Primary interaction
Default path
Exceptions first
The queue turns broad status polling into prioritized work. Select an exception, poll status, and route only what needs action.
MonitorPrioritizePollResolve
Failed to load exception queue.
Skip to primary interaction
Healthcare
Claim Status Automation
Healthcare RCM workflow that monitors claims, prioritizes exceptions into a queue, polls payer status, and routes only the claims that need a human.
- Classifies claim status work into exceptions and clean claims
- Polls selected claims with simulated 276/277 transaction responses
- Keeps specialists focused on claims that need action
Select a claim from the queue above to poll its current 276/277 status.
Specialists should not chase every claim. The queue shows which ones need action.
Loading economics…
Under the hood
Capability proof
Capability proof
Exception queue operating model
Service model
Live claim-status workflow for queue, metrics, selected-claim polling, and rules.
Intelligence layer
Prioritizes exception work and interprets status responses against claim context.
Operational state
Maintains queue status, claim age, payer response, and exception category.
Human control
Specialists work the claims that need action instead of chasing every status.
Business value
Converts high-volume follow-up into a monitored exception queue.
Why an exception queue compounds
Claim Status Automation — Live AI Demo
Claim status inquiries are high-volume and rules-based. The value is not only lower polling cost. It is turning noisy follow-up work into a monitored exception queue, with rules and SLA signals that tell the team which claims actually need attention.Architecture
Estimated monthly cost at portfolio traffic: under $1/month (Lambda free tier + minimal API Gateway requests)
| Layer | Tech | Purpose |
|---|---|---|
| Synthetic data | JSON · 400 claims · deterministic seed | Realistic payer mix, CPT distribution, and 276/277 status histories |
| Compute | AWS Lambda (nodejs20.x · 512MB) | Single function, route-based dispatch across 3 endpoints |
| API | API Gateway (HTTP API) | Shared with the AI Transformation Platform demo on the same SAM stack |
| Frontend | React + CSS modules · EDGE design tokens | Fetch on mount, no polling. Exception queue, poll simulator, economics |
| Deploy | AWS SAM · us-east-2 | `sam build && sam deploy`; CloudFormation manages all resources |
What this demo is, and isn't
What this demo is, and isn't
- All claim data is synthetic. NPIs use a prefix (9) that is not in any real NPI registry range. Claim numbers, patient details, and payer trace numbers are generated from a deterministic seed.
- The 276/277 status category codes (F1, F2, P1, P3, A3) and status codes (65, 1, 20, 21, 98, 107) are real WPC ANSI X12 values, included so the response shape is accurate. Real production data never appears in this demo.
- Poll latency (200–3000ms) and the 3% payer rail timeout rate are simulated to reflect production behavior. In a live RCM system, these vary by payer; some clearinghouses respond faster, some payer direct-connects time out more often.
- The economics math assumes a fully-automated polling workflow. Real implementations often start at 60–80% automation, with the rest requiring human follow-up that doesn't scale linearly with poll volume.