Join Paul Bradley of ZirMed as he explores the transition to population health management and the data required to make it happen.
Event Details
As healthcare providers transition to outcome-based reimbursements, it is imperative that they make the transition to population health management to stay viable. Providers already have data assets in the form of electronic health records and financial billing systems. Integrating these disparate sources together in patient-centered datasets provides the foundation for the application of probabilistic clustering (mixture modeling) to better understand their patient populations. These models are the core technology to compute and track the health and financial risk status of the patient population being served. We show how the probabilistic formulation allows for straightforward, early identification of a change in health and risk status. Knowing when the patient is likely to shift to a less healthy, higher risk category allows the provider to intervene to avert or delay the shift. These automated, proactive alerts are critical in maintaining and improving the health of a population of patients. We discuss results of leveraging these models with an urban healthcare provider to track and monitor type 2 diabetes patients. When intervention outcome data are available, data mining and predictive modeling technology are primed to recommend the best type of intervention (prescriptions, physical therapy, discharge protocols, etc.) with the best likely outcome.
Registration
Register Online
Registration will close at noon on 9/3/2015 -- the day of the event. Walk-ins will be accepted, space permitting.
Deadline: 9/3/2015
Speaker Profiles
Paul Bradley (Speaker)
Chief Data Scientist, ZirMed
http://www.zirmed.com
Paul is Chief Data Scientist at ZirMed, where he leads the research and development of predictive modeling technologies in revenue cycle and population health solutions. Prior to ZirMed, Paul was a co-founder, and Chief Data Scientist at MethodCare. Prior to co-founding MethodCare, Paul consulted on data mining algorithm integration with Microsoft Research and SQL Server, and led data analysis solution implementations for a number of Microsoft divisions. Earlier, Paul was the data mining development lead at Revenue Science, Inc. (formerly Digimine, Inc.), where he focused on integrating data mining technology into the company's service offering. Prior to Digimine, Paul was a researcher in the Data Management, Exploration and Mining Group at Microsoft Research, where he helped develop new data mining algorithms and components that shipped with Microsoft's flagship database products SQL Server and Commerce Server.
Paul earned his Ph.D. and M.S. degrees in computer science, and a B.S. in mathematics from the University of Wisconsin. His research interests include classification and clustering algorithms, underlying mathematical problem formulations, and issues related to scalability and their application to healthcare problems.
Questions
Roger L Moore, '92
Senior Principal / Sagence
312-543-1319
Other Information
6:30 - 7:00 PM - Networking w/hors d'oeuvres and cash bar
7:00 - 8:00 PM - Program
8:00 - 8:30 PM - Continued networking w/hors d'oeuvres and cash bar