Client’s Correspondence Challenges
- Tedious manual process to read 1000’s of correspondence/ lockbox letters
- Missed timelines and revenue loss due to delayed action on payor letters like denials and MRR
CognitiveHealth’s iCANTM Automation Solution
- Automated clean/sort/read payor letters for classifying and indexing batches of letters
- Take action on payor letters like automated appeals and Medical record requests from the EMR
iCANTM Correspondence Solution Implementation
- AI tools like LLMs, ML models, OCR are used for accurate classification
- Rules engine and GPT models used to execute on the WQs for appeals, MRRs
RESULTS
- 90+% accuracy of classified letter categories
- Reduced over 70+% manual effort for letters classification
- AI models trained to classify 10+ letter types and growing
BUSINESS VALUE DELIVERED
- Reduced staffing needs by 70% for document classification
- 47% of First level appeals processes automated
- Timely action on MRRs, with 34% reduced manual effort
“In the 15 months of collaboration with the CognitiveHealth team we have successfully implemented automation in the areas of cash posting, reconciliation and correspondence work. We are pleased with the early results and continuing to expand our partnership with CognitiveHealth”



VP, Financial Services
3000+ bed academic health system
3000+ bed academic health system