Patient 360 + Opioid Fraud Detection Using Machine Learning and TigerGraph

What You Will Learn

• Full Patient 360 - Tie all aspects of the patient together and identify relationships. Use Graph & Time Series to view history and identify with ML patterns.
• Churn Prevention - Data segmentation, analytics for historical data and pattern views.
• Sentiment Tracking & Patient Care Analysis - Analytics to navigate your data and track patient care.
• Identification of Claim Anomalies - Fast, impactful analysis of data for anomaly detection.
• Member Care & Doctor Abuse Detection - Intervention and Graph relationship management for cause and case tracking.
• Influence Analysis - Most influential doctors + pharmacies;  most influential financial analysts.
• Network Efficiency - Using graph to determine which networks are most efficient.
• Working Prototypes - See graph and ML in action