Health insurers face mounting pressure to automate complex workflows across claims processing, provider credentialing, and member services—all while maintaining HIPAA compliance and managing multi-user authorization at scale. With 46% of physicians receiving any value-based payments and healthcare systems struggling to transmit resources to frontline practices, payers need infrastructure that lets AI agents actually take action across fragmented, domain specific systems. Arcade's MCP runtime and AI tool-calling platform provides secure multi-user authorization and integrations required to move beyond passive automation into true agentic workflows for multiplan coordination and independent agent management.
Key Takeaways
- Healthcare payers managing multiplan networks face severe coordination challenges, with 133 organizations representing 772 practices having participated in complex multi-stakeholder models requiring aligned payment methodologies and data sharing
- Commercial payer engagement in value-based care remains insufficient despite alignment commitments, with most making minimal enhanced primary care payments and failing to shift away from fee-for-service
- Risk-adjusted payment methodologies incorporating social determinants created 12.5x payment differentials between highest and lowest-risk populations, demonstrating sophistication required for equity-sensitive automation
- Multi-user authorization at enterprise scale requires managing thousands of independent agent credentials, OAuth tokens, and granular permission scoping across carrier systems without exposing sensitive tokens
- Audit trail requirements and HIPAA compliance demand zero token exposure to AI models, with every agent action logged for regulatory reporting
- State-led initiatives like Washington's Multi-Payer Collaborative demonstrate viable paths for operational alignment across eight payers with publicly documented quality measure and payment commitments
Use Cases: Multi-Payer Coordination and Claims Automation
Managing Complex Multi-Payer Attribution and Payment Flows
Healthcare payers participating in advanced primary care models confronted operational complexity requiring simultaneous management of six distinct payment types: upfront infrastructure payments of $145,000, enhanced services payments ranging $2-$25 per beneficiary per month using dual risk adjustment, prospective primary care payments at varying percentages of historical spending, performance incentive payments up to 60% of primary care revenue, e-consult codes at $40 per service, and ambulatory co-management codes at $50 monthly.
The enhanced services payment alone utilized a sophisticated 5x5 risk tier matrix combining Low-Income Subsidy status with four clinical risk tiers based on CMS-HCC scores and four social risk tiers using Area Deprivation Index at the Census block group level. This created dramatic payment variation—practices serving high-risk populations received $25 PBPM compared to $2 PBPM for lowest-risk populations, representing the 12.5x differential necessary for equity-sensitive resource allocation.
For AI agents handling these calculations across multiple payers, multi-user authorization becomes critical. Each calculation must access patient-specific HCC scores, ADI data, historical claims, and attribution rosters while maintaining strict permission boundaries. Traditional login-centric approaches fail when agents need to act on behalf of thousands of independent insurance agents or broker portal users, each with different access scopes across carrier systems.
Provider Credentialing and Network Directory Automation
Provider network management presents massive data synchronization challenges. Payers must maintain accurate directories while validating practitioner licenses, verifying hospital affiliations, processing CAQH data extractions, confirming NPI numbers, and managing payer enrollment workflows. Manual credentialing committee processes create bottlenecks when practices need privileges across multiple plan networks.
The Washington Multi-Payer Collaborative committed eight health plans plus the state Medicaid program to aligned provider recognition standards with three-tiered levels tied to payment model eligibility. This required synchronized data across Community Health Plan of Washington, Coordinated Care, Kaiser Permanente Washington, Molina Healthcare, Premera Blue Cross, Regence BlueShield, UnitedHealthcare, Wellpoint Washington, and the Washington State Health Care Authority's Medicaid program.
AI agents automating credentialing workflows need properly scoped, multi-user authorization to: CAQH ProView for primary source verification, state medical board APIs for license validation, DEA databases for controlled substance access checks, hospital medical staff offices for privilege attestation, and each payer's internal credentialing systems. The agent must maintain separate authorization contexts for each data source while ensuring audit trails document every verification step for compliance reporting.
Arcade's MCP runtime for multi-user authorization handles the token and secret management complexity of multi-source credentialing workflows, supporting the multiple flows required when agents act across fragmented, domain specific healthcare data systems on behalf of credentialing staff with varying permission levels.
