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

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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