Collaborative AI: Bridging the Payer-Provider Divide

The Rhino Team

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April 8, 2026

Payers and providers each hold critical pieces of the patient journey, including claims, clinical records, outcomes, and utilization patterns, but these datasets remain fragmented across organizational, technical, and regulatory boundaries. Without the ability to run advanced analytics - including the development and deployment of AI models - across disparate data, the result is missed opportunities to create shared understanding with identifying care gaps, improving quality, reducing costs, and delivering on the promise of value-based care.

While both entities may be driven by different overarching objectives and have an inherent tension, especially when considering claims processing, they are ultimately impacted by the same drivers. If, for example, patient outcomes improve and unnecessary utilization is reduced, both entities are rewarded with fewer costs. At the same time, governments are incentivizing healthcare organizations for improving care and costs through initiatives like the CMS Innovation Center pilot program, which pushes risk-based agreements for increased care accountability. (2026 Medicare Accountable Care Organization Initiatives Participation Highlights | CMS.)

As such, greater alignment among the two is becoming a greater imperative. The question isn’t really whether collaboration would be beneficial, it’s how to enable it without compromising privacy, security, governance, or competitive boundaries.

The AI Potential

AI in healthcare has an enormous potential both in societal impact as well as at the organization level. For example, the National Bureau of Economic Research published a paper in which it estimated that wider adoption of AI could lead to drastic savings in US healthcare spending, approximately $200-360 billion annually. (2026 Medicare Accountable Care Organization Initiatives Participation Highlights | CMS.)

According to one analysis, “for every $10 billion of payer revenue, AI solutions could save $150 million to $300 million in administrative costs, save $380 million to $970 million in medical costs, and increase revenues by $260 million to $1.24 billion.”(Shubham Singhal and Jessica Lamb, “The AI opportunity: How payers can capture it now,” McKinsey, June 5, 2024.)

For healthcare providers, the rewards can be just as powerful when considering operational efficiencies gained and the improvement of patient outcomes, such as reducing diagnostic errors and readmissions, with one analysis estimating “that generative AI alone could create $60–110 billion in annual value for the American healthcare industry.” ((The Potential Impact of Artificial Intelligence on Healthcare Spending)

While there is generally consensus of that potential, much of this value is contingent on models being trained on sufficient data and being able to generate insights where they can add the most value. Accessing that data and implementing AI in strategic, measured, and secure ways is critical for highly-regulated organizations like health plans and hospitals. Yet, that is also the most difficult challenge.

The Challenge: Collaborating Without Compromise

Payer-provider collaboration has long been a strategic priority, especially as the industry shifts toward value-based care. And, while AI has great potential to support and amplify collaborative efforts, in practice, meaningful data (and therefore AI) collaboration is difficult:

Data sharing is restricted by privacy regulations (HIPAA, GDPR)
Organizations are reluctant to move or expose sensitive data, even under strict agreements
Data is siloed across incompatible systems and formats
Trust barriers limit willingness to collaborate deeply around model or other IP

Traditional approaches, such as centralizing data into a shared warehouse or exchanging datasets, are often slow, risky, and incomplete. What’s needed is a way to bring the analysis to the data, not the data to the analysis.

The Solution: Decentralized Intelligence via Federated Computing

Federated Computing is a privacy-native infrastructure that flips the approach. Instead of pooling raw, sensitive data, organizations collaboratively train AI models or run analytics across decentralized datasets. Each party keeps its data securely behind its firewall, while only model updates or insights are shared. This approach enables:

Data privacy by design - data never leaves the source.
Security and governance control at the source - supports compliance with strict data governance and regulatory requirements.
Scalable collaboration across organizations (ecosystems, not just bilateral data sharing) without exposing sensitive data or IP.
Trust-preserving collaboration where organizations retain complete control over their data and participation.
A continuous federated learning loop where models can continually learn across diverse data and generate deep, multi-dimensional insights.

In fact, Federated Computing is already being used in healthcare. Take the Cancer AI Alliance (CAIA), for example, where four leading cancer centers joined forces to launch a federated AI-driven research platform focused on accelerating cancer discovery.

Ultimately, Federated Computing removes the tradeoff between data access and data protection. For payer-provider ecosystems, this approach unlocks entirely new categories of AI-powered use cases.

High-Impact Use Cases for Payer-Provider Collaboration

1. Unified Risk Stratification for Value-Based Care: Accurate risk stratification is foundational to value-based care, but no single organization has the full picture.

Payers have longitudinal claims and cost data
Providers have rich clinical insights and outcomes data

Federated Computing enables joint model development across these decentralized datasets, resulting in:

More precise identification of high-risk patients
Better targeting of interventions
Improved care management and cost control

The Outcome: Smarter population health strategies without sharing raw patient data.

2. Closing Care Gaps and Improving Chronic Disease Management

Gaps in care, such as missed screenings, delayed follow-ups, and unmanaged chronic conditions, are major drivers of poor outcomes and rising costs. Health plans and providers alike seek ways to address these gaps. Yet, identifying and closing these gaps is often fragmented:

Payers detect gaps based on claims
Providers act on incomplete or delayed information

With a Federated AI approach:

Models can incorporate real-time clinical + claims data
Insights can be generated across both data ecosystems simultaneously
Interventions can be better timed and more personalized

The Outcome: Higher quality scores, better patient outcomes, and more effective chronic disease management, which also leads to lower overall utilization and costs.

3. Joint Fraud, Waste, and Abuse Detection

Fraud, waste, and abuse (FWA) cost the healthcare system billions annually, but detection is often siloed.

Payers identify suspicious billing patterns
Providers may detect anomalies in clinical workflows

Federated AI enables the training and deployment of collaborative detection models that:

Learn from patterns across organizations
Identify cross-system anomalies that would otherwise go unnoticed
Continuously improve without exposing sensitive financial or clinical data

The Outcome: Stronger, more proactive defense against FWA with shared intelligence.

Collaboration as a Competitive Advantage

Healthcare organizations don’t need more of their own data, they need better ways to use the data they already have and to expand access to the right data for filling gaps. They then need the infrastructure to securely train and deploy AI on those data. As payer-provider relationships evolve, collaboration is becoming a strategic shift and a key enabler. Organizations that embrace federated, collaborative AI will be able to:

Deliver more coordinated, patient-centered care
Succeed in value-based care models
Unlock insights that competitors cannot access in isolation

Federated Computing provides the foundation for secure, scalable, and impactful payer-provider collaboration. By enabling AI without data movement, it transforms long-standing barriers into opportunities. The future of healthcare AI isn’t centralized, it’s collaborative.