Agentic AI for Prior Authorization: How AKASA, CombineHealth, and UiPath Are Changing Healthcare

Prior authorization has never been only a form-filling problem. A successful authorization may require a team to identify whether approval is needed, retrieve the correct payer policy, search the EHR for supporting evidence, complete payer-specific forms, attach records, submit the request, monitor its status, answer additional-information requests, coordinate peer-to-peer review, and connect the final authorization to scheduling and billing.

Traditional automation can accelerate individual steps. Agentic AI attempts something more ambitious: coordinating the entire chain of work. That distinction matters in 2026.

CMS prior authorization process requirements are already taking effect, while federally mandated Prior Authorization APIs for impacted payers generally become operational in 2027. Health systems, payers, and revenue cycle vendors therefore need solutions that can work across both emerging FHIR APIs and today’s fragmented mix of payer portals, EDI transactions, fax, telephone, and manual clinical review.

AKASA, CombineHealth, and UiPath represent three different approaches to this transition:

• AKASA applies healthcare-specific generative AI and targeted automation to clinical documentation retrieval and authorization-status workflows.
• CombineHealth is developing a network of specialized revenue cycle agents, including a dedicated prior authorization agent.
• UiPath uses enterprise orchestration to coordinate AI agents, software robots, applications, business rules, and human decisions.

Understanding those differences is essential. 

The right solution depends less on which vendor uses the strongest “agentic AI” language and more on which operating model fits the organization’s data, workflows, technical maturity, and risk tolerance.

Key Takeaways

• Agentic AI for prior authorization combines reasoning, data retrieval, tool use, workflow execution, status tracking, and exception handling.
• The safest enterprise model is bounded autonomy, not unsupervised clinical decision-making.
• AKASA is strongest in provider-side clinical documentation support and authorization-status automation.
• CombineHealth is positioning prior authorization as one component of a coordinated AI revenue cycle workforce, although its dedicated Stephanie agent is described publicly as upcoming.
• UiPath is best understood as an enterprise automation and orchestration platform rather than a single-purpose prior authorization product.
• FHIR APIs will reduce some administrative friction, but healthcare organizations will still need portal, fax, EDI, document, and human-review workflows.
• The most important implementation metrics are not simply “tasks automated.” Organizations must measure completeness, exception rates, turnaround time, authorization-related denials, appeal outcomes, and patient access.

What Is Agentic AI for Prior Authorization?

Agentic AI for prior authorization is an automation model in which specialized software agents can interpret a workflow goal, gather relevant administrative and clinical information, invoke approved systems or tools, execute workflow steps, monitor progress, and escalate exceptions to qualified personnel.

The system does not merely generate a prior authorization letter.

It may also determine which information is missing, retrieve documentation from the EHR, compare the request with payer requirements, populate a submission, select an approved communication channel, track the case, and initiate the next action.

That makes agentic AI different from three earlier technologies.

Rules-Based Workflow Automation

Rules-based workflow systems follow predefined logic:

If payer equals X and procedure code equals Y, route the request to queue Z.

These systems are reliable when the inputs, rules, and exceptions are predictable. They become difficult to maintain when payer requirements change frequently or when clinical evidence is buried in unstructured notes.

Robotic Process Automation

Robotic process automation, or RPA, imitates user actions in software interfaces. A bot can log into a payer portal, enter patient information, download a status response, and update an EHR work queue.

RPA is valuable when APIs are unavailable.

But it generally does not understand why a document supports medical necessity or decide how to respond when the portal returns an unexpected message.

Generative AI Assistants

A generative AI assistant can summarize a patient record, retrieve supporting statements, answer policy questions, or draft a letter. It helps a person complete the task.

An agentic system goes further by taking approved actions across the workflow.

Why Prior Authorization Needs More Than Basic Automation

The burden is not concentrated in one screen or department.

It spans patient access, utilization management, clinical documentation, scheduling, revenue cycle operations, payer relations, and appeals.

