Containing health care costs

Trusted analytics paves the way to modern payment integrity

By Paola Csirke-Juliano, Senior Product Marketing Manager, SAS

To contain costs, health care organizations (HCOs) are changing how they pursue claims overpayments. Traditionally, efforts focused on fraud prevention and detection. Today – under the umbrella of “payment integrity” – organizations are uncovering a broader range of fraud, waste and abuse (FWA) in health care claims processing, not only to improve cost savings but also to reduce administrative burdens and system errors, and to improve access and the quality of care. Data-driven analytics is making that shift possible.

The past: When simple payments shaped early health care payment integrity

Health payers historically reimbursed providers on a fee-for-service (FFS) schedule for claims. Claims coding became even simpler with electronic claims processing. However, complexity increased with successive revisions of the International Classification of Diseases (ICD) into ICD-10 and other claims codes. Consequently, modernizing payment integrity in health care was less of a priority than tracking the evolving codes.

Meanwhile, social and political pressures to contain rising health care costs intensified. Managed care helped, but costs continued to climb. When claims payment integrity programs began, they were often limited to payer operations and focused on billing errors, third-party liability and claims subrogation.

From a data analytics perspective, the hardware in this era made analyzing large data volumes difficult and expensive. Most payers struggled to clean and combine data sets for meaningful analysis. Over time, more robust tools for data integration, cleansing and management made it easier for payers to prepare their data for advanced analytics and AI.

The present: Payment integrity evolves with value-pay models

Leaders of health care organizations and government programs recognize that FFS payment models are driving volume and higher costs without improving health outcomes. This trend accelerates the shift toward value-based payment models designed to reward higher quality of care and cost containment.

These models require more advanced analytics to balance cost savings and quality outcomes, making payment integrity an essential strategy.

Value-based payment models cluster services around specific health needs (e.g., knee replacement surgery) and related health outcomes. At the same time, point solutions and wraparound services – such as diabetes management – are increasingly paid under at-risk capitation models. These approaches require sophisticated analytics to determine patient health acuity, project costs for patient risk pools and support actuarial rate setting.

As health care delivery grows more complex, claims payment accuracy becomes more challenging.

• Coding options and requirements are increasing, including codes tied to quality of care. 
• Private payers are developing more nonstandard provider contracts. 
• Public payers, such as Medicaid and Medicare, are moving more beneficiaries into managed care administered by private payers.

In countries with national health care systems, data sharing is often limited between the national plan and private wraparound plans for the public plan’s non-covered services. This lack of coordination of benefits contributes to improper payments in health care as well as FWA and billing errors.

US health care organizations, including payers, have determined that integration helps control costs and enhance the quality of care. Today, we see a convergence among large health plans, pharmacy benefit managers and clinical providers – for both physical and mental health – reflecting the movement toward whole person care.

Meanwhile, rising claim data volumes, increasingly sophisticated health care fraud schemes and regulatory pressures are driving the adoption of AI-driven technologies and approaches to improve payment integrity in health care.

The emergence of cloud-based storage reshaped how data is managed and analyzed. Now, virtual machines, containers and Kubernetes enable data architecture to catch up with software capabilities, improving operational flexibility across a more connected health care ecosystem. Open source tools continue to evolve, though challenges with data volume, velocity and model management persist. The rise of AI and advanced analytics expanded access to new data, creating opportunities for automation while also increasing the risk of FWA.

The future: What is next for modern health care payment integrity

Advancing health technologies continue to reshape how health care is delivered.

For example, smartphones and wearable devices have gradually changed consumer expectations. Value-based payment models are still evolving and expanding. High data volumes and the need for health care clinical data integration and interoperability will continue to increase complexity. Improving access, payment accuracy, care quality and health outcomes will continue to be a common topic of discussion among public health leaders.

From a data analytics perspective, organizations have a deeper understanding of the value of unstructured data. However, as care service delivery and payment models evolve, data capture and storage are lagging. As a result, large volumes of unstructured data – care coordination notes, policy documents, medical records, claims and clinical data – remain rich in insights but often inaccessible for data analysis.

To ensure payment integrity in health care, organizations need:

• AI capable of analyzing unstructured data in medical records and complex policy language. Expect rapid advances in computer vision, document vision, text analytics, GenAI and agentic AI, along with continued growth in automation and intelligent decisioning.
• Prepay strategies, including provider credentialing and real-time fraud detection. Successful organizations will continue to form partnerships and to invest in technologies that strengthen collaboration, oversight and operational resilience.

Organizations that embed trusted AI, continuous monitoring and advanced analytics into their payment processes will be best positioned to prevent improper payments, contain costs, strengthen payment integrity issues from every angle and improve outcomes.

Trusted, sustainable payment integrity

SAS combines three disciplines − behavioral analytics, claim analytics and clinical targeting − into a comprehensive health care payment integrity solution available through multiple deployment options.

Explore how SAS empowers health care organizations to build resilient payment integrity systems that support sustainable care.