Capturing the patient’s voice to inform advanced clinical decision support for guideline-based cancer symptom management.

510 Background: Electronic patient reported outcomes (ePROs) have become routine in cancer care; however, ePROs alone do not provide sufficient information to enable informed symptom management. Informed symptom management requires an awareness of patient context to reduce inappropriate or erroneous advice. In this project we asked: how can information be collected directly from patients to provide clinicians with detailed, contextually-informed, patient-specific, evidence-based recommendations to manage distressing symptoms, especially among patients with complex comorbidities. Methods: The literature states that text messages are the most effective way to engage patients in clinical data collection. We texted patients a link that led them to a Web-based questionnaire that asked them about their common cancer symptoms. Prototypes were tested with 9 cancer patients through structured interviews and think aloud sessions from which themes were identified. Feedback from these interviews was used to develop a patient data collection questionnaire that we iteratively refined through think aloud sessions with 9 cancer patients to achieve universal patient acceptance. We also engaged 28 oncology clinicians in semi-structured interviews to assess the clinical impact of the additional contextual information on the symptom recommendations they received. Results: Formative evaluation identified four themes: 1) make instructions brief, 2) use intuitive navigation buttons with labels, 3) focus on multiple-choice style questions, and 4) extract data from the medical record for patient verification instead of free text entry by patients (e.g., medications). Developers incorporated “more detailed” questions into the patient data collection questionnaire; additional questions were presented if the patient indicated moderate or greater severity of a given symptom. These detailed questions focused on characteristics of the symptoms (e.g. neuropathic or somatic pain), remedies a patient had already initiated for self treatment (e.g., laxatives for constipation), and comorbid conditions that could impact the safety of recommendations (e.g., fall risk). Clinicians indicated that the additional data collected from patients increased the value of the tailored recommendations for enhanced symptom management. Conclusions: Additional data collection from patients using standard text messaging enables clinical decision support algorithms to provide more individually tailored cancer symptom management recommendations. This study is limited in that the design enhancements have been tested with a small number of end-users in a laboratory setting. Acknowledgements: This project was 100% funded using Cancer Moonshot funding through Phase I and II SBIR contracts from the National Cancer Institute, Contract #75N91018C00022 and #75N91020C00019, respectively.