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Multimodal ePRO Implementation Across Systemic Therapy and Radiotherapy: Real-World Use of Specified Symptom Items and Free-Text Reporting
ABSTRACT
Background:
Electronic patient-reported outcomes are increasingly used in oncology care, but real-world multimodal implementation across systemic therapy and radiotherapy remains less well characterized.
Objective:
This study investigates enterprise-wide reporting within a cloud-based ePRO system among patients receiving systemic therapy (ST), radiation therapy (RT), or combined treatment (ST+RT), using a platform that integrates logic-based, treatment-specific questionnaires that include specified symptoms and free-text options.
Methods:
This retrospective, descriptive analysis included 1,445 patients who submitted at least one symptom report via ePRO between 2015 and 2021. Patients were stratified by cancer type (breast, prostate, other) and treatment modality (ST, RT, ST+RT). Questionnaires were administered based on expected treatment-related symptom onset; symptoms could also be reported ad hoc.
Results:
A total of 37,968 symptoms were reported: 83.5% via specified symptom items and 16.5% via standalone free-text reporting. Patients receiving ST+RT reported the highest volume. Breast cancer patients submitted 26,948 symptoms (71.0%), prostate cancer patients 1,246 (3.3%), and other cancers 9,774 (25.7%). Fatigue was the most frequently reported symptom. Free-text reporting was most common among breast cancer patients (81.1%), although all groups used this feature. Nearly three quarters (74.5%) of free-text entries duplicated symptoms already listed in modules, most frequently pain.
Conclusions:
These findings demonstrate the feasibility of a unified, flexible, treatment-specific ePRO platform across treatment modalities, with symptom patterns aligning with prior literature. The higher-than-expected use of free-text reporting suggests that some patients value a hybrid approach combining structured symptom capture with patient-driven narrative input, thus representing a practical and adaptable model for capturing treatment-related symptoms within routine oncology care.
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Copyright
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