaire completion and at least 85% completeness of DDI screening for documented concomitant medications after the first month.

Results
As this is a protocol, results are not yet available The study will estimate the feasibility of the integrated workflow and describe early monitoring outputs during the first 30 days of treatment, including first-month tolerance as an exploratory integrated digital health status indicator. Patients will be classified as “Good Health Status” (GHS) or “Poor Health Status” (PHS) based on the proportion of days with green/yellow Cureety status and low-risk DDI profile versus orange/red Cureety status and/or higher-risk DDI profile.

Discussion
This protocol addresses a key methodological challenge in integrating patient-reported data with pharmacological risk assessment in real-world oncology care. Its methodological relevance lies in the definition of composite feasibility endpoints, the management of longitudinal patient-reported data, and the interpretation of digital alerts generated by the combination of PROMs and pharmacological risk information.

Conclusion
This pilot protocol may generate preliminary evidence on the feasibility of AI-supported integration of PROMs and DDI screening in advanced breast cancer care. The study may inform the design of future larger studies evaluating the implementation of patient-centered digital monitoring models in routine oncology practice.