Physicians continue to report significant administrative and clinical consequences. In an AMA survey, physicians reported completing dozens of prior authorizations each week, spending roughly two working days on them, and experiencing treatment delays, care abandonment, and workforce burnout.

The underlying workflow has five structural problems.

1. Authorization Requirements Are Difficult to Identify

A requirement can depend on:

• The patient’s current benefit plan
• The payer and product
• The requested service
• Diagnosis and procedure codes
• Site of service
• Provider network status
• Frequency limits
• Previous treatments
• Medical-benefit versus pharmacy-benefit coverage
• State and federal requirements

A static list is rarely enough. The system must determine the applicable requirement for the individual request and the patient’s active coverage.

2. Clinical Evidence Is Fragmented

The information needed to support medical necessity may appear across:

• Progress notes
• Imaging reports
• Laboratory results
• Medication history
• Previous procedures
• Therapy records
• Discharge summaries
• Scanned documents
• Referral notes
• External records

A specialist may spend substantial time searching the chart before completing the payer submission.

An AI system must therefore retrieve evidence accurately, preserve the source context, and avoid treating an unsigned, outdated, or unrelated note as current clinical evidence.

3. Payer Workflows Remain Heterogeneous

The same health system may submit prior authorizations through:

• FHIR APIs
• X12 278 transactions
• Payer portals
• Clearinghouses
• Telephone calls
• Fax
• Secure email
• Vendor-specific applications

CMS is moving affected programs toward electronic Prior Authorization APIs, but it has also allowed flexibility for FHIR-only or combined FHIR and X12 implementations. 

Organizations must therefore prepare for a hybrid environment rather than assume that one transport standard will immediately replace every channel.

4. The Workflow Is Stateful

Prior authorization is not a single transaction.

A request may remain open for days while the payer asks for additional information, changes its status, schedules a peer-to-peer review, approves only part of the service, or attaches an expiration condition.

An agent must understand the case state:

• Not started
• Requirement confirmed
• Documentation incomplete
• Ready for submission
• Submitted
• Pending payer review
• Additional information requested
• Peer-to-peer required
• Approved
• Partially approved
• Denied
• Appealed
• Expired

Without state management, automation creates duplicate submissions, missed deadlines, unnecessary follow-ups, and incorrect scheduling decisions.

5. Clinical Judgment Cannot Be Treated as an Administrative Shortcut

A system may summarize records or map evidence to policy criteria.

That does not mean it should independently deny care.

The risk is visible in recent federal oversight findings. In June 2026, HHS-OIG reported that the Medicare Advantage organizations it reviewed overturned 95% of appealed skilled-nursing-facility prior authorization denials. A separate OIG review found substantial overturn rates for long-term acute care and inpatient rehabilitation denials.

These findings do not establish that every overturned denial resulted from AI.

They demonstrate why speed and consistency alone are insufficient. Prior authorization systems also require defensible criteria, clinical oversight, traceable evidence, and an effective appeal pathway.

Why 2026 Is an Inflection Point

The market is changing because regulation, interoperability, and AI capabilities are converging.

CMS-0057-F requires impacted payers to improve prior authorization operations and implement new APIs. The rule applies to Medicare Advantage organizations, certain Medicaid and CHIP programs and plans, and Qualified Health Plan issuers on the federally facilitated exchanges.

Beginning in 2026, relevant operational requirements include:

• Specific reasons for denied prior authorization requests
• Public reporting of defined prior authorization metrics
• Decision timeframes of 72 hours for expedited requests and seven calendar days for standard requests for most impacted payers

The Prior Authorization API requirements generally begin in 2027. Those APIs must help providers determine requirements, identify required documentation, submit requests, and receive approval, denial, or additional-information responses.

The current final rule primarily addresses non-drug items and services. In April 2026, CMS proposed extending electronic prior authorization requirements to drugs covered under medical benefits and adopting additional FHIR-related standards. Those provisions remain proposals unless and until finalized.

This creates a two-speed market.

Organizations must prepare for standards-based electronic authorization while continuing to operate existing manual and portal-based workflows.

