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Improper Payment Detection

Uncover Healthcare Fraud, Waste and Abuse With Speed and Accuracy

Detect and prevent improper payments with a focus on high-value cases and maximum ROI

Fraudsters don’t play by the rules. Your detection strategy shouldn’t either.

Shift’s healthcare-trained AI models go beyond static, rules-based detection methods to increase detection accuracy, uncover higher-value healthcare fraud, waste and abuse, and boost the efficiency of investigators.

files-lightBG_Improved Efficiency

Investigators should spend their time investigating. They shouldn’t waste time combing through sources for the right data or information. With enhanced plan data, combined with extensive external data, Shift gets the right insights to investigators faster.  

Investigative detail all in one place

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Skip hours spent compiling data and information with claim and provider details, online reviews, exclusion lists and much more already integrated into the platform.

Uncover related providers

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Alerts with context and action

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Any data, any format

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Powerful detection across the full Payment Integrity lifecycle

Increase savings by stopping FWA prior to payment.
Shift enables lightning-fast detection and decisions before a claim is paid. Shift's AI-powered, real-time analysis stops emerging schemes in their tracks. Combined with enhanced external data, investigators are equipped with the context behind the alert to quickly conduct investigations and reduce provider abrasion.

TIME TO SHIFT AWAY FROM THE TRADITIONAL FWA DETECTION APPROACH

Healthcare fraud, waste and abuse is constantly changing - is the traditional approach enough? Dive into the traditional approaches to fraud, waste and abuse detection using rules-based tactics and the increasing shortfalls in relation to evolving fraud and demands from health plans. Learn how an AI-driven approach to improper payment detection can upend traditional approaches, with actionable insights and increased collaboration across the plan.

ShiftLeft: time to shift away from the traditional FWA detection approach