Peerbits Engineering Use Case · PriorAuth AI
Eliminate administrative delays by letting AI assist with clinical evidence gathering, payer requirement matching, gap identification, and justification drafting — while keeping 100% of final approval and sign-off decisions under human clinician control.
10
WORKFLOW STEPS
6
CORE MODULES
100%
HUMAN CONTROL
EHR clinical evidence extraction
BUILTPayer criteria & rule matching
BUILTDocumentation gap detection
BUILTAI medical necessity drafting
BUILTMandatory clinician review & sign-off
CONTROLLEDAdministrative friction, denials & documentation delays
Assists evidence extraction, gap checks & justification drafting
100% clinician review & final submission sign-off
Configurable coverage criteria & LCD/NCD rule matching
FHIR R4, SMART on FHIR & clearinghouse adapters
Working demonstration available
The Business & Clinical Impact
Prior authorization creates substantial friction across healthcare operations. Fragmented clinical evidence, constantly shifting payer requirements, manual paperwork, and opaque status tracking directly cause care delays, elevated denial rates, and massive administrative rework.
Clinical history, prior conservative therapies, physical therapy notes, and diagnostic imaging are buried across disparate EHR tabs, leading to missed evidence during submission.
Each health plan maintains unique, frequently changing clinical coverage criteria, specific diagnostic prerequisites, and customized documentation requirements.
Care teams spend hours manually drafting medical necessity letters, re-entering demographic data into payer web portals, and faxing clinical summaries back and forth.
Without centralized tracking, authorization requests fall through the cracks, leading to delayed patient care, preventable claim denials, and redundant submission efforts.
Operational Reality: These challenges represent industry-wide prior authorization friction points documented across US healthcare practices and utilization management teams.
What Peerbits Engineered
Peerbits engineered a working prior authorization platform demonstration that combines clinical data aggregation, payer requirement matching, AI-assisted documentation drafting, and provider review workflows.
Surfaces relevant diagnoses, medications, labs, imaging and chart history for the authorization request.
A configurable policy rule engine matching procedure and diagnosis codes to payer coverage criteria.
Assists evidence gathering, requirement matching, gap identification, and medical necessity drafting.
Interactive review interface where clinicians inspect evidence, edit justifications, and approve submissions.
Task routing, escalation queues, SLA monitoring, and automated notifications for authorization teams.
Visibility into request progression with support for clearinghouse and payer adapter workflows.
End-to-End Flow
A request moves systematically from the initial order through clinical data gathering, rule matching, AI-assisted package preparation, and into Provider Review — the mandatory human-control checkpoint — before submission and status tracking.
STEP 1
A clinician orders a procedure, medication or treatment that requires prior authorization.
STEP 2
Relevant clinical records, chart notes, labs, imaging, and patient history are extracted via EHR / FHIR APIs.
STEP 3
Configured payer coverage rules, clinical policies, and required evidence checklists are matched to CPT and ICD-10 codes.
STEP 4
AI parses unstructured clinical documentation to surface supporting evidence aligned with payer policy requirements.
STEP 5
The system highlights missing clinical documentation or required prerequisites before package assembly.
STEP 6
AI drafts a structured medical necessity justification letter with traceable citations for human review.
STEP 7
A clinician or authorization specialist reviews, edits, and digitally signs off on the completed package.
STEP 8
The approved authorization request is dispatched via payer APIs, clearinghouses (EDI 278), or portal adapters.
STEP 9
Real-time tracking of submission status, acknowledgment receipts, and payer review progress.
STEP 10
Final payer determination (Approved, Denied, or Additional Information Requested) synced to clinical workflows.
AI Capabilities & Human Governance
AI is positioned strictly as an assistive intelligence layer: synthesizing clinical evidence, matching policy rules, flagging missing documentation, and drafting medical necessity justifications. AI does not make clinical judgment calls or autonomous coverage determinations.
Surfaces relevant diagnoses, medications, observations, notes, and treatment history from clinical records.
Compares gathered clinical documentation against configured payer policy guidelines and LCD/NCD rules.
Flags missing prerequisites (e.g. required physical therapy duration or recent lab work) prior to submission.
Generates a draft medical necessity justification complete with EHR source citations for clinician approval.
Clinical Decision Support Boundary: All AI-generated evidence summaries and justification drafts are subject to mandatory provider review and digital sign-off. The system is engineered to assist administrative efficiency and documentation quality, preserving physician autonomy and payer determination authority.
