Next Meeting: Thu, Oct 24 2013
Data, Cluster Models, and Population Health Manage
Event Type: Online
Online Meeting URL: https://attendee.gotowebinar.com/register/8513550773294276866
UTC : Thu, Oct 24 2013 17:00 - 18:00
Event Time : Thu, Oct 24 2013 13:00 - 14:00 Eastern Daylight Time
Your Local Time: Thu, Oct 24 2013 17:00 - 18:00
Data, Cluster Models, and Population Health Management
Paul Bradley, Co-Founder & Chief Scientist MethodCare
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 system. Integrating these disparate sources together in patient-centered datasets provides the foundation for the application of probabilistic cluster 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 a 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.
Paul Bradley is a co-founder and Chief Scientist at MethodCare, overseeing the company’s research and development functions, including development of new processes, technologies, and products. In addition to keeping MethodCare at the forefront of predictive analytics, data mining advances, and industry trends, Paul works with various governmental agencies on advanced healthcare research that is leading to improved clinical results at reduced costs to patients, providers, and payers.
Previously, Paul led data mining algorithm implementations for a number of Microsoft divisions. He served as the data mining development lead at Revenue Science, Inc. (formerly Digimine, Inc.) and was a researcher in the Data Management, Explorations and Mining Group at Microsoft Research, where he helped develop data mining algorithms and components that shipped with SQL Server.