# Clinical Criteria Mapper

*/Opportunities/Clinical_Criteria_Mapper*

## Opportunity Overview

**Wedge**: The initial beachhead targets high-volume orthopedic and cardiovascular procedures at mid-sized regional health systems. These service lines feature complex, highly scrutinized criteria that drive significant hospital margin, allowing for rapid proof of ROI through reduced denial rates. From this starting point, the product expands into concurrent inpatient level-of-care reviews and finally into automated retrospective denial appeals.
**Timing**: Large language models now possess the extended context windows and reasoning capabilities required to ingest dense patient histories and complex payer policy documents simultaneously. This eliminates the previous technical bottleneck of building brittle, rules-based NLP parsers for every new payer guideline.
**Why This I C P**: Provider-side utilization management teams face immediate revenue loss from claims denials and struggle with severe shortages of specialized clinical staff. They deploy automation to protect margins directly, unlike payers who financially benefit from the friction of the prior authorization process.
**Size Of Prize**: There are ~6,000 hospitals and ~10,000 large specialty practices in the US. Capturing an average annual spend of $60,000 from hospitals and $20,000 from specialty practices for clinical mapping automation yields an addressable market of ~$560M annually.
**Gap Narrative**: Utilization review nurses manually cross-reference extensive patient charts against hundreds of evolving, payer-specific clinical guidelines to justify medical necessity. This manual extraction causes authorization delays, high administrative overhead, and avoidable denials when subtle clinical evidence is missed. No current solution automatically links unstructured EHR data directly to the specific Boolean requirements of individual payer policies.
**Defensibility**: Defensibility stems from deep EHR workflow integration and the accumulation of a proprietary policy-interpretation database. As the system processes thousands of cases, it catalogs the unwritten, payer-specific nuances in how different insurers interpret ambiguous criteria, creating an intelligence asset that continuously improves first-pass approval rates beyond what a baseline LLM achieves.
**Why This Thesis**: An agentic software approach aligns with the deterministic nature of medical necessity reviews. The system acts as a reasoning engine that maps unstructured clinical text to rigid policy logic, providing cited, verifiable evidence that humans quickly approve rather than requiring full autonomous decision-making.

## Opportunity Linked Thesis

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

## Opportunity Linked I C P

**Icp**: [Managed Care Organization](/CompanyTypes/Managed_Care_Organization)

## Opportunity Market Sizing

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

**S A M**: ~$400M-600M US regional and mid-market MCOs
**S O M**: ~$15M-30M
**T A M**: ~1,200 US Managed Care Organizations × ~$1.5M/yr ≈ ~$1.8B
**Growth Rate**: ~12-18%/yr, driven by accelerating state-level prior authorization mandates and Medicare Advantage compliance requirements
**Paid Comparable Spend**: ~$500k-1.5M/yr on medical director labor, policy analysts, and outsourced clinical consultants manually updating utilization management policies

## Opportunity Incumbents

- [Deep 6 AI](/Products/Deep_6_AI) — Tool
- [TriNetX Platform](/Products/TriNetX_Platform) — Tool
- [Epic SlicerDicer](/Products/Epic_SlicerDicer) — Tool
- [IQVIA Trial Services](/Products/IQVIA_Trial_Services) — Service
- [WCG Match Services](/Products/WCG_Match_Services) — Service
- [Manual Chart Review](/Products/Manual_Chart_Review) — DIY

## Opportunity Win Conditions

**Kill Thresholds**:
- Manual override rate exceeds 25 percent after 14 days of usage
- Time-to-first-value exceeds 45 days for a new pilot deployment
- D30 retention for individual analysts drops below 1 active day per week
- Pilot to paid conversion rate falls below 20 percent at the target ACV
**Leading Metrics**:
- Time to map a single policy criteria update
- Manual override rate per mapped policy
- Weekly active days per policy analyst
- Days from pilot deployment to first approved policy update
**What Proves Right**: Medical directors and policy analysts use the system weekly to map new state mandates directly into utilization management rules. Users complete policy updates in under 15 minutes with less than a 5 percent manual override rate. Mid-market Managed Care Organizations convert from pilot to annual contracts at price points exceeding 50,000 dollars per year.
**What Proves Wrong**: Policy analysts distrust the mapped criteria and duplicate the work via manual chart reviews and guideline comparisons. The system fails to parse complex state-specific mandates, requiring manual intervention on over 30 percent of updates. Security and compliance blockers extend pilot approval cycles beyond 90 days, bleeding out the sales pipeline.

## Opportunity Build Profile

**Hardest Part**: Accurately extracting temporal and nested clinical constraints from unstructured physician notes while maintaining a near-zero false positive rate.
**Min Viable Scope**: Build exclusively for oncology clinical trial pre-screening, mapping unstructured pathology and progress notes against a static set of ten trial protocols. Leave out automated prior authorizations, multi-specialty coverage, and any write-back capabilities to the EHR.
**Cold Start Problem**: The extraction models require thousands of diverse patient histories to learn nuanced exclusion criteria parsing, but acquiring HIPAA-compliant datasets is extremely slow. Break this by fine-tuning on the open-source MIMIC-IV dataset combined with synthetic patient profiles generated from publicly available trial protocols.
**Time To First Value**: 1-2 weeks of onboarding, gated by read-only EHR integration and historical data ingestion.
**Data Moat Available**: true
**Technical Difficulty**: High

## Neighborhood

### Where the gap lives

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

### Incumbent in

- [Manual Chart Abstraction](/Products/Manual_Chart_Abstraction) — incumbent in · Products
- [Deep 6 AI](/Products/Deep_6_AI) — incumbent in · Products
- [Epic SlicerDicer](/Products/Epic_SlicerDicer) — incumbent in · Products
- [IQVIA Trial Services](/Products/IQVIA_Trial_Services) — incumbent in · Products
- [WCG Match Services](/Products/WCG_Match_Services) — incumbent in · Products
- [TriNetX Platform](/Products/TriNetX_Platform) — incumbent in · Products

### Applies thesis

- [Managed Care Organization](/CompanyTypes/Managed_Care_Organization) — applies thesis · CompanyTypes

### Embodies

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

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