🧠 About AuthBridge

💡 Inspiration

Prior authorization is one of the most frustrating—and dangerous—failure points in modern healthcare.

A physician doesn’t just “submit a form.” They:

• Search through payer policy PDFs
• Translate clinical context into billing codes
• Sit on hold for approvals
• Rewrite documentation after denials

And when something small goes wrong—like a mismatched ICD code—the consequences are real: delayed chemotherapy, postponed imaging, or untreated chronic disease.

The system is broken not because the logic is impossible—but because it is distributed, unstructured, and time-sensitive.

AuthBridge was built on a simple belief:

If LLM agents can reason over messy clinical + policy data, they should be able to execute prior authorization end-to-end—faster, safer, and more consistently than humans under time pressure.

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🏗️ How We Built It

AuthBridge is a multi-agent clinical reasoning system built on top of real healthcare interoperability standards—not mock APIs.

🔗 Core stack

• FHIR R4 (US Core 6.1) → structured patient data (conditions, meds, labs)
• SMART on FHIR (OAuth2) → secure EHR access
• Da Vinci CRD / DTR / PAS → prior authorization workflows
• CDS Hooks → real-time trigger during order entry
• A2A Protocol → agent-to-agent communication
• SHARP-on-MCP → context propagation (patient + token + scopes)

🤖 Agent architecture

At the center is a Claude-powered reasoning loop that dynamically decides:

[ \text{Next Action} = f(\text{Clinical Context}, \text{Policy Requirements}, \text{Tool Outputs}) ]

Instead of hardcoding workflows, the agent:

1. Interprets payer requirements (free-text → structured intent)
2. Maps them to FHIR resources
3. Identifies missing evidence
4. Generates clinical justifications
5. Submits structured PA bundles
6. Simulates payer review (shadow adjudication)
7. Writes appeals if needed

🧰 Tooling layer (MCP)

We exposed the system as 7 composable tools, enabling:

• Deterministic I/O boundaries
• Auditable decision steps
• Safe failure modes (no hallucinated data)

Each tool operates on real schemas (FHIR Bundles, PAS requests), not synthetic abstractions.

☁️ Deployment

• Hosted on Railway (no cold starts)

• Integrated into Prompt Opinion as:

  • MCP Server
  • BYO Agent (AuthBridge Reviewer)

• Live endpoints for:

  • /mcp (tool execution)
  • /a2a (agent messaging)
  • /health (monitoring)

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🧠 What We Learned

1. LLMs are uniquely suited for prior auth

Traditional systems fail because:

• Policies are unstructured PDFs
• Clinical context is longitudinal + messy
• Justifications require human-like narrative reasoning

LLMs excel exactly where rule engines break.

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2. Tool boundaries > prompt engineering

The biggest unlock wasn’t prompting—it was tool design.

By forcing the agent to:

• Explicitly gather evidence
• Map requirements → data
• Stop when data is missing

We achieved safe, inspectable autonomy.

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3. Safety is about refusal, not just accuracy

One of our most important validations:

When given the wrong patient context, AuthBridge refused to fabricate step-therapy history.

This is critical in healthcare:

• Wrong data is worse than no data
• Agents must fail safely, not confidently

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4. Standards are finally ready

For years, automation wasn’t feasible because infrastructure didn’t exist.

Now, with:

• CMS-0057-F mandate (FHIR-based PA by 2027)
• Da Vinci implementation guides
• SMART + CDS Hooks adoption

The pipes are real.

AuthBridge is the agent layer on top of those pipes.

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⚙️ Challenges We Faced

🧩 1. Translating policy → executable logic

Payer rules look like:

“Patient must have failed at least 2 first-line therapies unless contraindicated…”

Turning this into:

• Structured requirements
• Verifiable FHIR queries
• Agent reasoning steps

…was non-trivial.

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🔄 2. Aligning clinical truth with billing logic

Clinical reality ≠ billing representation.

We had to bridge:

• ICD codes vs actual diagnoses
• Medication history vs formulary requirements
• Notes vs structured data

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🧪 3. Designing realistic evaluation

We avoided fake demos by:

• Running multi-specialty scenarios
• Simulating payer adjudication
• Testing failure paths (denials, missing data)

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⚖️ 4. Balancing autonomy with compliance

Healthcare requires:

• Audit logs (HIPAA)
• Deterministic outputs
• Explainability

We ensured:

• Every action is logged
• Every decision is traceable
• Every output is reviewable by a clinician

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🚀 What This Enables

AuthBridge is not just a demo—it’s a new primitive:

Autonomous administrative agents that sit between clinicians and payers.

Near-term:

• Reduce physician admin time
• Accelerate approvals
• Improve documentation quality

Long-term:

• Real-time “pre-authorization at order time”
• Continuous eligibility + policy awareness
• Fully automated revenue cycle workflows

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🧾 Impact Framing

If we model:

[ \text{Time Saved} = 13 \text{ hrs/week/physician} ]

[ \text{Cost Reduction} \approx \mathcal{O}(10^9) \text{ USD annually} ]

Even partial automation creates massive system-wide gains.

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🧑‍💻 Built By

Kaustubh Pardeshi Agents Assemble 2026 · Prompt Opinion / Darena Health

Built With

• claude
• llm
• python
• python-package-index