AI Revolutionizes Member Health Risk Identification
Artificial intelligence (AI) outperforms traditional models in identifying high-risk patients by integrating diverse data for proactive care in managed care settings.
In managed care, identifying members at risk for poor health outcomes is crucial for improving care coordination, reducing costs, and starting early interventions.
- Traditional rule-based risk models use fixed thresholds, such as lab values or diagnosis counts.
- These models often miss the complexity and changing nature of people's health.
AI offers a more flexible, data-driven approach that can keep learning from many different factors that affect risk.
According to Toshi Chintala, junior data scientist at Datalink Software, AI can more accurately identify and predict member health risks than traditional systems.
Author's summary: AI improves high-risk patient identification in managed care.