# Pre-Submission Clearinghouse

*/Opportunities/Pre-Submission_Clearinghouse*

## Opportunity Overview

**Wedge**: Target independent billing companies specializing in orthopedics and physical therapy. These specialties face high denial rates due to complex modifier rules and frequent payer policy shifts, providing an acute pain point and a bounded clinical context to master. After establishing dominance in musculoskeletal billing, expand horizontally into multi-specialty RCMs and eventually directly to enterprise provider networks.
**Timing**: LLMs now possess the reasoning capabilities to parse complex, unstructured payer policy bulletins and translate them into executable formatting adjustments on specific claim payloads in real-time, a leap beyond traditional static rules engines.
**Why This I C P**: Mid-sized RCM agencies operate on razor-thin margins and handle high claim volumes across dozens of specialties, making them highly motivated buyers who judge software purely on its ability to reduce the headcount dedicated to rejection management.
**Size Of Prize**: Roughly 8,000 independent RCM agencies and 15,000 mid-to-large medical practices operate in the US. If each spends an average of $50,000 annually on manual claim scrubbing labor and traditional clearinghouse fees, the total addressable prize is 23,000 entities times $50,000, yielding approximately $1.15B annually.
**Gap Narrative**: Existing clearinghouses run claims through static rulesets, kicking back rejections that require manual human review and correction. RCM teams need an active layer that automatically researches and rewrites the claim to meet shifting payer-specific logic before submission, eliminating the manual rejection-correction loop entirely.
**Defensibility**: Defensibility compounds through a proprietary, continuously updating graph of payer adjudication logic. Every rejected claim the system ingests and successfully corrects trains the routing model on undocumented payer behaviors, creating a shared learning network where a denial prevented for one RCM instantly prevents the same denial for all others.
**Why This Thesis**: A Service-as-Software approach fits this problem exactly because RCMs do not want another dashboard to monitor; they require a drop-in endpoint that ingests raw practice management data and outputs fully cleared, payer-ready claims with zero human intervention.

## Opportunity Linked Thesis

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

## Opportunity Linked I C P

**Icp**: [Medical Practice](/CompanyTypes/Medical_Practice)

## Opportunity Market Sizing

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

**S A M**: ~$1B-$1.5B targeting ~100k independent and mid-sized specialty practices
**S O M**: ~$20M-$50M realistic 3-year capture via direct sales to mid-market clinics
**T A M**: ~300k US medical practices × ~$10k-$15k/yr per practice for pre-submission claim scrubbing software ≈ $3B-$4.5B
**Growth Rate**: ~10-15%/yr, driven by rising payer denial rates and increasingly complex billing requirements
**Paid Comparable Spend**: ~$40k-$60k/yr per practice spent on billing FTEs dedicated to claim rework, denial management workflows, and traditional clearinghouse transaction fees

## Opportunity Incumbents

- [Change Healthcare Clearinghouse](/Products/Change_Healthcare_Clearinghouse) — Service
- [Availity Essentials](/Products/Availity_Essentials) — Tool
- [Waystar Claim Scrubber](/Products/Waystar_Claim_Scrubber) — Tool
- [Trizetto Provider Solutions](/Products/Trizetto_Provider_Solutions) — Service
- [Experian Health](/Products/Experian_Health) — Service
- [Manual Excel Trackers](/Products/Manual_Excel_Trackers) — Spreadsheet

## Opportunity Win Conditions

**Kill Thresholds**:
- False positive rate on intercepted claims remains above 20 percent at day 14
- Less than 50 percent of total practice claim volume runs through the system by day 30
- Technical onboarding and EDI mapping takes longer than 21 days
- CAC exceeds $6000 per mid-market clinic during the first 90 days
**Leading Metrics**:
- Days to first successful batch claim submission
- Percentage of daily claim volume routed through the engine
- False positive flag rate rejected by billers
- Number of batch approvals executed per biller per day
- First-pass payment rate on processed claims
**What Proves Right**: Mid-market specialty practices route at least 80 percent of their daily claims through the pre-submission engine within two weeks of onboarding. The system intercepts and corrects at least 15 percent of incorrectly coded fields before external clearinghouse submission. Billing managers approve batch corrections daily instead of reverting to manual payer portals or incumbent scrubbers.
**What Proves Wrong**: Practices bypass the engine and revert to manual scrubbing in their existing EHR systems because the rules engine generates false positive error flags. Billing teams process fewer than 20 percent of their total claims through the tool after 30 days due to workflow friction. The initial data mapping and onboarding process requires more than three weeks of custom engineering per clinic.

## Opportunity Build Profile

**Hardest Part**: Maintaining a hyper-accurate, low-latency rules engine that perfectly mirrors the opaque and undocumented rejection criteria of downstream regulatory or payer systems. Flagging false positives delays legitimate submissions and destroys user trust, while missing errors defeats the product's core purpose.
**Min Viable Scope**: Validate exactly one high-volume submission type for a single downstream recipient entity, returning only raw error flags with the precise line item. Leave out automated error correction, multi-endpoint routing, and complex reporting dashboards.
**Cold Start Problem**: The system requires a comprehensive map of rejection triggers to provide value, but these triggers are only discovered by observing live submission failures. Break this by ingesting five years of historical rejection logs from a single design partner to reverse-engineer the foundational ruleset.
**Time To First Value**: 2-4 weeks to map the customer's bespoke database schema to the clearinghouse ingestion format
**Data Moat Available**: true
**Technical Difficulty**: Moderate

## Neighborhood

### Where the gap lives

- [Prior Authorization Specialist](/Agents/Prior_Authorization_Specialist) — latent gap · Agents

### Incumbent in

- [Manual Excel Tracker](/Products/Manual_Excel_Tracker) — incumbent in · Products
- [Availity Essentials](/Products/Availity_Essentials) — incumbent in · Products
- [Change Healthcare Clearinghouse](/Products/Change_Healthcare_Clearinghouse) — incumbent in · Products
- [Experian Health](/Products/Experian_Health) — incumbent in · Products
- [Waystar Claim Scrubber](/Products/Waystar_Claim_Scrubber) — incumbent in · Products
- [Trizetto Provider Solutions](/Products/Trizetto_Provider_Solutions) — incumbent in · Products

### Applies thesis

- [Medical Practice](/CompanyTypes/Medical_Practice) — applies thesis · CompanyTypes

### Embodies

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

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