Inclusion of @sk|ChatBot as part of an activity on Chronic Lymphocytic Leukemia (CLL) Enhances Learner Engagement and Provides Insights Into Clinician Workflow Challenges

Projects In Knowledge had identified a clinical practice gap where evolving CLL therapeutic paradigms and algorithms had created uncertainties for clinicians in how to identify treatment sequences that optimally align with individual patient profiles. In the context of new targeted treatment options for upfront CLL management, clinicians had questions regarding choice of first-line therapy, the best follow-on therapy, and how to manage patients who fail to respond or relapse. Since little data exist regarding ideal treatment selection post-novel therapies, clinicians are without the information needed to select appropriate strategies. Variability in practice patterns and guideline-discordant treatment choices are symptomatic of these challenges.