New data and analytic products address complex health care issues of medication adherence, medical record review, reimbursement models, risk management and cost of care
CARY, N.C., June 16, 2025 /PRNewswire/ — SAS, the leader in data and AI, has announced a new solution – SAS® Health Cost of Care Analytics – targeted to health care payers and providers to support effective decision making about quality and cost of care.
Available in July 2025, SAS Health Cost of Care Analytics taps into health claims data and enables health care organizations to construct and analyze claims as episodes of care by stitching together codes and patterns all along a “care journey” from detection to treatment to care management. The health care analytics solution built on the data and AI platform SAS® Viya® can enable more cost-effective treatment pathways, reduce unwarranted admissions, decrease the length of hospital stays and more.
SAS will introduce SAS Health Cost of Care Analytics and other data and analytic solutions for health care payers and the industry at AHIP 2025 in Las Vegas.
Per Gartner®, “Payers increasingly need sophisticated data and analytics to achieve outcomes such as identifying rising risk populations, improving quality measure performance, managing the total cost of care and personalizing experiences.”1
SAS Health Cost of Care Analytics supports the industry with unique features that include:
- Flexible definitions for episodes of care. Users can analyze claims as episodes of care using transparent clinical definitions of their choice and detect associations for a holistic patient view.
- Value and care outcome measurement. The solution categorizes services and their care costs as being value-added or potentially avoidable. Additionally, expected and risk-adjusted costs are automatically calculated and can then be used as quality/efficiency measures or payment metrics. Other quality metrics, such as length of stay, are also captured.
- Risk-based calculations to compare provider performance. Using medical claim data and applying user-selected rules, the solution attributes episodes of care to providers, facilitating the measurement of cost and quality of care relative to patient severity or to set reimbursement targets.
- Analytic insights with simplified data management. The product supports data validation and simplified data ingestion into the SAS Health common data model to create data repositories.
“As health care incentives focus in on value, providers and payers must identify the drivers of cost, quality and outcomes to make decisions about protocols and provider contracts,” said Brett Davis, Senior Manager, Health Care Advisory at SAS. “Going far beyond traditional claims grouping, SAS Health Cost of Care Analytics generates valuable insights to identify variations and opportunities for improvement when assessing financial risk, all while understanding members in more detail.”
Ready-made AI models for health care
Also new on the market to support payers and providers are ready-made AI models. Complementing its health care solutions portfolio, SAS’ AI models are built from decades of expertise in applying scalable and trustworthy AI to real-world use cases and are designed to tackle business challenges with precision and efficiency.
The newly available models include:
- SAS Medication Adherence Risk: Medication adherence has long been a measure of patient health outcomes and a key factor in regulatory quality assessments (e.g., quality ratings for Medicare Advantage, Medicaid and Exchange). SAS Medication Adherence Risk enables managed care organizations to identify where resources are needed for timely and targeted intervention, resulting in enhanced patient engagement, better health outcomes, improved quality metrics and lower health care costs.
- SAS Document Analysis: The SAS Document Analysis model is an intelligent document processing pipeline focused on extracting contextual information from scanned document images and generating structured data assets. The AI model converts scanned images into summarized data for medical reviewers, allowing them to evaluate records more effectively and efficiently. The SAS Document Analysis model’s enhanced analysis of unstructured claims data has already shown a 400% efficiency gain over manual review in one of the largest U.S. health insurers.
- SAS Payment Integrity for Health Care Detect and Prevent: The payment integrity models offer capabilities for addressing issues with health billing errors and claim discrepancies, detecting fraud and preventing improper claims. The models are designed to accelerate implementation of SAS solutions that health care programs within private insurance plans and government-funded programs use to meet regulatory compliance requirements, operate transparently, and more efficiently manage and contain costs associated with fraud, waste and abuse.
SAS Health solutions at AHIP
SAS will unveil the new health care solutions at AHIP 2025 – the annual conference for the national trade association representing the health insurance industry – this week in Las Vegas. Conference attendees are invited to join live demo sessions in the SAS booth at AHIP to experience hands-on insights, expert Q&A and a glimpse into the future of health care.
Learn more about how SAS delivers the most productive AI platform to transform data into life-changing insights and accelerate breakthroughs in health care here.
About SAS
SAS is a global leader in data and AI. With SAS software and industry-specific solutions, organizations transform data into trusted decisions. SAS gives you THE POWER TO KNOW®.
SAS and all other SAS Institute Inc. product or service names are registered trademarks or trademarks of SAS Institute Inc. in the USA and other countries. ® indicates USA registration. Other brand and product names are trademarks of their respective companies. Copyright © 2025 SAS Institute Inc. All rights reserved.
1 Gartner, “Top Technology Trends for U.S. Healthcare Payer CIOs in 2025”, February 2025. GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved.
Editorial Contact:
Jennifer James
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