Vivek Rudrapatna is a physician and a clinical data scientist. His research group works on developing new methods for analyzing electronic health records and other large datasets to uncover real-world evidence on treatment effects. Vivek is a practicing gastroenterologist and specializes in the treatment of patients with Inflammatory Bowel Disease (IBD). As a clinician, a researcher, and a patient, he is interested in the study of precision medicine as it pertains to this patient population. His group is working on the development of algorithms to predict which patients may be at greater risk for developing the condition and to optimize treatment selection for IBD patients. He hopes that this work may eventually lead to a future where we can prevent IBD altogether. Vivek received his AB from Harvard (2006), his MD/PhD from Mount Sinai (2014), internal medicine residency at Baylor College of Medicine (2016), and gastroenterology fellowship at UCSF (2020).
Dr. Sim is a leader in the policy and technology of large-scale health data sharing for clinical care and research. She is co-founder of Vivli, a global data sharing platform for participant-level clinical trials data and co-founder of Open mHealth, a non-profit organization whose open mobile health data standards are now the IEEE 1752 global standard. She co-developed CommonHealth, the Android equivalent of Apple Health for bringing USCDI EHR data to smartphones, and is integrating digital technologies into primary care at UCSF. Dr. Sim is Co-Director of the new UCSF UC Berkeley Joint Program in Computational Precision Health. She earned her MD and PhD from Stanford University, and is a recipient of the United States Presidential Early Career Award for Scientists and Engineers (PECASE), a Fellow of the American College of Medical Informatics, and a member of the American Society for Clinical Investigation. She is a practicing primary care physician.
Dr. Hoffman is an expert in EHR data at both the source system and aggregate data. He led the design and development of the first commercial molecular diagnostics solution integrated with an EHR. His research focuses on using massive aggregate de-identified EHR data resources from more than 100 non-affiliated organizations to compare "real world" clinical practice with guidelines and other ideals. For example, his team recently demonstrated that A1c tests are frequently ordered for sickle cell patients. They have also demonstrated over-ordering of opioids for young adults evaluated in the ED for headache. He is also an expert in methods to identify and characterize EHR implementation impacts on "real world data".
Trekking in the Jungle of "Real World Data"
I will describe aspects of working with real world EHR data and will share 3 examples from my research.
Julian Hong is a radiation oncologist and informaticist. He specializes in the treatment of genitourinary malignancies such as prostate cancer. His research program focuses on the development and implementation of computational tools to provide personalized, precision cancer care for patients. His group combines clinical domain knowledge with computational expertise to generate insights from real world data, develop actionable artificial intelligence-based tools, and evaluate the benefit of these advances in patient care. He led one of the first randomized controlled studies of machine learning, successfully implementing machine learning-directed clinical evaluations to reduce emergency visits and hospitalizations during cancer radiotherapy. Dr. Hong’s research has been published in high-impact, peer-reviewed journals such as the Journal of Clinical Oncology and JAMA Oncology. He has received awards including the Radiation Oncology Institute Publication Award, Bio-IT World Innovative Practices Award, and Symposium on Artificial Intelligence for Learning Health Systems New England Journal of Medicine Travel Award.