Independent Insurance Agent Portal Access and Commission Tracking
Independent insurance agents accessing multiple carrier portals face authentication friction when quoting across carriers, enrolling members, tracking commissions, comparing policy options, and managing multi-carrier client portfolios. Each carrier maintains separate login credentials, session timeouts, and permission structures.
For AI agents assisting independent agents with these workflows, the multi-user authorization challenge intensifies. The agent needs delegated access to each agent's credentials across multiple carrier systems without ever exposing those credentials to the language model. When an agent asks their AI assistant to "pull commission reports from all my carriers for Q4," the system must:
- Establish per-agent, per-carrier access using that specific agent's credentials
- Maintain separate authorization contexts for each carrier
- Respect granular permission scoping (read-only access to commissions, no policy modification rights)
- Generate comprehensive audit trails showing which agent accessed which carrier data at what time
- Ensure tokens are encrypted at rest and never visible to the LLM
This represents the core multi-user authorization problem that standard chatbots cannot solve. Arcade's MCP runtime and user management capabilities enable AI agents to securely act on behalf of thousands of independent agents, with token and secret management plus fine-grained permission scoping across carrier systems—without exposing credentials to the language model.
Building equivalent multi-user authorization for independent agents without Arcade would require payers to:
- Implement and maintain separate OAuth flows for every carrier portal and distribution channel
- Build a centralized, encrypted token vault with per-agent, per-carrier scoping and rotation policies
- Create a consistent permission model that maps thousands of agent roles to carrier-specific access levels
- Instrument end-to-end audit trails for every commission query and portal action across all carriers
For most organizations, this represents years of engineering investment and ongoing security maintenance that falls far outside their core business.
Prior Authorization and Eligibility Verification Workflows
Prior authorization workflows create massive administrative burden, requiring verification of: member eligibility and benefits, diagnosis code validation against coverage policies, procedure code authorization requirements, in-network provider confirmation, coordination of benefits across multiple payers, and documentation submission to medical review staff.
Research identifies that lack of payer alignment on measures and reporting requirements creates documentation burden exceeding patient care time. AI agents automating prior authorization must access real-time eligibility databases, clinical documentation systems, payer authorization platforms, and provider credentialing databases simultaneously.
The multi-user authorization requirements become complex when the same AI agent handles requests for multiple practices across multiple payers. Each authorization check requires appropriate credentials for that specific payer-practice relationship, with audit trails documenting who initiated the authorization request and what data was accessed.
Best Practices: Operationalizing Multi-Payer Collaboration at Scale
Establishing Publicly Documented Multi-Payer Commitments with Accountability Mechanisms
The failure mode of non-binding payer participation agreements provides critical lessons. While 133 organizations representing 772 practices signed binding participation agreements committing to model requirements, payer Letters of Intent and Memoranda of Understanding were explicitly non-binding. This created provider uncertainty—practices committed to significant practice transformation investments without assurance that promised commercial payer participation would materialize.
A research found that most commercial payers have not sufficiently engaged, and those that have chosen to make relatively small enhanced primary care payments. The gap between theoretical alignment commitments and actual meaningful engagement damaged provider trust and contributed to inadequate primary care funding for delivery transformation.
Washington State's approach offers a superior model. Their Washington's 2024 MOU publicly codified commitments to specific alternative payment policies with concrete deliverables:
- Alignment on ten core quality measures from the Washington Primary Care Core Measure Set
- Minimum 50th percentile performance benchmarks for quality incentive eligibility
- Standardized quality reporting formats to reduce provider documentation burden
- Phased implementation timeline with specific start dates
- Recognition Level requirements (1-3) tied to payment model eligibility through the Primary Care Practice Recognition Program
For AI agents operationalizing these multi-payer workflows, the documented nature of commitments determines system design. Agents can be configured to enforce minimum payment levels, validate quality measure alignment, verify benchmark calculations, and flag non-compliant payer behaviors for executive review.
