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