AuthNeuro

AuthNeuro turns 3-week insurance delays into 30-second workflows. We generate precise medical letters with Claude Opus 4.7 and use Arize AX to verify every metric.

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Inspiration

The inspiration for AuthNeuro was born from a realization of the disconnect between modern medical technology and administrative reality.

Having personally experienced the frustration of an insurance denial for a medically necessary surgical procedure, I understand what it feels like to be trapped in administrative limbo. While my own case involved a relatively minor surgery, the denial still resulted in a month-long delay in care.

That experience stayed with me. After developing my skills in software development and AI engineering, I wanted to solve this exact problem—the administrative "purgatory" patients face when insurers determine that treatment is not "medically necessary."

AuthNeuro is our attempt to bridge that gap and accelerate access to care.

What it does

AuthNeuro transforms neuroimaging data and physician documentation into payer-ready prior authorization packages.

The platform:

  • Parses FreeSurfer .stats neuroimaging files
  • Extracts clinically relevant structural metrics
  • Combines imaging findings with physician dictation and clinical notes
  • Generates professional Letters of Medical Necessity (LMNs)
  • Validates generated content against source imaging data
  • Produces submission-ready PDF and email outputs

The result is a streamlined workflow that reduces administrative burden while maintaining clinical integrity and traceability.

How we built it

AuthNeuro was architected as a high-integrity, end-to-end clinical workflow.

Clinical Reasoning Core

We utilized Claude Opus 4.7 to perform the heavy synthesis work, transforming raw structural neuroimaging metrics and physician documentation into professional, persuasive Letters of Medical Necessity.

Production Observability

To ensure reliability and operational visibility, we instrumented the backend using Sentry for:

  • Real-time error monitoring
  • Performance tracking
  • Latency analysis
  • Production debugging

Safety & Compliance

The backbone of the project is the Arize AX observability stack, where we implemented a multi-layered LLM-as-a-Judge evaluation framework.

Truth Layer

A dedicated evaluator mathematically verifies that generated clinical metrics exactly match the source-of-truth neuroimaging files.

Narrative Validation Layer

A second evaluator reviews outputs for completeness, ensuring the generated letter captures all clinically relevant details and patient context.

Challenges we ran into

Building AuthNeuro was not without significant hurdles.

Major Project Pivot

Midway through development, we were forced to abandon our original project direction entirely. This required rapid re-scoping, redesigning, and rebuilding under strict time constraints.

Overcoming Hallucinations

Clinical AI demands a much higher standard than traditional applications.

We learned that AI-generated clinical documentation must be treated as a hypothesis rather than a source of truth. To address this, we built programmatic guardrails that continuously cross-reference generated content against authoritative neuroimaging data sources.

This approach allowed us to create outputs that are both auditable and clinically trustworthy.

Accomplishments that we're proud of

We're incredibly proud of turning a complex vision into a functional, end-to-end prior authorization workflow.

True Clinical Integrity

The imaging data is honest.

Every metric appearing in the final Letter of Medical Necessity is derived directly from parsed FreeSurfer .stats morphometry rather than manual user input.

High Clinical Bar

We implemented a mandatory KPI attestation step requiring physicians to verify extracted metrics before document generation.

This was a deliberate design decision that prioritized patient safety over hackathon shortcuts.

Infrastructure Complexity

We successfully integrated an extensive healthcare AI stack, including:

  • clabtoolkit
  • Redis
  • LangCache
  • Sentry
  • Claude Opus 4.7
  • Arize AX
  • Deepgram
  • AgentMail

The resulting pipeline remains functional and resilient even when individual services experience configuration issues.

Resilient Pivoting

Perhaps most importantly, we're proud of our team's ability to completely change directions midway through development and still deliver a polished, production-quality solution.

What we learned

Systems Engineering > Prompt Engineering

Enterprise healthcare AI is fundamentally a systems engineering challenge.

While model prompting is important, the real innovation lies in building reliable validation, monitoring, and auditing infrastructure around AI systems.

The Necessity of Validation

Clinical AI becomes safer when generated outputs are treated as hypotheses that must be verified against authoritative data sources.

Programmatic guardrails and validation pipelines proved far more valuable than prompt optimization alone.

The Power of Pivoting

Being forced to abandon our initial idea taught us how to rapidly redefine scope, focus on core value, and still ship a high-quality product under pressure.

What's next for AuthNeuro

Version 1 proves the full workflow:

.stats → metrics → clinical note → LMN → PDF/email submission

The next phase is transforming the prototype into a deployable clinical product.

What we didn't do (and why)

EHR Integration & Auto-Fetch Imaging

Hospital integrations require PHI handling, compliance reviews, and institutional access. Manual uploads were sufficient to validate the core concept.

Real Payer APIs

Insurance authorization APIs remain fragmented and difficult to onboard. For demonstration purposes, AgentMail simulates successful submission workflows.

Authentication, Audit Trails & HIPAA Infrastructure

This project was intentionally built as a single-user localhost application. Compliance and enterprise security belong in the productization phase, not a weekend hackathon.

Appeals & Case Tracking

We intentionally scoped the project to first-pass prior authorization generation rather than the full appeals lifecycle.

Full Test Suite & CI/CD

Given time constraints, we prioritized live API smoke testing over comprehensive automated testing infrastructure.

How we move forward

Pilot Ingestion

  • Automated watched-folder workflows
  • FHIR-based imaging retrieval by MRN
  • Manual upload retained as a fallback option

Trust Layer

  • User authentication
  • Comprehensive audit logging
  • Attending physician sign-off prior to submission

Clinical Visualization

Provide physicians with interactive 3D visualizations of patient hippocampal, cortical, or vascular structures alongside extracted morphometric metrics.

Real Clinical Workflow

Expand from document generation to full authorization lifecycle management:

  • Draft
  • Submitted
  • Under Review
  • Approved
  • Denied
  • Appeals
  • Case Tracking

Built With

  • built-with-languages:-python-backend:-fastapi
  • deepgram-(clinical-dictation)-neuroimaging:-clabtoolkit-(freesurfer-.stats-parsing)-caching-&-memory:-redis
  • dictation
  • github
  • httpx
  • job-state
  • langcache
  • metrics-extract
  • openinference-(anthropic-instrumentation)-data-/-formats:-freesurfer-.stats
  • pdf-download)-tools:-git
  • pdf-infrastructure:-local-development-(redis-on-localhost);-api-keys-via-.env-apis:-rest-(/api/v1/authorize
  • pydantic
  • python-dotenv
  • redis-agent-memory-email-/-outbox:-agentmail-api-observability:-arize-ax-(opentelemetry)
  • reportlab-frontend:-streamlit-ai-/-llm:-anthropic-claude-(claude-opus-4-7)
  • sentry
  • uvicorn
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