Health Care Fraud, Waste & Abuse

Understand and manage clinical and financial risk.

How SAS Detects and Prevents Health Care Fraud, Waste and Abuse

Fraud, waste and abuse in health care divert billions away from patient care annually. Faster, more aggressive investigation and detection of key risk indicators at every stage of the process is key to controlling costs and protecting patients.

A holistic view of clinical conditions & events

  • Gain a more comprehensive view of patient care across a variety of conditions and procedures to identify important event interdependencies.
  • Determine the true cost of clinical conditions, better manage payment models and improve patient outcomes.

Value-based care & payment models

  • Confidently predict and manage financial and clinical risks and rewards associated with contracting for value.
  • Use clinical data to adjust for acuity within provider populations, thus increasing adoption of a value-based care model.

Fraud data management

  • Consolidate historical data from internal and external sources – claims systems, watch lists, third parties, unstructured text, etc.
  • Discover connections among entities sooner to expose organized fraud rings or collusive activities.

Detection & alert generation with embedded AI

  • Gain awareness earlier with alerts based on a calculated propensity for improper billing at first submission.
  • Reduce false positives to improve investigative focus.
  • Uncover suspicious activity sooner with an end-to-end framework that includes modern statistical, machine learning, deep learning and text analytics algorithms.

Why SAS for detecting and preventing health care fraud, waste & abuse?  

SAS enables you to view the cost of care and associated patient outcomes at a level of detail never possible before. Our advanced analytics with embedded AI capabilities optimize the detection, management and prevention of payment integrity issues from every angle.    

Gain insights for value-based care delivery & payment models

Track variations in care delivery – and associated financial implications – by analyzing historical data. Identify potentially avoidable costs by category, and set financial incentives for care and cost improvements.

Report accurate risk adjustment information

Close condition gaps, identify new potential suspects, monitor submissions and highlight conditions that may be the focus of a future audit.

Get a consolidated view of fraud risk

Quantify risk and increase risk metric visibility to affected teams. Identify links among seemingly unrelated claims, and go beyond individual and account views to analyze all related activities and relationships.

Reduce false positives & increase efficiency

Focus on high-priority fraud alerts that require further investigation. Apply risk- and value-based scoring models to prioritize alerts before routing.  

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