326 Background: In the US, about 1 in 6 men is diagnosed with prostate cancer (PCa). Over 90% of them are localized PCa patients, which have the most controversial treatment decisions with few certainties about outcomes such as survival years and quality of life (QoL). Shared decision making is emerging, where patients need to make trade-offs between longevity and QoL based on personal preferences and treatment outcomes. Methods: By collaborating with leading PCa centers, medical psychologists and health economists, we investigate, iteratively design, and eventually test a decision support solution that could enhance treatment decision making and patient-clinician interaction. We interviewed 7 PCa clinicians and 13 patients and survivors, and observed 4 patient-clinician consultations. Results: Key insights from the user research: 1) existing decision aids are very generic and not personalized to the patient’s preferences, are not integrated in the clinical workflow, and involve a complex user experience; 2) there is a significant amount of unwarranted variation in PCa treatment (i.e. not preference sensitive, or patients lack the confidence to choose non-aggressive options that could lead to similar or better outcomes, e.g., active surveillance; and 3) clinicians need to understand what the patient’s preferences are (verbally discussed only), which consumes significant time in consultations. These insights led to developing a shared decision support system based on algorithms that use quantitative computations of quality adjusted life years (QALYs) and patient-friendly interactions. This system can be integrated in the clinical workflow, allow patients to make better informed decisions, and increase their confidence to choose the best treatment option according to their own preferences. We aim to increase patients’ involvement and satisfaction, enhancing consultation efficiency, and reducing unwarranted variation. Conclusions: Our ongoing research, motivated by user insights, focuses on developing shared decision support technology that is personalized to the patient’s profile and sensitive to their preferences. We will deploy validation studies at clinical sites and evaluate the system across the predefined outcome measures.
Implementing CommonGround in a community mental health center: Lessons in a computerized decision support system.