Implementing Risk-Adjusted Payment Methodologies That Address Health Equity
The most sophisticated payment approach used dual risk adjustment combining clinical and social risk factors. Payments incorporated:
Clinical Risk Tiers (using CMS-HCC risk scores):
- Tier 1: Lowest clinical risk quartile
- Tier 2: Second quartile
- Tier 3: Third quartile
- Tier 4: Highest clinical risk quartile
Social Risk Tiers (using Area Deprivation Index):
- Tier 1: Lowest social risk quartile (ADI 1-25)
- Tier 2: Second quartile (ADI 26-50)
- Tier 3: Third quartile (ADI 51-75)
- Tier 4: Highest social risk quartile (ADI 76-100)
Low-Income Subsidy Status: Automatic assignment to highest payment tier regardless of other risk factors
This created payment amounts that accurately reflected practice costs for serving vulnerable populations. Track 3 payments ranged from $2 PBPM for lowest-risk (Tier 1 clinical, Tier 1 social, non-LIS) to $25 PBPM for highest-risk (LIS status or Tier 4 clinical + Tier 4 social).
AI agents calculating these payments need authenticated access to: CMS-HCC risk score databases, Census block group ADI data, LIS enrollment files, beneficiary attribution rosters, and historical payment records. The calculation must occur in real-time as beneficiaries move between risk tiers, with audit trails documenting every payment adjustment.
Arcade's tool execution infrastructure enables agents to call multiple authenticated APIs in sequence—retrieving HCC scores, matching beneficiaries to Census geographies, applying ADI tiers, and calculating final payments—while maintaining proper authorization contexts throughout the chain of tool calls.
Addressing Health System Resource Transmission Accountability
A critical finding from Commonwealth Fund research revealed that "health systems participating in VBP models not reliably transmitting resources to frontline primary care practices, instead using resources for system-level priorities or hiring care management staff at corporate offices rather than embedded in practices."
This represents a financial leakage problem requiring operational monitoring. AI agents can track resource flows by:
- Monitoring payment disbursements from payers to health systems
- Comparing contracted payment amounts to actual funds reaching practice sites
- Tracking employment of care managers (system corporate offices vs. embedded in practices)
- Flagging discrepancies between promised and delivered practice-level investments
- Generating executive reports on resource transmission compliance
The audit trail requirements become substantial. Each resource flow must be documented with: originating payer, contracted payment amount, receiving health system, downstream practice allocation, actual utilization by practice sites, and variance from contractual commitments.
For payers serious about ensuring investments reach intended destinations, AI agents with comprehensive audit capabilities provide the transparency necessary to hold health systems accountable. Every agent action accessing financial data, employment records, or practice-level utilization metrics must be logged with proper attribution to the user who initiated the inquiry.
Managing State-Specific Implementation Variations
The eight participating states (Colorado, Massachusetts, Minnesota, New Jersey, New Mexico, New York, North Carolina, Washington) developed varied approaches to Medicaid alignment, creating implementation complexity for multi-state payers.
North Carolina proposed standardizing Advanced Medical Home performance incentives across all Standard and Tailored Plans with:
- Aligned subset of key AMH measures across five Prepaid Health Plans
- Consistent member attribution methodology for quality calculation
- Uniform measure targets and benchmarks
- Consistent incentive calculation methodology not contingent on total cost of care
For national payers operating across multiple state Medicaid contracts, AI agents need state-specific configuration management. The same agent handling quality measure reporting must apply different measure sets, attribution algorithms, benchmark calculations, and incentive formulas based on the state context of each request.
This requires sophisticated rule engines within the agent architecture. When a user asks the agent to "generate quality reports for all our Medicaid contracts," the system must:
- Identify which states have active contracts
- Apply state-specific quality measure sets
- Use correct attribution methodologies for each state
- Calculate benchmarks using state-defined standards
- Format reports according to state requirements
- Maintain separate authorization contexts for each state Medicaid agency
Trends: Operational Transformation Beyond Federal Model Participation
State-Led Primary Care Investment Mandates
Despite the early termination of the federal model on June 30, 2025, state-level initiatives continue driving payer behavior. California approved a requirement that 15% primary care spending be allocated to total medical expenses by 2034, with annual 0.5-1 percentage point increases from 2025-2033.
