10:00am - 11:00am Thursday, March 12

Location & Room

Orlando - Orange County Convention Center
W414A
When selecting among potential data sources for population health, it is important to select the data sets that are most helpful to segment the population. Data sources, such as EHR components, social determinants of health, socioeconomic data, wearables, clinical data, claims data, and others, are likely candidates for inclusion. When one considers selecting among various data sources for inclusion in a population health machine learning and analytics platform, it is important to identify the data sources that are most relevant, use technology tools such as machine learning to segment the population, and present the machine learning findings to care managers and providers in their native workflow. In this session, Christiana Care Health System will describe its use of various data sets to drive a machine learning platform, identify the relative contribution of various types of data, and discuss which data sources are most important for accurate predictive modeling efforts.
Speakers: 
Chief Health Information Officer and Vice President of Population Health Informatics,
Christiana Care Health System
Chief Data Scientist Officer,
Health Catalyst
Continuing Education Credits: 
CAHIMS
1.00
CME
1.00
CNE
1.00
CPHIMS
1.00