AI for Population Health Management: Assessing Risk, Building Trust, and Driving Better Outcomes

Artificial Intelligence (AI) is increasingly leveraged for population health management by healthcare organizations seeking to improve patient care while reducing costs. Used as a predictive tool, AI can identify at-risk and emerging-risk populations, enabling limited clinical resources to target patient populations that will most benefit from — and respond to — hands-on intervention.

In this white paper, we take a deep dive into:

• ZeOmega’s methodology around using AI for population health management
• Existing models that can accurately predict risk for some of healthcare’s biggest cost drivers: opioid use and diabetes
• How these and other AI models are developed, tested, iterated on, and integrated into existing population health management workflows
• Machine Learning (ML) techniques and how they improve AI models over time
• The tremendous possibility of applying predictive AI models to other chronic conditions

Additional Population Health Management Resources:

White Paper: Behavioral Health Care Management Transformation

White Paper: Manage the Complex Long-Term Services and Supports (LTSS) Population Leveraging Population Health Management Technology

White Paper: Population Health Management (PHM) Platform with High-performance Rules Engine

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