Session ID: 
229

Machine Learning and Data Selection for Population Health

10:00am - 11:00am Thursday, March 12
Orlando - Orange County Convention Center
W414A

Description

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.

Learning Objectives

  • Explain how to assign relative value to population health data sets/sources for inclusion in machine learning algorithms to effectively manage population risk
  • Identify the key pain points associated with acquiring, integrating, and maintaining population health data sources
  • Describe how machine learning, using a variety of data sources, can segment populations to assist providers in care management and focused care delivery for selected individuals
  • Apply these learnings to develop a strategy for selection of new data sources

Speaker(s)

Chief Health Information Officer and Vice President of Population Health Informatics,
Christiana Care Health System
Chief Data Scientist Officer,
Health Catalyst

Continuing Education Credits

ABPM
1.00
CAHIMS
1.00
CME
1.00
CNE
1.00
CPHIMS
1.00

Audience

Clinical Informaticists
Investor/Entrepreneur
Population Health Management Professional

Level

Intermediate