UC20: Underpayment Recovery Agent

The Problem

Payers frequently underpay claims relative to contracted rates. A claim for $1,000 should be paid $800 per the payer contract, but the payer remits only $600, citing a bundled service or non-covered modifier. Manual reconciliation against contract fee schedules is laborious; many underpayments go undetected. Hospitals lose 1 to 2% of net revenue to underpayments, or $500K to $2M annually for a large health system.

What the Agent Does

Processes incoming 835 ERA payment data in batch mode (daily or weekly)
Cross-references payment amount against payer contract fee schedules for the specific CPT/HCPCS code and modifier
Identifies underpayments: claimed amount exceeds paid amount beyond expected deductible/coinsurance
Generates underpayment appeal letters citing the specific contract section, fee schedule attachment, and claim reference number
Tracks appeal outcomes (accepted, denied, pending) and feeds results back into contract negotiation strategy
Produces aggregate underpayment reports by payer, CPT code, and contract to identify systemic issues

Data Requirements

Data Sources:

835 ERA Files , Payer remittance data, claim-level details, payment amounts, claim IDs
Payer Contracts , Executed fee schedules, effective dates, rate changes, bundling rules
Claims Submission Data , Original claim amounts, submitted CPT/HCPCS codes, modifiers, units of service
Finance System (Oracle, SAP) , Posting ledger, expected vs. actual payment reconciliation
Historical Appeal Outcomes , Prior underpayment appeals by payer to identify successful appeal strategies

Data Classification:

835 ERA data: External, Structured
Payer contract fee schedules: Sensitive, Internal, Structured
Claims submission data: Internal, Structured
Appeal outcomes: Internal, Structured

Data Quality Requirements:

835 ERA data: received within 24 hours of payer processing (EDI standard)
Payer contract fee schedules: updated within 5 days of contract execution or rate change notification
Claims submission data: retained per CMS Conditions of Participation (minimum 7 years)
Reconciliation: claims matched to payments within 30 days of ERA receipt

Integration Complexity: Low-Medium

835 EDI parsing (mature standard)
Payer contract database normalisation (manual data entry or OCR)
Claims submission system query (Epic, Waystar, R1 RCM APIs)
Finance system integration for payment posting (GL account mapping)

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%		3.95

Why It Scores Well

Underpayment recovery is a direct, high-ROI initiative: identifying and appealing even 50% of underpayments recovers $250K to $1M annually for mid-sized hospitals. The data is highly structured (835 EDI, contract fee schedules), and appeal logic is rule-based. This use case scales efficiently with batch processing and requires minimal ongoing operational overhead.

Regulatory Alignment

HIPAA Transactions & Code Sets (45 CFR 162): 835 remittance data is HIPAA-mandated; audit logs required
CMS Claims & Billing Rules: Underpayment appeals must reference contract terms and claim substantiation
State Insurance Laws: Some states require payer acknowledgment of underpayment appeals within 30 days
False Claims Act (31 USC 3729): Overclaiming is fraud; agent must not flag disputes based on aggressive coding

Sprint Factory Fit

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

Underpayment recovery is a scheduled batch process: 835 files arrive daily or weekly, and processing can run overnight without operational disruption. The initial 2-week sprint focuses on 835 parsing and single-payer contract mapping; a second sprint adds multi-payer support and appeal letter generation. This is a lower-complexity use case suitable for rapid deployment.

Comparable Implementations

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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.