Transforming Population Health with Predictive BI, Risk Stratification & Geospatial Insights

Abstract

Population Health Management (PHM) is a cornerstone of the shift toward value-based care. However, over 60% of healthcare organizations struggle with challenges such as: - Identifying high-risk populations - Predicting chronic disease trends - Efficiently allocating healthcare resources

The Role of Business Intelligence in PHM

Integrating Business Intelligence (BI) platforms empowers healthcare providers to unify data from: - Electronic Health Records (EHRs) - Claims data - Social Determinants of Health (SDOH) This consolidation generates a comprehensive, 360-degree view of patient risk factors and community health trends.

Key Capabilities and Outcomes

Predictive Insights and Risk Stratification

Using predictive BI models and risk stratification, healthcare organizations have achieved: - 30% improvement in preventive care adherence - Significant reductions in ER visits and hospital readmissions - Up to 25% decrease in hospitalizations through early identification and proactive care

Geospatial and Machine Learning Analytics

• Geospatial analytics map disease prevalence and highlight underserved communities, enabling data-driven resource planning - Machine learning models forecast chronic disease progression with 85% accuracy, facilitating more targeted and effective interventions

Real-World Impact

This session highlights recent advancements in PHM analytics, featuring case studies that demonstrate: - 40% increase in patient engagement - 20% reduction in healthcare costs - Enhanced health equity through AI-driven decision-making

Takeaways for Healthcare Leaders

Attendees will gain actionable strategies to: - Optimize preventive care delivery - Address healthcare disparities - Drive improved population health outcomes By leveraging the power of data and AI, healthcare organizations can transform patient care, reduce costs, and advance equity at scale.