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List of SAS Episode Analytics Customers

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Logo Customer Industry Empl. Revenue Country Vendor Application Category When SI Insight Insight Source
Arizona Care Network LLC Healthcare 65 $17M United States SAS Institute SAS Episode Analytics Analytics and BI 2016 n/a In 2016 Arizona Care Network LLC initiated implementation of SAS Episode Analytics in the Analytics and BI category to standardize encounter-level clinical data. The project began with a formal scope to implement CDISC standards alongside the SAS implementation of the Observational Medical Outcomes Partnership OMOP standard for encounter data, aligning clinical records to standardized data models for downstream analysis. Implementation work focused on configuring SAS Episode Analytics modules for episode construction, cohort definition, and standardized encounter mapping. The configuration included data model normalization, ETL pipelines to translate raw encounter records into OMOP-conformant structures, and CDISC-aligned metadata tagging to support clinical reporting and regulatory-ready datasets. Operational integration centered on ingesting encounter datasets from the organization clinical sources into the SAS Episode Analytics framework, enabling clinical analytics and population health workflows to consume OMOP-standardized episodes. The deployment covered clinical analytics and care management functions, with data feeds routed into reporting and visualization processes configured inside SAS Episode Analytics. Governance work established CDISC and OMOP mapping rules, validation checkpoints, and a staged rollout plan to onboard departments to the new episode analytics capability. The program emphasized controlled data standardization and repeatable mapping processes to ensure consistent encounter normalization across the network.
Envision Healthcare Healthcare 55000 $16.0B United States SAS Institute SAS Episode Analytics Analytics and BI 2016 n/a In 2016, Envision Healthcare's Evolution Health implemented SAS Episode Analytics. The deployment positioned SAS Episode Analytics as an Analytics and BI solution for episode-based cost analysis and coordinated care assessment, enabling clinicians and care managers to model episodes such as joint replacement and chronic disease treatment. SAS Episode Analytics was configured to establish diagnosis-specific baselines and to support clinician-driven adjustments for severity and case complexity. Functional capabilities implemented include advanced episode definitions, episode attribution logic to differentiate avoidable complications from typical services, and configurable modeling to assess the full cost of coordinating care across service types. The implementation emphasized operational use by clinicians and care coordination teams to examine relevant services across the entire continuum of care, including pre-admission, inpatient, post-discharge, rehab, home health and long term care. The solution acts as an Analytics and BI layer that surfaces episode-level cost and service patterns to clinical stakeholders and care management workflows. Governance and workflow change centered on clinician interaction with episode models, with clinicians starting from a baseline diagnosis model and adjusting parameters to reflect patient severity and complexity, embedding model tuning into care pathway reviews. Evolution Health expected that advanced episode definitions and the use of SAS Episode Analytics would improve care and patient outcomes by enabling separation of avoidable complications from routine services.
Geneia Professional Services 200 $100M United States SAS Institute SAS Episode Analytics Analytics and BI 2015 n/a In 2015, Geneia implemented SAS Episode Analytics. The deployment focused on Analytics and BI to provide episode-based clinical and cost analytics for accountable care organizations, reflecting the collaboration between Geneia and SAS to improve patient health. SAS Episode Analytics was configured to support episode construction, cohort analytics, cost and utilization analysis, quality measurement, risk stratification, and interactive reporting dashboards. Configuration emphasized reusable episode definitions and attribution logic, automated analytics pipelines for recurring reporting, and role-based dashboards to support population health and care management workflows. Operational scope centered on ACO operations and care management teams, with the solution embedded into clinical governance and performance monitoring processes. Governance established centralized analytics stewardship and iterative configuration cycles to align episode definitions with ACO performance frameworks, while ongoing collaboration between Geneia and SAS supported refinement of analytics and reporting to improve patient health.
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FAQ - APPS RUN THE WORLD SAS Episode Analytics Coverage

SAS Episode Analytics is a Analytics and BI solution from SAS Institute.

Companies worldwide use SAS Episode Analytics, from small firms to large enterprises across 21+ industries.

Organizations such as Envision Healthcare, Geneia and Arizona Care Network LLC are recorded users of SAS Episode Analytics for Analytics and BI.

Companies using SAS Episode Analytics are most concentrated in Healthcare and Professional Services, with adoption spanning over 21 industries.

Companies using SAS Episode Analytics are most concentrated in United States, with adoption tracked across 195 countries worldwide. This global distribution highlights the popularity of SAS Episode Analytics across Americas, EMEA, and APAC.

Companies using SAS Episode Analytics range from small businesses with 0-100 employees - 33.33%, to mid-sized firms with 101-1,000 employees - 33.33%, large organizations with 1,001-10,000 employees - 0%, and global enterprises with 10,000+ employees - 33.33%.

Customers of SAS Episode Analytics include firms across all revenue levels — from $0-100M, to $101M-$1B, $1B-$10B, and $10B+ global corporations.

Contact APPS RUN THE WORLD to access the full verified SAS Episode Analytics customer database with detailed Firmographics such as industry, geography, revenue, and employee breakdowns as well as key decision makers in charge of Analytics and BI.