Agentic AI is emerging as the coordination layer between those two environments.

How an Agentic Prior Authorization Workflow Works

A technically complete agentic workflow should contain at least ten stages.

Step 1: Detect the Authorization Trigger

The workflow begins when a relevant event occurs, such as:

• Physician enters an order
• Referral is received
• Procedure is scheduled
• Treatment plan is created
• Prescription is initiated
• Payer or coverage record changes

The agent should receive structured context from the EHR, practice management system, referral platform, or scheduling system.

It should not rely solely on staff launching a separate application.

Step 2: Confirm Patient and Coverage Data

The system verifies:

• Patient demographics
• Member identifier
• Active coverage
• Plan and product
• Coordination of benefits
• Provider participation
• Benefit limitations
• Applicable service dates

Eligibility information must be timestamped because coverage can change between scheduling, service delivery, and billing.

Step 3: Determine Whether Authorization Is Required

The agent checks the planned service against current payer requirements.

In a standards-based workflow, Coverage Requirements Discovery can help identify whether authorization is required and what supporting information is needed. Documentation Templates and Rules can help obtain structured clinical data, while Prior Authorization Support facilitates the request and response exchange. CMS identifies these Da Vinci implementation guides as part of the electronic prior authorization implementation landscape.

When a payer does not expose those capabilities, the system may need to use a payer portal, policy repository, clearinghouse connection, or maintained rules service.

Step 4: Build a Canonical Authorization Case

The platform should create a normalized case object independent of any payer channel. The case should contain:

• Patient and coverage identifiers
• Ordering and servicing providers
• Diagnosis and procedure codes
• Requested quantity and service dates
• Site of service
• Urgency
• Relevant clinical facts
• Required documents
• Submission channel
• Payer reference number
• Current status
• Deadlines
• Complete action history

This canonical model prevents portal-specific logic from becoming the organization’s master record.

Step 5: Retrieve Clinical Evidence

The agent searches approved data sources for evidence matching the payer’s requirements. A retrieval process may inspect:

• FHIR resources
• Clinical notes
• C-CDA documents
• Scanned PDFs
• Imaging and laboratory results
• Medication administration records
• Prior procedures
• External documents

Every extracted fact should retain its provenance.

For example, the output should identify the source note, author, date, section, and relevant passage rather than generate an unsupported statement such as “conservative treatment failed.”

Step 6: Evaluate Completeness

Before submission, the system checks whether the packet contains all required administrative and clinical elements.

A completeness check may detect:

• Missing diagnosis codes
• Inconsistent service dates
• Absent conservative-treatment history
• Missing imaging results
• Incorrect site of service
• Unverified benefits
• Unsigned notes
• Expired documentation
• Missing provider identifiers

This stage should distinguish between a hard stop and a warning.

An absent member identifier may block submission. A potentially useful but optional clinical note may only create a recommendation.

Step 7: Route the Case by Risk and Complexity

Not every authorization should follow the same path. A low-risk request with complete structured evidence may proceed automatically. 

A complex oncology request, experimental treatment, ambiguous policy match, or potential adverse determination should be routed to a qualified reviewer. Useful routing signals include:

• Confidence score
• Missing documentation
• Clinical complexity
• Dollar value
• Urgency
• Payer history
• Probability of denial
• State requirements
• Need for licensed clinical review

The objective is not to remove people from the workflow. It is to concentrate their attention where judgment is needed.

Step 8: Submit Through the Best Available Channel

The orchestration layer selects an approved submission mechanism:

• FHIR Prior Authorization API
• X12 278
• Payer portal
• Clearinghouse
• Fax
• Telephone workflow

Channel selection should be deterministic and auditable.

A generative model should not freely choose an external destination or transmit protected health information outside approved connections.

Step 9: Monitor and Respond

After submission, the agent monitors the request according to payer-specific timing rules. It may:

• Query an API
• Check a portal
• Interpret a status message
• Download correspondence
• Update the EHR
• Notify scheduling staff
• Request missing documentation
• Escalate a delayed case
• Prepare a peer-to-peer summary

The system should avoid excessive portal polling. Monitoring schedules should reflect payer behavior, service urgency, and known response patterns.

