Project Summary: Chronic pain is a major healthcare problem with an annual cost of above $600 billion. The quality of data available for treatments of chronic pain is not optimal. Generalizability of explanatory randomized controlled trial data is problematic as these trials exclude up to 90% of patients: leaving out real-world patients with serious medical and psychological comorbidities. Pragmatic trials embedded in patient care compare effectiveness of currently used treatments in real-world application leading to findings that generalize to broader range of patients. The changes in clinical practice and workflow necessary to integrate this type of research within patient care present pragmatic challenges. In this research, my overall objective is to overcome these challenges using an open-source learning health care system – CHOIR – developed by my mentor at the Stanford Pain Management Center. CHOIR is currently used to track patients’ clinical trajectory and treatment response across multiple academic sites resulting in over 25 publications characterizing chronic pain. Through our pilot studies, we have already developed a point-of-care randomization for CHOIR that facilitates integration of research and patient care by allowing the physicians to randomize patients during clinic visits. We have already demonstrated feasibility of the randomization and data collection platform in two ongoing pilot pragmatic clinical trials. We are proposing to better integrate pragmatic research within our clinical practice through conducting a randomized comparative effectiveness trial in 450 patients with chronic pain comparing effectiveness of anti- convulsants and anti-depressants (two most commonly used classes of medications for treatment of chronic pain). We will use the data available in CHOIR as well as the real-world data generated from this clinical trial to build, validate and test a model to predict what clinical characteristics can predict response to either of these classes of medications. The proposed study is the first step to use flexible point-of-care randomization to compare effectiveness of different treatments in different subgroups of patients whenever equipoise exists. Our prediction model will guide decision making process of clinicians choosing between these medications based on clinical characteristics of individual patients. Dr. Salmasi is a physician-scientist with clinical training in Pain Medicine as well as academic training in Clinical Research and Epidemiology. The detailed career development and research plan presented in this application will provide the required resources and mentorship for him to become an independent R01-funded expert in two domains critical to his long-term career goals: (1) advanced clinical trial design and conduct; and (2) advanced statistics and machine learning.