Session Synopsis: Individualized health, or InHealth, is a scientific approach to health promotion and disease management by combining clinical, genetic, lifestyle, and other data sources with sophisticated data analytics to improve health decisions in real time. This session will touch upon this comprehensive initiative.
Session Chair Profile
M.D., Donald S. Coffey Professor of Urology; Director, Brady Urological Institute, Johns Hopkins Medicine; Johns Hopkins University, School of Medicine
Dr. Pienta is a Professor of Urology, Oncology, and Pharmacology and Molecular Sciences and a two-time American Cancer Society Clinical Research Professor Award recipient. Dr. Pienta is co-Director of the Johns Hopkins University inHealth Signature Initiative, a trans-University, cross-disciplinary effort to coordinate and apply the intelligent use of population health data for individual patients. Clinically, theinHealth Initiative is being realized through the formation of Precision Medicine Centers of Excellence. The central tenant of the Precision Medicine Centers of Excellence is to create and foster a dynamic relationship with the patient as an equal partner in their care – for life. The patient partners with their disease – specific PMCOE for their care and participates in research by donating biospecimens, contributing outcomes data, and participating in clinical trials. Each patient is provided with optimal, non-provider biased, individualized clinical care based upon current scientific understanding. The PMCOEs provide the iterative data that allows for further sub-setting of disease states and refinement of care – creating the infrastructure and basis for a Learning Health System.
Ph.D., Professor of Biostatistics, Johns Hopkins University
Scott L. Zeger is co-Director of Hopkins inHealth, the Johns Hopkins precision medicine partnership of the University, Health System, and Applied Physics Laboratory. He conducts statistical research on Bayesian hierarchical models for better estimating an individual’s state, trajectory, or likely treatment benefits from clinical cohort data. Professor Zeger is a member of the Springer-Verlag editorial board for statistics and was the founding co-editor of the Oxford University Press journal Biostatistics. His work has been recognized with several awards including most recently an honorary doctorate from England’s Lancaster University and the 2015 Karl Pearson Prize from the International Statistical Institute with long-time colleague Dr. Kung-Yee Liang. Dr. Zeger is a Golden Apple Awardee for excellence in teaching.
Hopkins inHealth: A Model for Individualized Healthcare
Individualized and population health are two sides of a coin because better decisions for individuals are made when informed by appropriate analyses of data on a cohort of similar people. Bayesian hierarchical models can improve clinical judgements about a patient’s health state, trajectory, or likely benefits from competing treatments.
M.D., Professor of Urology and Oncology, School of Medicine, Johns Hopkins University
H. Ballentine Carter, M.D., is Professor of Urology and Oncology at the Johns Hopkins University School of Medicine. He has written extensively on the diagnosis and management of prostate cancer, and chaired the American Urological Association guideline panel that made recommendations for prostate cancer diagnosis. He leads one of the largest active surveillance programs in the United States to monitor men with prostate cancer who do not need immediate treatment. Results from this program have been used to inform guidelines for the management of men with early prostate cancer. Dr. Carter has had research articles published in a number of publications, including The Journal of Urology, Urology, Cancer Research, the Journal of the American Medical Association (JAMA), the Journal of Clinical Oncology and the Journal of the National Cancer Institute.
Active Surveillance: Individualizing Prostate Cancer Care
Active surveillance (AS), an alternative to immediate curative intervention for men with favorable prostate cancer, reduces over treatment and avoids treatment related morbidity. Individualizing selection of appropriate candidates and monitoring strategies for AS represents an unmet need for the future.