Electronic patient-reported data capture as the foundation of a learning health care system

Abstract

6522 Background: In a “learning healthcare system” clinical decisions are supported by accurate information delivered at point of care; information gathered today iteratively informs future care and research. Methods: Customized software on wireless tablet personal computers presented a review of systems (ROS) instrument, validated research surveys (e.g., quality of life [QOL]), and a satisfaction survey, tailored by user. The system was piloted in the Duke Cancer Clinics and affiliated hospitals. We previously demonstrated equivalence of electronic and paper survey data. We conducted a series of studies using similar procedures to evaluate feasibility, acceptability, and utility. Results: First, we assessed the ability to collect ROS data at point of care to inform the clinic visit for participating breast (n = 65), gastrointestinal (n = 113), and lung (n = 97) cancer patients. Duke physicians reported that the system's clinical reports informed care and increased dictation efficiency. Second, we assessed patient satisfaction in the breast cancer cohort. Participants found the computers easy to read (94%), navigate (99%), and use (98%); the system helped 74% remember forgotten concerns to report to their clinician. Third, we assessed whether these data could contribute to current research. If the patient was on another clinical trial, PRO data (e.g., pain, QOL) were delivered to the investigator for research purposes in real time; data governance rules provided assurance to investigators. Fourth, we identified whether the PRO data could inform future research directions. Symptoms monitored longitudinally in aggregate uncovered unmet needs. Sexual distress was an underserved concern; intervention studies were initiated. Warehoused PRO data were integrated with clinical trials, genomic, biomarker, radiology, and administrative datasets for analyses. The approach has been scaled to 4 clinics and 3 hospitals. Conclusions: An integrated, real-time, electronic data capture system that interdigitates PROs with clinical and other data allows creation of a learning oncology environment that continuously improves care and research. Advantages include: patient-centeredness, description of the PRO phenotype, interoperability, and interface with caBIG infrastructure. No significant financial relationships to disclose.