SAS® Health Features

Industry-specific solutions

  • Empowers health and life science organizations to accelerate time to value with industry-specific solutions and applications powered by the SAS Platform.
  • Provides interactive drag-and-drop graphical user interfaces for guided development 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.
  • Includes prebuilt, industry specific studies – e.g., comparative effectiveness, readmissions/length of stay, incidence/prevalence, adherence/compliance, patient journey and treatment pathways.
  • 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 graphics for showing the effects of each inclusion/exclusion criterion on the patient population to determine study feasibility.
  • Lets you leverage 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 associations for rapid time to value.
  • Includes prebuilt, industry-specific insights, such as provider attribution, financial implications of potentially avoidable complications, outlier studies and risk/severity adjusted cost/utilization comparisons.

Efficiently consume and standardize clinical data

  • Maps clinical source data to industry standards.
  • Enables you to create approved libraries of mappings for internal use.
  • Automates data transformations based on approved mapping processes.

Integrate and 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.
  • Provides access to predefined reports, 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.

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 the SAS® Platform

  • 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.
    • Offers prebuilt analytical models, including predictive models for health costs, utilization and outcomes.
    • 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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