Rhode Island established comprehensive primary care expenditure definitions effective March 2025, requiring all claims-based and non-claims-based payments to primary care practices for services delivered to Rhode Island residents be measured. Multiple states joined the Primary Care Investment Workgroup hosted by the Primary Care Collaborative, creating momentum for regulatory intervention if voluntary payer action proves insufficient.
For payers, this trend demands sophisticated spending tracking across: claims-based payments (fee-for-service, capitation, case rates), non-claims-based payments (infrastructure investments, care management fees, quality incentives), practice types (independent, health system-employed, federally qualified health centers), and demographic stratification (race, ethnicity, geography).
AI agents handling this reporting need authenticated access to: claims databases, contract management systems, payment processing platforms, practice taxonomy files, and demographic stratification tools. The calculations must separate primary care spending from specialty, hospital, pharmacy, and behavioral health expenditures while maintaining HIPAA-compliant data handling.
Multi-Stakeholder Data Sharing and Infrastructure Collaboration
The five alignment areas established for payer partnerships provide a framework still relevant for collaborative initiatives: performance measurement and reporting, payment approaches shifting from fee-for-service, timely and consistent data sharing, learning supports and technical assistance, and collaboration on regional data infrastructure.
Data sharing emerged as particularly challenging. Dr. Howard Haft emphasized that payer participation will be difficult to maintain if reliable data, performance monitoring, and objective evidence are insufficient to justify ongoing investment.
AI agents facilitating multi-payer data sharing must navigate complex multi-user authorization scenarios:
- Each payer maintains separate data governance policies
- Practices need aggregated views across all payers without individual payer identifiability
- State agencies require stratified reporting from all participating payers
- Patients must consent to data sharing under varying state privacy laws
- Each data access must be logged for HIPAA audit trails
The technical infrastructure requirements mirror challenges faced by AI platforms managing multi-user authorization at scale. When agents access data from eight different payer systems (as in Washington's collaborative), they need separate authentication contexts, permission scoping aligned with each payer's governance policies, and audit trails documenting every cross-payer data aggregation.
Primary Care Physician Participation Decline and Retention Challenges
Despite evidence that ACOs with more than 50% primary care physicians generate 2.4x savings compared to those with fewer than 50% PCPs, the percentage of Medicare beneficiaries in primary care-centric ACOs declined from 27.5% in 2017 to 24.0% in 2022.
The estimated 36% revenue reduction for practices following abrupt model terminations creates financial instability. Practices faced uncertain migration paths to alternative arrangements including Medicare Shared Savings Program tracks, ACO REACH (itself ending in 2026), or Medicare Advantage risk contracts.
For payers, this creates retention risk and market opportunity. AI agents can support transition management by:
- Identifying high-performing primary care practices at risk of leaving value-based arrangements
- Modeling alternative payment structures with comparable revenue
- Generating personalized transition proposals for each practice
- Tracking practice migration patterns across competitor payer arrangements
- Alerting executives to retention risks before practices sign with competitors
These workflows require authenticated access to practice performance data, contract terms, competitive intelligence, and financial modeling tools. The agent must act on behalf of network management executives with appropriate permissions to access commercially sensitive data while maintaining audit trails for compliance review.
Commercial Payer Accountability and Transparency Requirements
The persistent gap between commercial payer alignment commitments and actual engagement creates pressure for accountability mechanisms. California's 2024 Medi-Cal contracts now require managed care plans to report primary care spending as a percentage of total spending stratified by practice type, creating transparency that exposes inadequate investment.
Multiple states implementing NCQA Health Equity Accreditation requirements (California, Delaware, Georgia, Michigan, New Mexico, Oklahoma, Pennsylvania, Rhode Island) established parallel compliance pathways with concrete deliverables and consequences for non-compliance.
AI agents supporting transparency reporting need to:
- Calculate primary care spending percentages using state-specific definitions
- Stratify spending by practice type (independent, system-employed, FQHC)
- Track progress toward multi-year spending targets
- Generate standardized reports for state regulatory submission
- Flag variance from contractual commitments for executive escalation
These transparency requirements align with the audit trail, token, and secret management capabilities that Arcade's infrastructure provides—ensuring tokens and credentials are handled securely while calculations and data access remain in the underlying payer systems, and every report generation is logged with proper attribution to the user who initiated the request.
