Forward Deployed Context Engineering

• Health plans lose over $2M annually to provider data issues. ​
• Nearly 30% of claim errors trace back to provider data gaps.​

Generic AI cannot reliably manage provider data accuracy. ​

It lacks:​

• healthcare ontology​
• requires constant manual oversight ​
• fails without strong context and governance

Emids deploys Intelligent Matching Agents, powered by its FDCEs, which understand and govern the provider ecosystem, ensuring sustained, high data accuracy.​

• Eliminates issues with provider entity matching​
• Reduces claim errors and rework​
• Supports faster and cleaner claims processing​
• Improves accuracy in claim adjudication and payment

• 50M+ prior auth requests in Medicare​
• 2–3 requests per member, slowing care and ​
  increasing cost
• Manual review & outreach delay care and raise costs​

Generic AI can summarize data but:​

• Lacks clinical policy and benefit logic​
• Cannot meet audit and regulatory standards​
• Fails without expert supervision

• Auto-gathers evidence and validates medical necessity​
• Sends contextual denial guidance for faster solution​
• FDCEs ensure compliant, audit-ready decisions

• Manual effort drops to <10%​
• Scales to ~99% automation over 12–24 months​
• Governed, compliant, and explainable decisions

• Up to 20% of claims need manual intervention​
• Only ~60% resolved on first pass — up to 40% need re-work​

Generic AI can categorize claims but:​

• Cannot apply benefit edits, appeal rules, payer logic​
• Creates error loops without context delaying ROI

Emids uses ontology-trained entity matching and FDCE governance to:​

• Clean and match provider entities accurately​
• Maintain data currency and trust​
• Feed clean data into claims for accuracy and speed​

• ~50% reduction in manual claim processing effort​
• Scales toward ~90% automation over 12–24 months​
• 28% fewer manual reviews within 90 days

Denials remain high at ~12 percent (14–17 percent in primary care), with single codes like CO59 driving nearly $300M in avoidable loss.​

AI can flag claims but cannot apply payer rules, medical necessity logic, or clinical documentation context without domain structure.

Emids works alongside RCM teams to prevent avoidable denials and strengthen appeal success.​

• Prevents denials upstream​
• Guides compliant appeals with documentation intelligence ​
• FDCEs provide compliant clinical and RCM oversight​

• Higher net collections and faster AR cycles​
• Lower avoidable denials and write-offs​
• Measurable gains in 90 days​