# Pre-Submission Correction For RCM Agencies

*/Opportunities/Pre-Submission_Correction_For_RCM_Agencies*

## Opportunity Overview

**Wedge**: Target independent RCM agencies handling high-volume, modifier-heavy specialties like orthopedics and dermatology. These specialties suffer the highest rate of coding-related denials due to complex anatomical modifiers, offering a fast proof of value. Once the system drops the initial denial rate in these complex lines, expand horizontally to primary care, behavioral health, and eventually full-suite post-submission denial management.
**Timing**: LLMs with large context windows now ingest unstructured, frequently updated payer policy manuals and instantly apply complex semantic rules to individual claim lines, a capability that replaces brittle regex-based rules engines.
**Why This I C P**: RCM agencies aggregate claim volume across multiple specialties and directly bear the labor cost of denial rework. Replacing human scrubbers with software drives immediate margin expansion for them, unlike hospital IT departments which face longer procurement cycles and misaligned incentives.
**Size Of Prize**: Approximately 3,000 mid-market RCM agencies in the US spend an average of $150,000 annually on human pre-claim scrubbing and denial prevention staff. Multiplying these 3,000 agencies by $150,000 per year yields a $450M addressable market.
**Gap Narrative**: RCM agencies lose margin resolving preventable front-end denials caused by coding errors, missing modifiers, and payer-specific quirks. Existing clearinghouse rules engines rely on static, hard-coded logic that fails to adapt to constantly changing payer guidelines, forcing agencies to deploy costly human review teams before claim submission.
**Defensibility**: The system builds an accumulating, proprietary graph of unwritten payer-specific approval patterns and hidden edits based on millions of successful and denied claim feedback loops. As the system scales across multiple agencies, its predictive accuracy for claim clearance outpaces any single agency's internal knowledge base, creating high workflow lock-in and switching costs.
**Why This Thesis**: The Service-as-Software approach fits perfectly because pre-submission claim scrubbing is a deterministic, high-volume operational expense currently fulfilled by outsourced labor. Packaging this as software directly replaces headcount, converting a variable labor cost into a fixed technology expense.

## Opportunity Linked Thesis

**Thesis**: [Software](/Theses/Software)

## Opportunity Linked I C P

**Icp**: [Revenue Cycle Management Agency](/CompanyTypes/Revenue_Cycle_Management_Agency)

## Opportunity Market Sizing

_Illustrative — target and order-of-magnitude estimate figures, not an achieved track record (this Thing is concept-stage)._

**S A M**: ~$150M - $250M targeting agencies managing mid-to-large multi-provider groups with complex specialty coding
**S O M**: ~$10M - $25M
**T A M**: ~10,000 US independent medical billing and RCM agencies × ~$50k/yr expected software spend ≈ ~$500M
**Growth Rate**: ~12-18%/yr, driven by increasing payer denial rates and margin compression forcing agencies to automate claim scrubbing
**Paid Comparable Spend**: ~$45k - $90k/yr per agency spent on dedicated manual QA billers and secondary clearinghouse scrubbing fees

## Opportunity Incumbents

- [Waystar Claim Scrubber](/Products/Waystar_Claim_Scrubber) — Tool
- [Experian Health](/Products/Experian_Health) — Tool
- [Optum Claims Management](/Products/Optum_Claims_Management) — Tool
- [R1 RCM Services](/Products/R1_RCM_Services) — Service
- [Excel Claim Trackers](/Products/Excel_Claim_Trackers) — Spreadsheet
- [In-House Chart Auditors](/Products/In-House_Chart_Auditors) — DIY

## Opportunity Win Conditions

**Kill Thresholds**:
- Implementation time > 45 days for standard PM/EHR integrations
- False-positive flag rate > 20% after 30 days of deployment
- Biller bypass rate > 30% on flagged claims
- Net reduction in first-pass denials < 5 percentage points after 90 days
**Leading Metrics**:
- Percentage of total daily claims successfully ingested and scrubbed
- False-positive flag rate (claims flagged that require zero edits)
- Time-to-correction (minutes from system flag to biller resolution)
- System bypass rate (flags ignored or overridden by billers)
- Days to complete initial PM/EHR data integration
**What Proves Right**: RCM agencies successfully connect the system to their practice management software and route a minimum of 80% of daily claim volume through the pre-submission engine. Agencies reduce their first-pass denial rate by at least 15 percentage points within the first 60 days of deployment. Customers convert to $45k annual contracts after validating the system eliminates the need for dedicated manual QA billers.
**What Proves Wrong**: Agencies refuse adoption because payer-specific rules change too rapidly, resulting in false-positive flags that degrade daily billing velocity. Billers ignore the correction prompts and bypass the software entirely to meet daily claim submission quotas. The engineering cost and time required to integrate with legacy, on-premise billing systems destroys implementation margins and blocks scalable distribution.

## Opportunity Build Profile

**Hardest Part**: Translating constantly shifting, payer-specific local medical review policies into executable logic without generating high false-positive rates that create alert fatigue for billers.
**Min Viable Scope**: Focus strictly on one high-volume specialty to intercept claim files and flag definitive coding errors against the top three commercial payers. Deliberately leave out clinical chart NLP auditing and automated write-back correction, requiring billers to manually fix the flagged issues in their native billing software.
**Cold Start Problem**: No proprietary data exists on localized payer denial patterns until live claims flow through the system. Break this by ingesting 12 months of historical 835 remittance data from the first three design partners to retroactively tune the error-catching heuristics.
**Time To First Value**: 2-3 weeks of onboarding, gated by the initial EHR or clearinghouse API integration required to intercept the 837 claim batch prior to transmission.
**Data Moat Available**: true
**Technical Difficulty**: Moderate

## Neighborhood

### Incumbent in

- [R1 RCM](/Products/R1_RCM) — incumbent in · Products
- [Excel Claim Logs](/Products/Excel_Claim_Logs) — incumbent in · Products
- [In-House Chart Auditors](/Products/In-House_Chart_Auditors) — incumbent in · Products
- [Optum Claims Management](/Products/Optum_Claims_Management) — incumbent in · Products
- [Waystar Claim Scrubber](/Products/Waystar_Claim_Scrubber) — incumbent in · Products
- [Experian Health](/Products/Experian_Health) — incumbent in · Products

### Applies thesis

- [Revenue Cycle Management Agency](/CompanyTypes/Revenue_Cycle_Management_Agency) — applies thesis · CompanyTypes

### Embodies

- [Software](/Theses/Software) — embodies · Theses

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