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Healthcare Economics Engine

Payment Integrity Analysis

Detect claim errors, duplicate payments, and pricing anomalies with ML-powered auditing that recovers 2-4% of paid claims annually

The 3% Leak

Industry benchmarks show 3-5% of healthcare claims are paid incorrectly—duplicate charges, unbundling, upcoding, incorrect pricing, coding errors. On $30M annual spend, that's $900K walking out the door. Your TPA's "payment integrity" caught $120K. Where's the other $780K?

Claim Error Rate
3-5%
industry average
Recoverable Amount
$900K
per $30M spend
TPA Recovery Rate
10-20%
of total leakage

✗ What Fails Without This Engine

  • •TPA catches obvious duplicates but misses unbundling and upcoding
  • •No visibility into pricing accuracy: paying $3,200 for a $1,800 service
  • •Can't quantify total leakage to justify recovery investment
  • •CFO assumes "clean claims" because TPA doesn't flag errors

ML-Powered Claims Auditing

Our Payment Integrity Engine runs 47 automated audit rules across every claim, flags anomalies using machine learning, benchmarks pricing against fair value databases, and prioritizes recovery opportunities by ROI. Clients typically recover 2-4% of annual spend.

Payment Integrity Rules Engine
// Multi-layer audit process
FOR each claim IN paid_claims:
  
  // Layer 1: Duplicate detection
  IF MATCH(claim_fingerprint, prior_claims, 90_days):
    FLAG "Duplicate payment - recovery target"
  
  // Layer 2: Unbundling detection
  bundled_code = CHECK_ncci_edits(procedure_codes)
  IF bundled_code EXISTS AND components_billed_separately:
    overpayment = SUM(component_payments) - bundled_rate
    FLAG "Unbundling violation - recover $" + overpayment
  
  // Layer 3: Pricing validation
  fair_value = GET_benchmark(DRG, CPT, geography, percentile_40)
  IF claim.allowed_amount > fair_value × 1.25:
    pricing_error = claim.allowed_amount - fair_value
    FLAG "Pricing anomaly - investigate"
  
  // Layer 4: Medical necessity
  IF procedure NOT supported_by_diagnosis:
    FLAG "Coding inconsistency - medical review"
  
  // Layer 5: ML anomaly detection
  anomaly_score = MODEL_predict(
    claim.features,
    trained_on: historical_overpayments
  )
  IF anomaly_score > THRESHOLD:
    FLAG "Statistical outlier - audit"

// Prioritize recovery
RANK flags BY:
  recovery_amount DESC,
  statute_of_limitations_proximity ASC,
  provider_cooperation_history

GENERATE recovery_action_plan

Engineering Architecture

Core Components

  • 47 Audit Rules: Duplicates, unbundling, upcoding, incorrect pricing, coding errors
  • ML Anomaly Detector: Pattern recognition trained on historical overpayments
  • Fair Value Benchmarking: Medicare, commercial rate databases, market surveys
  • Recovery Workflow: Provider outreach, documentation, appeals management

Recovery Metrics

Error Detection Rate
3-5%
Recovery Success
60-80%
Annual ROI
8-15x
Audit Cycle
Monthly

Real-World Applications

Manufacturing Company Audit

  • •4,200 lives, $28M annual medical spend
  • •Engine audits 24 months of paid claims
  • •Identifies $1.14M in errors (4.1% of spend)
  • •Top issues: duplicates ($340K), unbundling ($420K), pricing ($380K)
  • •Recovers $842K (74% success rate), ROI: 12x audit cost

Ongoing Payment Integrity Program

  • •7,500 lives, continuous monthly claim audits
  • •Flags 3.6% of claims monthly for review
  • •Proactive provider education reduces future errors
  • •Year 1 recoveries: $1.8M (2.7% of spend)
  • •Year 2 error rate drops to 1.9% as providers adapt

Find Your Missing Money

Upload 24 months of paid claims. Get a complete payment integrity audit report in under 48 hours. See exactly how much you're losing to preventable claim errors.

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