How SAS® Helps Modernize Medicaid Management
Provide better care
Achieve intended outcomes in the face of internal and external constraints.
Enhance program integrity
Detect and prevent fraud, waste and abuse before money goes out the door.
Effectively manage costs and demonstrate effectiveness.
Why choose SAS for Medicaid modernization?
Gain holistic data insights and enhanced information credibility.
- Seamlessly integrate any enterprise data source and apply embedded data quality techniques to improve accuracy.
- Gain a holistic view of a recipient or provider to better detect anomalies or discrepancies across government programs or systems.
- Enable data scientists with varied programmatic skill sets to work in their preferred programming language.
Focus on prepayment.
Our hybrid approach combines anomaly detection, rules and predictive modeling to identify fraud, waste and abuse earlier than traditional methods by applying:
- Predictive modeling rules. Ensure that known schemes are detected with a program integrity solution that includes an embedded rules library.
- Unsupervised learning. Proactively predict where fraudulent activities will occur.
- Machine learning models. Get faster, better modeling results from our solution, which includes industry best practice predictive modeling templates, hyperparameter autotuning and embedded model interpretability – all accessed through an intuitive interface.
SAS Detection and Investigation for Health Care
SAS delivers a comprehensive solution for detecting and prevent health care fraud, waste and abuse at every stage of the claims process, and stopping improper payments before claims are paid. The solution uses advanced analytics with embedded AI and machine learning algorithms, combined with other techniques – business rules, outlier analysis, text mining, database searches, exception reporting, network link analysis, etc. – to uncover more suspicious activity with greater accuracy.