SAS^® Payment Integrity for Health Care

SAS Payment Integrity for Health Care Features List

Fraud data management

Provides a health-care-specific fraud, waste and abuse data model.
Consolidates historical data from internal and external sources – claims systems, watch lists, third parties, unstructured text, etc.
Eliminates or reduces redundant or inconsistent data with the solution’s built-in data quality tools.
Seamlessly integrates with existing payment integrity solutions, including case management systems, overpayment recovery vendors, audit control systems, etc.
Back-end entity resolution leverages demographic identifiers to reconcile instances of individuals who appear as separate entities in submitted data.

Provides a broad set of modern statistical, machine learning, deep learning and text analytics algorithms from within a single environment
Enables you to improve fraud models by testing different approaches in a single run, and comparing results of multiple supervised learning algorithms with standardized tests.
Provides an array of analytical capabilities, including clustering, different types of regression, random forests, gradient boosting models, support vector machines, natural language processing, topic detection and more.
Continuously updates and improves models based on prior output results.

Provides prepackaged heuristic rules, anomaly detection and predictive models, so you can harness the power of advanced analytics right out of the box.
Lets you create and logically manage business rules, analytic models, alerts and watch lists.
Enables you to customize analytical models to identify fraud, waste and abuse not found by existing business rules.
Enables easy management of the deployment, aggregation, scheduling, suppression and routing of similar rules across multiple factors, such as parties, data sources and business lines.
Lets you run groups of rules and models alone, in parallel or at different times (intraday, daily, weekly, monthly, etc.).
Facilitates collaboration with other business units (e.g., member cost management, chronic condition management, operations analytics) on model development.

Calculates the propensity for fraud at first submission, then rescores claims at each processing stage as new claims data is captured.
Reviews claims early in the adjudication process so you can stop suspicious activity at the prepayment stage.
Enables you to incorporate fraud detection methods into the process at the most appropriate points – e.g., cases where anomaly detection scenarios may require data that is not available until later in the adjudication process.

Combines alerts from multiple monitoring systems, associates them with common individuals and provides a more complete perspective on the risk of particular individuals or groups.
Prioritizes the investigative order of alerts by scoring them in real time, based on specific characteristics.
Automatically routes alerts to appropriate team members based on user-set rules and requirements.
Displays all evidence for each case on a dashboard that you can customize to accommodate your investigative unit's processes.

Provides a unique network visualization interface that lets you go beyond transaction and account views to analyze related activities and relationships at a network dimension, and identify linkages among seemingly unrelated claims.
Enables you to produce complete dossiers of networks surrounding a case, and gain fast access to full details on all related parties and networks.
Produces independent and combined fraud scores, so you can assess overall risk on a customer, claim or network basis.
Increases investigator effectiveness by enabling investigators to merge and delete network entities, and add annotations (text and images) to specific entities in a network.
Provides time slider functionality, which enables you to see how activity in a network develops over a time horizon.

Enables free-text, field-based or geospatial searches across all data (internal and external).
Lets you refine searches using interactive filters and facets that are purposed for the SIU team.
Provides an intuitive interface that lets you construct complex queries without the need to understand specific syntax. For example, you can use fuzzy searching, proximity searching and field boosting while restricting searches to specific entity types, fields, comments or insights.

Systematically facilitates investigations using a configurable workflow.
Stores all information pertinent to a case, including detailed investigation information – e.g., interview notes and evidence for criminal or civil prosecution, restitution and collections.
Assesses overall fraud exposure, including losses due to fraud as well as fraud detected or prevented.

Lets you take advantage of all the benefits of SAS Viya.
Language-agnostic programming enables data scientists to access SAS algorithms via open source programming interfaces – including R, Python, Java and Lua – from applications such as Jupyter Notebook.
Lets you choose the package level that best meets your objectives and budget.