Step 10: Close the Revenue Cycle Loop

An authorization is not complete when the payer says “approved.” The result must be connected to:

• Scheduling
• Patient communication
• Authorization number
• Approved codes
• Approved quantity
• Site of service
• Effective period
• Claim edits
• Charge capture
• Expiration monitoring
• Appeal management

Otherwise, a valid approval may still result in an authorization-related denial.

How AKASA Approaches Prior Authorization

AKASA’s approach is centered on provider revenue cycle operations. Its current public portfolio separates two important functions:

1. AI Advisor, which helps staff locate and interpret clinical documentation.
2. Auth Status, which automates authorization-status retrieval and documentation.

Clinical Documentation Support

AKASA describes AI Advisor as a healthcare revenue cycle research assistant connected to live patient data. Staff can use it to locate clinical facts, answer payer questions, prepare peer-to-peer cases, draft letters, and retrieve supporting documentation without manually reviewing the entire chart. The product emphasizes citations, source visibility, role-based access, audit logging, and user oversight.

AKASA originally introduced Authorization Advisor as an assistant embedded in the staff workflow while the user works in a payer portal. The company reported that the system could identify more relevant clinical documents and reduce time spent preparing authorization submissions. 

These figures are vendor-reported and should be independently validated against an organization’s specialties, payer mix, and EHR configuration.

This model addresses one of the hardest provider-side problems: finding the evidence that demonstrates medical necessity.

It is especially relevant when the record is large, the clinical facts are unstructured, or non-clinical authorization specialists need to understand complex documentation.

Authorization-Status Automation

AKASA Auth Status uses AI-supported automation and an expert-in-the-loop model to check payer portals, interpret the response, and document the status in the provider workflow.

In its Montage Health case study, AKASA reported:

• 5,663 automated authorization-status checks in one year
• Coverage across nine payers and six work queues
• A 22% reduction in authorization work-queue volume

The case study also describes an important operational lesson. Initial status checks sometimes occurred too early, creating unnecessary “authorization not found” results. AKASA and the health system adjusted the timing based on payer- and queue-specific data.

That example demonstrates why healthcare automation cannot be optimized only at the task level.

The system must learn the operating characteristics of each payer and workflow.

Where AKASA Fits Best

AKASA is likely to be most relevant for:

• Large provider organizations
• Health systems using complex EHR environments
• High-volume authorization teams
• Specialties with extensive documentation
• Organizations struggling with chart review and payer-status work
• Revenue cycle teams that want embedded assistance rather than a new standalone workflow

How CombineHealth Approaches Prior Authorization

CombineHealth uses an AI-workforce model.

Instead of positioning one application as responsible for the entire revenue cycle, the company describes specialized agents assigned to distinct functions.

Relevant agents include:

• Mark, for eligibility and benefits work
• Amy, for coding and documentation analysis
• Penny, for payer-policy review
• Stephanie, for prior authorization
• Adam, for claim-status and denial workflows
• Rachel, for appeals

The Multi-Agent Operating Model

In a coordinated prior authorization workflow:

1. Mark verifies coverage and benefits.
2. Stephanie determines that authorization is needed.
3. Amy or another documentation function analyzes clinical and coding data.
4. Penny compares the case with current payer policies.
5. Stephanie prepares and submits the request.
6. The system monitors the case and escalates delays.
7. Downstream agents handle denials or appeals.

This is conceptually attractive because prior authorization does not operate in isolation.

Eligibility errors create authorization errors. Documentation gaps create medical-necessity denials. Authorization details that do not reach billing create claim denials.

A coordinated agent architecture can share context across those boundaries.

Stephanie’s Proposed Role

CombineHealth publicly describes Stephanie as an upcoming AI prior authorization specialist.

