Patient 360 + Opioid Fraud Detection Using Machine Learning and TigerGraph

April 26, 2019
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April 26, 2019

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
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