SAS Health Features | SAS

SAS® Health Features

Industry-specific solutions

  • Empowers health, public health and life science organizations to accelerate time to value with industry-specific solutions and applications powered by SAS.
  • Provides interactive drag-and-drop graphical user interfaces for guided development and analysis of clinical mappings, cohorts and episodes of care.
  • Includes embedded AI and machine learning capabilities.
  • Connects to internal or external data from point-of-care systems, electronic health records, insurance claims, patient-reported outcomes, health devices and trusted third-party data providers, as well as nonhealth data sources.

Interactive, near-real-time cohort builder

  • Lets you create population-specific analytics through a complementary point-and-click and programming interface contained within a single, unified open environment.
  • Runs complex queries that go beyond simple subsetting to selecting criteria with multiple temporal relationships and Boolean logic.
  • Provides progressive near-real-time patient counts and visualizations for showing the effects of each inclusion/exclusion criterion on the patient population to determine study feasibility.
  • Lets you leverage previously internally developed models to ensure consistent findings.

Interactive clinical episode builder

  • Enriches existing data sources by combining event outputs with traditional data sources for gaining enhanced insights.
  • Lets you create customized clinical event definitions and associations for rapid time to value.
  • Generates new insights, such as provider attribution, financial implications of potentially avoidable complications, outlier studies and risk/severity adjusted cost/utilization comparisons based on trusted analytics.

Efficiently consume & standardize clinical data

  • Maps clinical source data to industry standards, such as STDM, OMOP or others.
  • Enables you to create approved libraries of mappings for internal use.
  • Automates data transformations based on approved mapping processes.

Integrate & explore real world data

  • Enables you to explore, visualize and report insights from real world data sources.
  • Lets you identify treatment pathways, gaps in care and barriers to reimbursement, and supports formulary development, market access and clinical development.
  • Create repeatable shared reports and analytic insights, such as cohort characterizations, patient outcomes, and incidence and prevalence.
  • Enables you to understand unmet needs in therapeutic areas, medical products or devices, and longitudinal effects of therapies on patients.
  • Lets you go beyond creating cohort patient lists with embedded advanced analytics, including the ability to apply AI and machine learning techniques in the same analytic environment.

Identify, investigate and act on suspicious activities and events of interest

  • Lets you view entities, claims and other transactions in an interactive network diagram.
  • Enables you to search free text and explore geospatial relationships in data.
  • Lets you create and manage alerts to triage response and intelligent decisioning.

Powered by SAS®

  • Data
    • Integrates nontraditional health care data – such as Social Determinants of Health (SDoH), consumer and streaming data – to enrich analytics and support organizational initiatives.
    • Connects to internal or external data from point-of-care systems, electronic health records, insurance claims, patient-reported outcomes, health devices and trusted third-party data providers, as well as nonhealth data sources.
    • Includes built-in capabilities for cleansing, standardizing, loading and integrating real-world data.
    • Provides an intuitive interface for profiling, integrating and moving data stored in the cloud without having to code.
  • Discovery
    • Provides machine learning, artificial intelligence and visualization capabilities for addressing pertinent industry-relevant business challenges.
    • Includes an analytics library of methodologies that includes simple descriptive statistics, predictive analytics and machine learning methods.
    • Provides an intuitive interface for including SAS or R programs and driving parameters.
    • Includes techniques such as clustering, decision trees, linear regression, logistic regression, generalized linear models, generalized additive models and nonparametric logistic regressions.
  • Deployment
    • Enables you to get the best models into production quickly or deploy them into decision/workflow engine.
    • Lets you register models in a centralized repository for version control and simplified compliance processes.
    • Augments open source capabilities (Python, R, Java, etc.) within a unified environment to empower collaboration within your analytic ecosystem.
    • Lets you refresh cohorts and analytic outputs based on data refreshed through process automation and common data models

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