November 12, 2013
Jonathan P. Weiner, DrPH
Professor of Health Policy & Management, Johns Hopkins Bloomberg School of Public Health
Professor of Health Informatics, Johns Hopkins School of Medicine
Director, Johns Hopkins Center for Population Health Information Technology (CPHIT)
About the Presentation: Professor Weiner’s presentation will focus on how electronic health records and other e-health tools can be harnessed to move beyond providing medical care for a single patient episode towards the achievement of “population health.” This provocative presentation will offer new conceptual paradigms and will review “big data” opportunities and challenges. The emphasis of the talk will be on how population focused care transformation can be brought about through the integration and application of e-health/EHR systems and claims/MIS systems. The talk will offer examples of analytic tools and methods designed to increase the effectiveness, efficiency and equity of care provided at a geographic or community level and to “populations” of consumers enrolled in health plans, ACOs and other integrated delivery systems.
Key goals of presentation:
– To offer frameworks and paradigms to better understand how EHRs and other HIT can improve population health
– To outline opportunities and challenges for communities, ACOs and other integrated delivery systems
– To offer some case studies on the application of health IT to population health
About the Presenter: Jonathan P. Weiner, DrPH, is a professor of health policy and management at the Johns Hopkins Bloomberg School of Public Health in Baltimore. He is also a professor of health informatics at the Johns Hopkins School of Medicine. He is the director of the newly formed Johns Hopkins Center for Population Health IT (CPHIT). Dr. Weiner is an internationally regarded researcher, consultant and lecturer. His current work focuses on the application of electronic health records (EHRs) and Health IT for population based applications. He is the co-developer of the Johns Hopkins ACG case-mix / predictive modeling tool now widely applied to care for 80 million patients in more than 18 nations.