SAS Health Solutions Features List

Health & Life Sciences solutions

  • Empower providers, public health and life sciences organizations to drive trusted health innovations and accelerate time to value with SAS Health Solutions.
  • Unlock explainable insights faster to make confident decisions at every moment.
  • End-to-end solutions for health data integration, management, automation and analytics.
  • Connect 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 non-health data sources.
  • Provide machine learning, artificial intelligence and visualization capabilities for addressing pertinent industry-relevant business challenges.

Efficiently ingest & securely access clinical data

  • Easily ingest data from industry-standard formats, including Fast Healthcare Interoperability Resources, into a common data model.
  • Establishes secure access to various systems, data sources and applications.
  • Automates incremental data load for faster decision making, reporting and analytics.

Integrate & unlock trusted analytic insights faster

  • Integrates health and non-health data sources, and prepares data for analytics. Manages data and analytic model lineage.
  • Explore, visualize and report insights from real world data sources.
  • Identify treatment pathways, gaps in care and barriers to reimbursement.
  • Gain support for formulary development, market access and clinical development.
  • Understand unmet needs in therapeutic areas, medical products or devices, and longitudinal effects of therapies on patients.
  • Access embedded advanced analytics and apply transparent AI and machine learning predictions with repeatable explanations of data, models and predictions that complement descriptive analytics and help you with confident decision-making, such as the anticipation of member/patient needs.
  • Creates transparent reports and unlocks analytic insights faster, such as disease prevalence within a population, patient outcomes or potentially avoidable care.
  • Includes techniques such as clustering, decision trees, linear regression, logistic regression, generalized linear models, generalized additive models and nonparametric logistic regressions.
  • Built-in bias monitoring and transparent explanations of data, models and predictions complement descriptive analytics and drive confident decision making, such as the anticipation of member/patient needs.

Low-code/no-code interface

  • All users across your health organizations can access analytic insights via our low-code/no-code, graphic user interface.
  • Enables all users to participate and have analytic insights at their fingertips.

Enterprise-level episode builder

  • Enrich existing data sources by combining event output with traditional data sources for gaining insights.
  • Create customized episode definitions and associations for rapid time to value.
  • Generate new insights, such as provider attribution, financial implications of potentially avoidable complications, outlier studies and risk-adjusted cost comparisons based on trusted analytics.
  • Build retrospective and prospective bundled payments to identify savings, to identify financial and clinical risk, and to expose potentially avoidable complications.
  • Identify areas of improvement in patient care coordination.

Flexible deployment options & open-source integration

  • Cloud-native and cloud-agnostic with complete portability across on-premises, hybrid or multicloud environments.
  • Enables you to get the best models into production quickly or deploy them into the decision/workflow engine.
  • Lets you register models in a centralized repository for version control and simplified compliance processes.
  • Easily integrate teams and technology across the analytics life cycle, enabling all types of SAS and open-source users to collaborate.
  • Lets you refresh cohorts and analytic outputs based on data refreshed through process automation and common data models.