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Healthcare

Eligibility + Front-End QA

Prevent denials at the source, before claims are submitted

  • Prevents denials before they happen, at the cheapest fix point
  • Anchors every risk prediction to a named pattern and CARC
  • Produces clean training signal for every downstream automation
Try the risk predictorBrowse the pattern library

Primary interaction

Operating model

Interpret, predict, classify, prevent

The workflow moves eligibility and front-end quality issues upstream before they become denials.

InterpretPredictClassifyPrevent
334ms wall · undefinedms classifier
Prediction failed: Failed to fetch
702ms wall · undefinedms interpreter
Interpretation failed: Failed to fetch

A pre-encounter fix costs a fraction of a downstream appeal.

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Under the hood

Capability proof

Capability proof

Front-end denial prevention workflow

Service model

Live pre-encounter workflow for risk prediction, eligibility interpretation, claim QA, and pattern review.

Intelligence layer

Flags denial risk, interprets coverage signals, and classifies quality issues before submission.

Operational state

Maintains encounter context, payer behavior, risk patterns, and QA outcomes.

Human control

Front-end teams can inspect risk drivers and correct issues before downstream damage.

Business value

Moves the cheapest fix point upstream and creates cleaner training signals for later revenue-cycle automation.

The bigger story is the training signal

Eligibility + Front-End QA — Live AI Demo

Front-end intercept is the cheapest place in the revenue cycle to fix a problem. A downstream appeal costs many times what a pre-encounter correction does, and the cost compounds on every denial that reaches a payer. The bigger story is the training signal: every prevented denial is a clean labeled example for every model downstream, which is why the risk patterns the predictor knows about link back to the same taxonomy nodes the denial spine uses, so a prevented denial and a recovered one share vocabulary instead of fragmenting it.

Library overview

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Architecture

Estimated monthly cost at portfolio traffic: under $1/month (Lambda free tier + minimal API Gateway requests)

LayerTechPurpose
Synthetic dataJSON · 8 payers · 12 risk patterns · 50 encounters · 50 eligibility responses · 50 claim payloads · 150 pre-computed AI outputsReal CPT/HCPCS codes and real X12 271 segment shapes; pre-computed AI outputs (interpretations, predictions, classifications); one deterministic seed (20260514)
ComputeAWS Lambda (nodejs20.x · 512MB)Single function, route-based dispatch across 8 endpoints (payers, risk-patterns, encounters, encounters/{id}, interpret-eligibility, predict-risks, classify-qa, metrics)
AI surfacesPre-computed model outputs + realistic latency simulation (150-700ms)Three classifier outputs baked into the dataset to mirror production model shapes: per-field confidence on the interpreter, ranked top-5 risks with reasoning on the predictor, structured field-level flags on the QA classifier.
APIAPI Gateway (HTTP API)Shared with the other healthcare demos on the same SAM stack
FrontendReact + CSS modules · EDGE design tokens · shared EncounterPicker across surfacesLibrary overview, three AI surfaces with independent default encounters, encounter workbench with filterable table + detail pane, risk pattern library grouped by category
Cross-demo linkage12 risk patterns reference taxonomy node IDs from the denial-spine demoSame vocabulary as downstream automations: a prevented `rp-auth-required-no-auth` here is the same `tax-auth-missing` node that the denial spine and claim-status demos consume.
DeployAWS 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 patient names, member IDs, DOBs, and encounter records are synthetic. CPT/HCPCS codes are real WPC values; X12 271 segment shapes are real. The data behind every code and segment is fictional; no PHI, no real claims, no real eligibility responses.
  • The encounter risk predictor is the headline AI surface, but its outputs are pre-computed in the synthetic dataset. A production deployment would call a gradient-boosted classifier or a fine-tuned distilbert with features like payer + CPT + POS + auth-status + eligibility-staleness + historical denial rate per payer/CPT pair. The per-pattern confidence shape, the ranked top-5 structure, and the feature-attribution reasoning are modeled on what that classifier would return — not narrated text dressed up to look like a model output.
  • The eligibility interpreter mirrors how production X12 271 parsers behave: it has to handle messy responses with missing or contradictory fields, output per-field confidence, and produce a narrative summary that a rep can read in seconds. The interpretations here are pre-computed for demo determinism; production would call a managed service (Comprehend Medical, a custom Bedrock model) or an in-house fine-tuned LLM with the same output shape.
  • The QA classifier is modeled on rules+ML hybrid outputs from real RCM production deployments. Real classifiers combine regex-on-schema rule packs (modifier-required, demographic-match fuzzy threshold, payer-ID format) with a learned classifier for ambiguous cases. The output shape — field_path + issue_type + severity + current_value + suggested_value + confidence + rationale — is what a production deployment would emit, so the frontend doesn't change when the backend is swapped from baked outputs to live inference.
  • Risk pattern severity weights and baseline rates are illustrative, not benchmarked. Real organizations would learn these from their own historical denial data per payer / per service line. The pattern library structure here is the contract that the rest of the system builds against; the numbers inside are placeholders for the values a real deployment would compute from its own data.
  • Economics assumes a mid-size health system at 100K monthly claims with a 5-8% denial rate. Real organizations vary widely. The 10-20x cost ratio between front-end correction and downstream appeal is the durable claim — driven by who touches the work (scheduler vs. specialist) and how many payer rail cycles are involved (zero vs. multiple). The absolute dollars should be calibrated to the specific organization.
  • Risk patterns link to taxonomy nodes from the denial-spine demo, but the linkage is illustrative. In a real deployment the taxonomy is shared infrastructure; here it's referenced as plain IDs to show the conceptual coupling without requiring the demos to share runtime state.
  • The intercept rate metric (32% in this dataset) is a function of how many encounters were seeded with risks. In a real deployment the intercept rate is the share of encounters where the predictor surfaces at least one preventable issue — it would land lower if the underlying patient population is mostly clean, higher if the payer mix is more complex.