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Meir Dick
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Serious illness is a second full-time job.

Not figuratively. Literally. Between insurance claims, prior authorizations, denial letters, billing disputes, medication schedules, specialist appointments, and a constant flood of medical communications — patients and their caregivers are buried in logistics at the exact moment they have the least capacity for it.

The system wasn’t designed for them. It was designed for institutions — payers, providers, billing departments. And the result is that people fighting for their lives also have to fight to use the coverage they’ve already paid for.

That bothered me. So I built CareBridge.

What It Does

CareBridge is an AI command center for patients managing serious illness. It centralizes everything — insurance claims, appeals, billing disputes, medications, appointments, care team contacts, and communications — and deploys specialized AI agents to do the work that patients and caregivers currently have to do manually.

The core modules:

  • Insurance — track claims, manage appeals (AI-drafted), dispute bills, log status changes, surface stalled claims automatically
  • Queue — an intelligent triage system that surfaces what needs attention today, AI-prioritized and routed
  • Calendar — appointment management with automatic pre-appointment briefings generated before each visit and post-appointment processing to capture notes and action items
  • Prescriptions — medication tracking with adherence monitoring, refill alerts, and schedule management
  • Documents — medical record organization with text extraction from uploaded files
  • Communications — incoming messages analyzed for tone, urgency, and action items
  • Dashboard — a daily AI briefing that opens the app with “here’s what matters today”

The AI layer has 8 specialized agents, each with a narrow job:

  1. Appeal Draft Agent — writes insurance appeal letters tailored to the specific denial code, insurer, and clinical context
  2. Billing Dispute Agent — drafts formal dispute letters for incorrect or questionable charges
  3. Queue Triage Agent — scores and prioritizes incoming items (claims, communications, tasks) by urgency
  4. Dashboard Briefing Agent — synthesizes daily state into a clear briefing
  5. Pre-Appointment Briefing Agent — prepares the patient before each appointment with context, questions to ask, and logistics
  6. Post-Appointment Agent — captures and structures notes after a visit
  7. Communication Summary Agent — summarizes and classifies incoming messages
  8. Letter Quality Evaluator — reviews appeal drafts before sending for completeness, tone, and persuasiveness

Eight agents. Each one replacing hours of human effort.

The Part I’m Most Proud Of: The Intelligence Pipeline

An AI appeal agent is only as good as what it knows about the insurer it’s fighting.

Before I wrote a single line of product UI, I spent several days doing something that might be the most interesting part of this whole project: using Claude as a research engine to build a structured Insurance Intelligence Engine.

Not generic information. Operational intelligence.

Here’s what the system knows today:

Insurer Profiles (5 Major Payers)

UnitedHealthcare, Aetna, Cigna, Blue Cross Blue Shield, Humana Medicare Advantage.

For each one:

  • Verified appeal fax numbers, phone numbers, and best times to call
  • IVR navigation instructions (“Press 3, then 7, ask for the Oncology Case Management line”)
  • Appeal deadlines at each level — first, second, external review
  • Pre-authorization requirements broken down by treatment type (chemo, radiation, PET/CT, genetic testing, immunotherapy, surgery)
  • Policy document references with specific IDs and URLs
  • Known quirks — this is where Claude earned its keep

That last point matters most. For example:

UnitedHealthcare uses nH Predict AI to auto-deny claims. Studies show it has a 90% error rate on certain oncology denials. Appeals citing this AI system by name, and requesting peer-to-peer review, have significantly higher success rates.

Cigna delegates oncology prior authorizations to EviCore. Appeals sent directly to Cigna instead of EviCore create routing delays that can cost patients their appeal window.

This isn’t in any FAQ. It’s the kind of intelligence that a patient advocate with 10 years of experience knows — and that most patients never have access to.

Claude found it, structured it, and it now lives in the system as actionable data every AI agent can pull from.

Denial Code Intelligence (175 Codes)

35 denial codes per insurer, across 14 categories:

  • Medical Necessity, Experimental/Investigational, Prior Authorization, Step Therapy, Bundling, Coding Error, Timeliness, Coordination of Benefits, Benefit Limit, Patient Responsibility, Diagnosis Exclusion, Network Restriction, Duplicate, Missing Information

Each code includes:

  • Plain-language explanation of what it means
  • Recommended appeal strategy
  • Relevant policy language to cite
  • Typical success rate
  • Most effective appeal level (first-level, second-level, external review, state regulator)

When the Appeal Draft Agent writes a letter, it doesn’t just know that a claim was denied. It knows why, what the insurer’s specific policy says, and what argument wins at each stage.

CPT Code Intelligence (38 Oncology-Specific Codes)

38 CPT codes covering chemotherapy administration, radiation therapy, diagnostic imaging, genetic testing, surgical procedures, immunotherapy, cell therapy (including CAR-T at $424K/treatment), and supportive care.