Payer Policy Intelligence
Payer requirements are managed through a version-controlled rule engine that maps clinical procedures (CPT/HCPCS) and diagnoses (ICD-10) to payer medical policies, CMS National/Local Coverage Determinations (NCD/LCD), and specific clinical documentation checklists.
| Payer Policy Criterion | Required Clinical Evidence | Rule Matching Logic | Verification Status |
|---|---|---|---|
| Conservative Therapy Trial | Documented PT / structured exercise > 6 weeks | EHR PT Encounter logs & progress notes | Verified (12 sessions) |
| Medication Management | Failed trial of NSAIDs or muscle relaxants > 4 weeks | MedicationRequest history + pharmacy fill log | Verified (Ibuprofen / Naproxen) |
| Neurological Examination | Objective findings of radiculopathy / reflex deficit | Physical Exam clinical note section | Verified (Positive SLR right) |
| Recent Plain Radiographs | Lumbar X-ray within prior 12 months | DiagnosticReport (Imaging) observation | Verified (01/22/2024) |
Ingests structured payer clinical guidelines, LCD/NCD bulletins, and commercial pre-authorization criteria into queryable JSON rules.
Tracks rule revisions with effective dates and change history, ensuring authorization checks align with active payer guidelines.
Automatically cross-references ordered CPT/HCPCS codes against relevant ICD-10 indication criteria to identify required documentation.
Rule Engine Scope: The platform provides a configurable intelligence layer that models clinical rules and criteria. Rule sets are maintained and customized per payer and specialty — they do not represent hardcoded, universal assumptions across all healthcare plans.
Clinician Experience
The interactive workspace puts gathered EHR evidence, payer requirement gap analysis, and the AI-generated justification draft side-by-side — allowing clinicians to verify details, make direct edits, and authorize submission with full confidence.
Aggregates patient history, chief complaints, medication trial timelines, PT session records, and imaging findings into an organized, readable summary.
Displays real-time checklist status against payer policy requirements with clear visual indicators of verified vs missing documentation items.
Generates structured medical necessity letters with inline EHR citations, editable text fields, and a digital clinician sign-off workflow.
Lifecycle Visibility
Track every request from initial creation through review, submission, and final payer adjudication with real-time status visibility.
Track urgent vs standard turnaround times to prevent authorization expirations and procedural delays.
Instant notifications for required documentation addenda, peer-to-peer review requests, or approval notices.
Time-stamped history of every data extraction, clinical review edit, provider sign-off, and payer status event.
System Integration
The platform connects EHR clinical records to AI reasoning and clinical review, dispatches approved packages through an extensible Payer & Clearinghouse Adapter layer, and syncs status updates back to provider systems.
EHR / Clinical
Epic / Cerner FHIR
FHIR & SMART
Resource Extraction
Rules & AI Engine
Criteria & Gap Analysis
Clinician Review
Human Sign-off
Payer / Clearinghouse
EDI 278 / FHIR PAS / APIs
Status & Response
Decision & EHR Sync
PAYER & CLEARINGHOUSE INTEGRATION ADAPTER OPTIONS
Batch or real-time electronic prior authorization and attachment transactions
Standards-based direct prior authorization support via emerging CMS-interoperability APIs
Secure API or programmatic gateway submission to specific commercial payer endpoints
The application draws on standard FHIR resources — including Patient, Condition, Procedure, Observation, DiagnosticReport, and MedicationRequest — to assemble the clinical picture behind a request.
Actual resource consumption and payer transport channels depend on the specific EHR, clearinghouse, and health plan integration requirements of each deployment.
Security & Governance
Encryption at rest
Encryption in transit
Role-based access control
Secure authentication
Audit logging
Document security
Consent-aware workflow design
Human approval controls
Security Note: The demonstrated architecture was designed around HIPAA-ready security patterns. Production compliance depends on deployment configuration, operational controls, infrastructure, Business Associate Agreements (BAAs), organizational policies, and the customer's implementation environment.
Technology Stack
Frontend
Backend
AI & Rules
Data Layer
Cloud Infrastructure
Interoperability
Applications
Specialty practices
Health systems
Prior authorization teams
Utilization management platforms
Revenue cycle platforms
Digital health products
Common customization areas: specialty-specific authorization workflows, payer-specific rules, EHR/FHIR integration, document templates, workflow approvals, status integrations, dashboards, role models, AI models/prompts, and cloud deployment.
The demo provides a working reference implementation. Peerbits can adapt the workflow, integrations, AI layer and application architecture around an organization's specific requirements.
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No. This is a Peerbits engineering use case — a working demonstration we built to show how this workflow can be engineered. It is not a named client project, and Peerbits can walk you through it on request.
No. AI acts strictly in an assistive capacity by organizing clinical evidence, matching documentation to configured payer requirements, drafting justification content, and flagging missing gaps. A licensed provider or authorized care team member must review, edit, and approve everything before submission.
No. Payer requirements are managed through a configurable rule layer, and EHR/FHIR integration depends on the specific system and implementation environment. The architecture is designed to integrate with payer endpoints and clearinghouse standards (such as EDI 278 or FHIR Da Vinci PAS), but direct out-of-the-box support for every vendor is not claimed.
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