Predictive Modeling requires an existing data repository, in which data is mined, and selection of a specific modeling tool to apply to the historical timeframe to predict outcomes for an alternative timeframe. Traditional data sources may include individual demographics, pharmacy and medical claims data, lab values, health risk assessment data, and other clinical data. The National Predictive Modeling Summit provides the leading forum on the techniques, technologies, strategies, innovations, and case experiences involved with predictive modeling clinical and actuarial applications for health plans and related settings

The Summit provides nationally renowned keynote speakers, plus concurrent sessions organized into conference tracks including:

1. Innovations
2. Actuarial Applications
3. Health Plan Populations
4. Clinical Applications
5. Basics

Learning Objectives

• Review the current environment regarding predictive modeling concepts, trends, initiatives, and results
• Offer insights into new innovations in predictive modeling techniques and applications
• Analyze return on investment criteria and experiences
• Consider clinical and business applications of predictive modeling relevant for hospital settings
• Explore the ongoing application of predictive modeling in actuarial and plan design settings
• Examine predictive modeling applied to clinical and care management settings
• Gain a working knowledge of specific challenges being addressed by health plans, provider networks, and purchasers of health care services
• Understand the information technology, data and infrastructure requirements to undertake various predictive modeling applications
• Share case experiences of predictive modeling initiatives already applied in the industry
• Examine various solutions available to implement various types of predictive modeling programs
• Provide a basic understanding of predictive modeling concepts, techniques and issues for those previously not intimately familiar with the subject