For each code:

  • Medicare reimbursement rate and typical billed range
  • Common denial reasons specific to that procedure
  • Required modifiers and billing tips
  • Bundling rules — what can and can’t be billed together
  • Prior authorization requirements
  • NCCN guideline references

This powers the Billing Dispute Agent. When a patient receives a bill that looks wrong, the system can cross-reference it against known bundling rules, typical rates, and common billing errors — and draft a dispute with specific citations.

Regulatory Framework (35 Regulations)

10 federal regulations (ACA, ERISA, No Surprises Act, MHPAEA, CMS drug coverage rules, clinical trial mandates, Women’s Health and Cancer Rights Act) and 25 state regulations across 10 states — each written as actionable guidance, not legal abstracts.

The system knows, for example, that California patients have step therapy override rights that most other states don’t. Or that New York has oral chemotherapy parity laws that require insurers to cover oral chemo no differently than IV. When a patient’s state and situation match a regulatory protection, it surfaces automatically.

Community Intelligence Pipeline (21 Live Sources)

This is where the system stays current.

A live ingestion pipeline pulls from 21 sources across four tiers:

  • RSS/API: Reddit r/HealthInsurance, KFF, ProPublica, CMS updates, STAT News, NPR Health
  • Newsletters: NCCN, American Cancer Society CAN, Triage Cancer, Patient Advocate Foundation, AMA
  • Web scraping: breastcancer.org, California DMHC, New York DFS, Texas DOI complaint databases
  • Manual/Social: Patient Facebook groups, ERISA law blogs, state commissioner reports

php artisan intelligence:ingest runs on a schedule, dispatching queued jobs that fetch, parse, deduplicate, auto-tag, and classify incoming content. The system infers whether a new article is a denial pattern, policy change, appeal strategy, regulatory update, community tip, or insurer behavior signal.

The appeal knowledge base doesn’t go stale. It learns.

How I Built It

Stack: Laravel + Inertia/React + Prism (AI SDK) + Laravel Cloud

The build itself was fast — CareBridge went from zero to deployed in 6 hours. Not a week. Not a sprint. Six hours of focused build time using Claude Code as the primary coding agent, with me directing architecture and making product decisions. The AI SDK (Prism) connects to Claude and routes to specialized agents based on context. Each agent receives structured data from the database, not raw text, so its outputs are grounded in the patient’s actual situation.

A few architectural decisions I’m glad I made:

Each agent has one job. Early drafts had a more general “AI assistant.” I scrapped it. Narrow agents are more reliable, easier to evaluate, and more explainable. The patient should know exactly what the AI did and why.

Quality evaluation is baked in. The Letter Quality Evaluator runs after every appeal draft before it’s presented to the user. It scores the letter for completeness, appropriate tone, and use of policy citations. Bad drafts don’t ship.

The queue is the product. Seriously ill patients don’t have mental bandwidth to manage a complicated dashboard. The queue triage system is what transforms CareBridge from “a place to store information” into “a thing that tells you what to do today.” That was the key product insight.

Testing at this depth requires fake data that feels real. The demo seeder generates a complete patient profile — realistic diagnosis, insurance history, claims at various stages, some denied, some appealing, medications, upcoming appointments, a backlog of communications. It took almost as long to build good fake data as some of the features.

What I Learned

AI research before AI features. The biggest leverage wasn’t the agents — it was the knowledge base they run on. I spent more time on the intelligence engine than on the UI. That was the right call. A Claude agent writing an appeal with 175 indexed denial codes and 85 insurer-specific templates is categorically more useful than one that’s just “smart.”

The hardest decisions are what the AI should NOT do. Every feature I cut came down to the same question: does this add clarity or complexity for someone in crisis? The answer eliminated a lot of ideas.

Vibe coding for this domain required serious knowledge work first. Most apps I build, I can go from idea to shipped fast because the domain is simple. This domain isn’t. Insurance billing and appeals is adversarial, jurisdiction-specific, and highly technical. AI-assisted development meant I could move fast on the product layer — but the intelligence layer needed careful, researched, structured knowledge work first. Claude was instrumental in that phase.

The 1% statistic is the whole product. Less than 1% of denied insurance claims are ever appealed. Success rate when properly appealed: 44–80%. That gap is the entire reason this exists. If CareBridge helps even a small fraction of seriously ill patients win appeals they otherwise wouldn’t have filed, the value is enormous and obvious.

What’s Next

CareBridge is live at care-bridge-master-5yx480.laravel.cloud.

I’m exploring whether this becomes a company. The problem is massive and underserved. The AI capability to address it now exists. The data infrastructure is built.

If you’re working in healthcare AI, patient advocacy, or health-tech — I’d love to connect. If you or someone you know is navigating serious illness and the insurance maze that comes with it, reach out.

Some problems are worth building for.