According to the company, the planned agent is intended to:

• Detect authorization needs using payer rules, benefits, diagnoses, and procedure codes
• Prepopulate forms
• Submit through portals, phone, and fax
• Monitor open cases
• Escalate stalled requests
• Call payer representatives where necessary

CombineHealth states that Stephanie is designed to work across more than 70 payer portals. These are vendor descriptions of planned capabilities, not equivalent to independently verified production outcomes across 70 payers.

That maturity distinction is important.

Organizations should not evaluate an upcoming agent using the same procurement criteria as a capability supported by multiple live customer deployments and published outcome data.

Where CombineHealth Fits Best

The CombineHealth model may appeal to:

• RCM companies
• Medical billing organizations
• Multi-specialty provider groups
• Organizations seeking multiple coordinated automation roles
• Teams with substantial portal, calling, fax, and follow-up work
• Buyers seeking automation beyond prior authorization alone

How UiPath Approaches Prior Authorization

UiPath takes a platform and orchestration approach.

It is not primarily a vertical prior authorization application. It provides an enterprise environment in which organizations can combine:

• AI agents
• RPA robots
• APIs
• Document processing
• Rules
• Business process models
• Human tasks
• Monitoring
• Governance

In February 2026, UiPath announced healthcare-specific agentic solutions covering medical-record summarization, denial prevention and resolution, and prior authorization. 

The prior authorization solution automates eligibility and benefit validation, maps clinical data to medical-necessity requirements, routes cases based on complexity, and provides status information. UiPath is working with Genzeon on the prior authorization offering.

Orchestration Is the Core Differentiator

A UiPath implementation can assign different work to different execution mechanisms.

For example:

• An API retrieves structured coverage data.
• A robot logs into a payer portal.
• Document processing extracts information from a fax.
• An AI agent summarizes the clinical record.
• A rules service evaluates submission completeness.
• Maestro maintains the case state.
• A nurse reviews an ambiguous clinical match.
• Another robot writes the result back to the EHR.

This provides flexibility for enterprise organizations with heterogeneous systems.

It also avoids the error of using generative AI for deterministic work. A member identifier should be transferred exactly, not “reasoned about” by a language model.

Production Evidence

UiPath’s PromptCare case study describes the use of bots and agents across revenue cycle functions, including prior authorization and reauthorization. 

PromptCare reported that some tasks previously requiring 20 to 30 minutes, or occasionally days, could be completed in less than three minutes. It also reported a reduction in manual labor and faster turnaround for new-patient issues. These are customer and vendor-reported results from a wider automation program, not a controlled evaluation of every prior authorization type.

Where UiPath Fits Best

UiPath is likely to be most relevant for:

• Enterprise health systems
• Payers
• Large RCM organizations
• Organizations with an established automation center of excellence
• Environments containing many legacy systems
• Buyers that need one orchestration platform across multiple departments
• Organizations planning to build or customize their own agentic workflows

AKASA vs. CombineHealth vs. UiPath

Evaluation Area	AKASA	CombineHealth	UiPath
Primary model	Healthcare-specific RCM AI and automation	Coordinated AI workforce for RCM	Enterprise agentic automation and orchestration platform
Main prior authorization strengths	Clinical evidence retrieval, staff assistance, status checks	Planned end-to-end execution across portals, phone, and fax, supported by adjacent RCM agents	Orchestration across agents, robots, APIs, documents, rules, and people
Current public maturity	Current products and provider case evidence for AI assistance and status automation	Adjacent agents are marketed; Stephanie is publicly described as upcoming	Healthcare prior authorization solution launched in 2026; supporting platform and customer automation evidence
Provider or payer orientation	Primarily provider revenue cycle	Primarily providers, billing teams, and RCM operations	Both providers and payers
Human-in-the-loop model	Explicit expert and user oversight	Escalation-oriented multi-agent model; exact controls require validation	Human tasks and clinical review can be modeled within orchestration
Integration approach	Embedded in healthcare revenue cycle and EHR workflows	EHR, practice management, payer portal, fax, and calling workflows	APIs, RPA, agents, document processing, BPMN-style orchestration, and enterprise applications
Best fit	Health systems needing better documentation retrieval and status automation	Organizations seeking a broad AI workforce across RCM	Enterprises needing configurable, governed, cross-system automation
Primary buying risk	Assuming targeted automation equals full end-to-end autonomy	Treating planned capabilities as proven production functionality	Underestimating implementation and operating-model complexity

The vendors should therefore not be ranked on a single “best prior authorization AI” scale. They solve different layers of the problem.

