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Exploring how AI-driven analytics can transform care management through focus on Population Health Risk Stratification and Predictive Modeling. We will discuss how advanced data science techniques using Electronic Health Records (EHR) and claims data provides us an opportunity to perform risk stratification to help identify high-risk populations, predict adverse health events, and enable proactive interventions; ultimately improving health outcomes and reducing costs.
Discovering real-world applications, case studies, and the latest innovations that are empowering healthcare organizations to shift from reactive to preventive, value-based care strategies.
Key Topics Covered:
- AI driven Risk Stratification: Identify high-risk patients & build member cohorts with extreme utilization
- Predictive Modeling in Action: AI and ML techniques for forecasting current & evolving health risks
- Driving Proactive Care Management: Leveraging insights to optimize intervention planning
- Real-World Case Studies: Success stories in reducing ER visits and improving health outcomes
- Integrating SDOH & Clinical Data: Enhancing models with social determinants of health
- Measuring Impact: Key KPIs and metrics to evaluate success
This session demonstrates the solutions and opportunities for healthcare leaders, data scientists, and care management professionals looking to enhance patient outcomes with data-driven strategies. The presentation is a mix of business and technical (AI ML) topics.
Speakers:
– Saurabh Bhargava – VP of Data Science, DataLink
– Priyank Jain – Director of Data Science, DataLink