Strategic Implications for Health Insurance Leadership
Health insurers navigating complex multi-payer coordination, independent agent management, and regulatory compliance requirements need infrastructure that enables AI agents to take authenticated action across fragmented systems. Many payers orchestrate these agent workflows with LangGraph, a framework built on LangChain for designing stateful, multi-step AI processes. In that architecture, LangGraph/LangChain govern the agent’s reasoning and workflow logic, while Arcade acts as the MCP runtime and tool catalog that provides fine-grained, delegated multi-user authorization and scoped permissions so agents can safely take real actions against claims, credentialing, and commission systems. Teams can also use Arcade’s MCP framework to build tools for proprietary payer platforms that never appear in any public catalog, while still benefiting from the same controlled, production-ready authorization model.
When thousands of independent insurance agents, credentialing staff, network managers, and compliance officers need AI assistance with their workflows, the system must maintain separate authorization contexts, enforce granular permission scoping, encrypt tokens at rest, generate comprehensive audit trails, and ensure zero token exposure to language models.
The most successful payers start by implementing a single, high-value use case—such as prior authorization automation or independent agent commission workflows—prove out multi-user authorization, audit trails, and governance in production, and only then scale to additional claims, credentialing, and payment scenarios. This staged approach limits risk while building organizational confidence in MCP-powered agents.
the question is not whether agents can chat about healthcare operations, but whether they can securely execute multi-user authorized actions across the hundreds of enterprise platforms that payers depend on daily. That capability gap is what separates conversational AI from true operational automation.
Frequently Asked Questions
How do health insurers manage OAuth token lifecycle across thousands of independent agent credentials without exposing sensitive credential data?
Independent insurance agents accessing multiple carrier portals create massive token management challenges. Each agent maintains separate credentials across carriers, with varying session timeouts and refresh requirements. Enterprise-grade solutions encrypt tokens at rest, handle automatic refresh cycles, and ensure zero token exposure to AI models while maintaining proper audit trails documenting which agent accessed which carrier system at what timestamp.
What multi-user authorization approaches support AI agents acting on behalf of credentialing staff with different permission levels across payer systems?
Credentialing workflows require simultaneous access to CAQH ProView, state medical boards, DEA databases, hospital medical staff offices, and internal payer systems—each with distinct authentication requirements. Proper implementation uses managed multi-user authorization with multiple OAuth flows behind the scenes, granular permission scoping aligned with staff roles, and separate authorization contexts for each data source to prevent privilege escalation.
How can payers ensure audit trails meet HIPAA requirements when AI agents access protected health information across multiple systems?
HIPAA compliance demands comprehensive logging of every access to protected health information, including user identity, timestamp, data accessed, and purpose. AI agent audit trails must document the complete chain of tool executions—which APIs were called, what data was retrieved, how it was processed, and which user initiated the workflow. Systems should provide immutable audit logs with proper attribution, encrypted storage, and retention periods meeting regulatory requirements.
What technical infrastructure enables risk-adjusted payment calculations incorporating HCC scores, ADI data, and LIS status in real-time?
Real-time payment calculations require properly scoped API access, governed by multi-user authorization, to CMS risk score databases, Census Bureau geographic data, beneficiary enrollment files, and historical claims. AI agents must orchestrate multiple tool calls in sequence—retrieving HCC scores, matching beneficiaries to Census block groups, applying ADI tier logic, checking LIS enrollment status, and calculating final payments using state-specific formulas. The infrastructure must maintain authorization contexts throughout multi-step workflows while ensuring each API call uses appropriate credentials.
How do multi-state payers handle different quality measure sets, attribution methodologies, and benchmark calculations across state Medicaid contracts?
AI agents supporting multi-state operations need sophisticated configuration management applying state-specific rules based on request context. When generating quality reports, the system must identify the relevant state contract, apply that state's measure set, use correct attribution algorithms, calculate benchmarks per state standards, and format output meeting state submission requirements. This requires rule engines managing state-specific business logic with proper version control as state policies evolve.