What Most Vendor Comparisons Miss

Most prior authorization AI comparisons focus on features:

• Does it submit requests?
• Does it read documents?
• Does it check status?
• Does it generate letters?

Enterprise buyers need to evaluate the operating architecture beneath those features.

Policy Versioning

The system must record which payer policy, medical-necessity criteria, contract rule, or coverage document was used for each case. Without versioning, the organization may be unable to explain a recommendation after the policy changes.

Evidence Provenance

Every AI-generated assertion should point back to a source. The system should distinguish:

• A documented fact
• A model-generated summary
• An inferred conclusion
• A staff-entered statement
• A payer-provided requirement

These categories should not be merged into one narrative.

Case-State Management

A prior authorization platform needs durable workflow state.

Language-model memory is not an acceptable system of record.

The authoritative case state should reside in a transactional workflow database or orchestration engine with explicit transitions, timestamps, ownership, deadlines, and retry rules.

Deterministic and Probabilistic Boundaries

Some work should remain deterministic:

• Identifier transfer
• Date calculations
• Code validation
• Role checks
• Submission destinations
• Deadline enforcement
• Approval-number writeback

AI is more appropriate for:

• Document classification
• Clinical evidence retrieval
• Summarization
• Policy interpretation support
• Exception explanation
• Draft generation

Separating these functions reduces hallucination and operational risk.

Authorization-to-Claim Integrity

The system must validate that the eventual claim aligns with the authorization. That includes:

• Procedure code
• Service date
• Quantity
• Units
• Provider
• Facility
• Site of service
• Authorization period
• Approved modifiers where relevant

A fast authorization process that remains disconnected from billing may not reduce denials.

Recommended Enterprise Reference Architecture

A scalable agentic prior authorization product should contain six layers.

1. Integration Layer

Connect to:

• EHRs
• Practice management systems
• Payer APIs
• Clearinghouses
• X12 services
• Payer portals
• Document repositories
• Fax services
• Telephony
• Identity systems

The integration layer should normalize connectivity and prevent agent logic from being tightly coupled to individual applications.

2. Canonical Authorization Data Layer

Maintain one normalized authorization case model.

The case record should act as the operational source of truth even when the request moves through multiple channels.

FHIR resources can support interoperability, but the internal case model may require additional workflow attributes such as queue ownership, retry count, portal status, escalation level, and operational SLA.

3. Knowledge and Evidence Layer

This layer manages:

• Payer policies
• Medical-necessity criteria
• Contracts
• Coverage rules
• Clinical documents
• Code sets
• Prior case outcomes
• Source citations
• Effective dates

Retrieval should be permission-aware and time-aware. The system must not apply a policy that became effective after the authorization was submitted.

4. Agent and Tool Layer

Use specialized agents rather than one unrestricted model. Potential agents include:

• Requirement-discovery agent
• Eligibility agent
• Clinical-evidence agent
• Documentation-completeness agent
• Submission agent
• Status-monitoring agent
• Exception-resolution agent
• Appeal-preparation agent

Each agent should receive only the tools and data required for its task.

5. Orchestration and Human Workbench

The orchestration layer manages:

• Workflow state
• Assignments
• Timers
• Retries
• Escalations
• Human approvals
• Clinical review
• Exception queues
• SLA monitoring

The workbench should display the evidence, policy, recommendation, uncertainty, and proposed action in one place. A reviewer should not have to reconstruct the agent’s reasoning from raw system logs.

