UC26: Revenue Leakage Auditor (Charge Capture)

The Problem

Missing charges represent direct revenue loss. A medication is administered but not documented in the charge capture system; a procedure is performed but not billed due to a missing charge code. Studies estimate 2 to 5% of billable services are never captured, costing hospitals $1 to 2M annually. Manual charge audits are labour-intensive and often incomplete; many missed charges are never recovered.

What the Agent Does

Reconciles clinical orders in the EHR against charge capture data (charges logged in the billing system)
Flags discrepancies: ordered/administered items with no corresponding charge (missing charge)
Cross-references MAR (Medication Administration Record) against pharmacy charges to identify unbilled medications
Validates charges against the CDM (Charge Description Master): ensures correct charge codes for documented services
Automatically generates backdated charges for recent missed items (<30 days old); routes older items to manual review
Generates charge capture audits by unit, service line, and provider to identify systemic gaps

Data Requirements

Data Sources:

EHR Clinical Orders (Epic, Cerner, Meditech) , Medication orders, procedure orders, lab orders, timestamps
MAR (Medication Administration Record) , Administered medications, time, dose, route
Charge Capture System (Epic charges module, Meditech financial) , Logged charges, charge codes, dates
CDM (Charge Description Master) , Charge code definitions, DRG mappings, contract rates
Billing System (Oracle Financials, SAP) , Posted charges, claim details, aged accounts receivable

Data Classification:

Clinical orders & MAR: Sensitive, Internal, Structured
Charge capture: Internal, Structured
CDM: Internal, Structured
Billing data: Internal, Structured

Data Quality Requirements:

Clinical orders: complete within 24 hours of service
MAR: documented within 1 hour of medication administration
Charge capture: posted within 48 hours of service (billing deadline)
CDM: updated quarterly with new codes or rate changes
Billing system: reconciled daily for accurate aged receivables

Integration Complexity: Low-Medium

EHR order extraction API (Epic FHIR, Cerner REST)
MAR data retrieval (typically available via EHR API)
Charge capture system query (may be proprietary; direct database access sometimes required)
CDM mapping database (often maintained manually)

Score Breakdown

Criterion	Weight	Score (1-5)	Weighted
Time Recaptured	15%	4	0.60
Error Reduction	10%	4	0.40
Cost Avoidance	10%	4	0.40
Strategic Leverage	5%	4	0.20
Data Availability	15%	4	0.60
Process Clarity	15%	4	0.60
Ease of Implementation	10%	4	0.40
Fallback Available	10%	4	0.40
Audience (Int/Ext)	10%	4	0.40
Composite	100%		3.90

Why It Scores Well

Charge capture recovery is a direct revenue lever: recovering 50% of 2 to 5% missed charges = $500K to $1M annually for mid-sized hospitals. The data is highly structured (orders, charges, codes), and reconciliation logic is deterministic. This use case requires minimal operational overhead and delivers immediate ROI.

Regulatory Alignment

CMS Billing Rules (42 CFR 413): Providers must bill for all services rendered; billing must be accurate and timely
HIPAA: Charge data is semi-PHI (contains patient identifiers); must be handled per privacy rules
Anti-Kickback Statute (AKS): Charge capture must not be used to inflate billing for services not rendered
False Claims Act (31 USC 3729): Unbilled charges do not constitute fraud, but overbilling does

Sprint Factory Fit

Sprint 0 (2 weeks) + 2 build sprints (4 weeks)

Charge capture audits run nightly as a batch process: reconcile today's orders against charges, identify gaps. The initial 2-week sprint focuses on EHR order-to-charge reconciliation and simple missing charge detection; a second sprint adds CDM validation and automated backdating. This is a lower-complexity use case suitable for rapid deployment.

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Governance Risks to Consider

Before deploying this use case, review these agentic AI risks from the Corvair Risk Catalogue. Each is scored on the DAMAGE framework and mapped to regulatory expectations.