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Implementation and impact of an electronic patient reported outcomes system in a phase II multi-site adaptive platform clinical trial for early-stage breast cancer. | LitMetric
Objectives: We describe the development and implementation of a system for monitoring patient-reported adverse events and quality of life using electronic Patient Reported Outcome (ePRO) instruments in the I-SPY2 Trial, a phase II clinical trial for locally advanced breast cancer. We describe the administration of technological, workflow, and behavior change interventions and their associated impact on questionnaire completion.
Materials And Methods: Using the OpenClinica electronic data capture system, we developed rules-based logic to build automated ePRO surveys, customized to the I-SPY2 treatment schedule. We piloted ePROs at the University of California, San Francisco (UCSF) to optimize workflow in the context of trial treatment scenarios and staggered rollout of the ePRO system to 26 sites to ensure effective implementation of the technology.
Results: Increasing ePRO completion requires workflow solutions and research staff engagement. Over two years, we increased baseline survey completion from 25% to 80%. The majority of patients completed between 30% and 75% of the questionnaires they received, with no statistically significant variation in survey completion by age, race or ethnicity. Patients who completed the screening timepoint questionnaire were significantly more likely to complete more of the surveys they received at later timepoints (mean completion of 74.1% vs 35.5%, P < .0001). Baseline PROMIS social functioning and grade 2 or more PRO-CTCAE interference of Abdominal Pain, Decreased Appetite, Dizziness and Shortness of Breath was associated with lower survey completion rates.
Discussion And Conclusion: By implementing ePROs, we have the potential to increase efficiency and accuracy of patient-reported clinical trial data collection, while improving quality of care, patient safety, and health outcomes. Our method is accessible across demographics and facilitates an ease of data collection and sharing across nationwide sites. We identify predictors of decreased completion that can optimize resource allocation by better targeting efforts such as in-person outreach, staff engagement, a robust technical workflow, and increased monitoring to improve overall completion rates.