6. Governance and Observability

Record:

• Data accessed
• Model and version used
• Prompt or policy template
• Tools invoked
• Source documents
• Generated output
• Human modifications
• Final decision
• Submission response
• Error and retry history

HIPAA-regulated organizations must perform a risk analysis and apply appropriate administrative, physical, and technical safeguards to electronic protected health information. AI does not create an exception to those obligations.

The NIST AI Risk Management Framework provides a useful complementary structure through its Govern, Map, Measure, and Manage functions.

How to Implement Agentic Prior Authorization Safely

Phase 1: Establish the Baseline

Measure the current workflow before selecting a vendor. Document:

• Monthly authorization volume
• Volumes by payer and service line
• Staff minutes per request
• Submission channels
• First-pass completeness
• Additional-information rate
• Approval and denial rates
• Authorization-related claim denials
• Appeal volume
• Scheduling delays
• Payer response time

Without baseline data, the organization may automate activity without proving improvement.

Phase 2: Select a Constrained Use Case

Begin with a workflow that has:

• Significant volume
• Stable operational ownership
• Measurable outcomes
• Accessible data
• Limited clinical risk
• Repeatable payer interactions

Authorization-status checks are often easier to automate than medical-necessity determination.

Document retrieval assistance may be safer than autonomous submission. The first use case should test the architecture without exposing the organization to its highest-risk decisions.

Phase 3: Build the Control Model

For each action, define:

• Fully automated
• Automated with retrospective review
• Automated with pre-action approval
• Human-only

For example:

Action	Recommended Initial Control
Retrieve eligibility	Automated
Search records	Automated
Draft clinical summary	Human review
Select attachments	Human confirmation for complex cases
Submit low-risk complete request	Preapproved automation based on policy
Respond to ambiguous clinical question	Human review
Issue adverse determination	Qualified clinical reviewer
Initiate appeal	Human approval

Autonomy can expand after the system demonstrates reliable performance.

Phase 4: Test the Entire Workflow

Testing must cover more than model accuracy. Include:

• Identity and access testing
• Payer-portal changes
• API failures
• Duplicate submissions
• Incomplete documentation
• Conflicting policies
• Incorrect patient matching
• Model hallucination
• Timeout and retry behavior
• Expired authorization rules
• Human-override behavior
• Audit-log completeness
• Disaster recovery

Healthcare organizations should also maintain a “golden” test set of representative authorization cases across payers, specialties, outcomes, and complexity levels.

Phase 5: Scale by Exception Type

Do not scale only by transaction volume. Analyze which exceptions remain manual:

• Missing clinical evidence
• Policy ambiguity
• Portal failure
• Payer mismatch
• Clinical complexity
• Additional-information request
• Member-data error

Then improve the workflow one exception class at a time. This produces more durable automation than simply increasing the number of bots.

Build Agentic Prior Authorization with CapMinds Services

Agentic prior authorization requires more than adding an AI model to an existing portal workflow. 

CapMinds provides end-to-end healthcare product engineering services that help health systems, payers, and RCM companies design, integrate, deploy, and govern automation across the authorization lifecycle.

Our teams build secure workflows that connect clinical evidence, payer requirements, human review, submission channels, status tracking, and downstream billing operations. Services include:

• Agentic AI and healthcare automation architecture
• Custom prior authorization software development
• FHIR CRD, DTR, and PAS API implementation
• HL7, X12, EHR, clearinghouse, and payer portal integration
• Clinical document retrieval and healthcare RAG solutions
• UiPath, RPA, and human-in-the-loop workflow orchestration
• AI model evaluation, guardrails, audit trails, and observability
• Authorization status, denial, appeal, and RCM workflow automation
• HIPAA-aligned security, cloud deployment, testing, and ongoing support

Whether you are modernizing a provider-side authorization operation, implementing CMS interoperability requirements, or developing a commercial AI prior authorization product, CapMinds can support the program from workflow discovery and architecture through production implementation and optimization.

Reduce manual work, improve submission completeness, strengthen governance, and connect authorization decisions to scheduling, claims, and revenue outcomes.