Medical records processing · Production
CodaMetrix automates 85% of radiology and 73% of pathology coding at Mass General Brigham, cutting denials 58.7%
The problem
Mass General Brigham's computer-assisted coding system had stalled, leaving radiology and pathology coding largely unautomated and generating high volumes of coding-related claim denials.
First attempt
Mass General Brigham's prior CAC system had stalled and was abandoned.
Workflow diagram · grounded in source
1
Contextual coding automation
ai_action
“automated 85% of radiology and 73% of pathology coding”
2
Denial reduction output
output
“cut coding-related denials by 58.7% - saving millions annually”
Reported outcome
CodaMetrix automated 85% of radiology and 73% of pathology coding and cut coding-related denials by 58.7%, saving millions annually.
Reported metrics
Radiology coding automation rate85%
Pathology coding automation rate73%
Coding-related denial reduction58.7%
Annual savingsmillions annually
Reported stack
CodaMetrix
Frequently asked questions
What did this team achieve with this AI workflow?
CodaMetrix automated 85% of radiology and 73% of pathology coding and cut coding-related denials by 58.7%, saving millions annually.
What tools did this team use?
CodaMetrix.
What results were reported?
Radiology coding automation rate: 85%; Pathology coding automation rate: 73%; Coding-related denial reduction: 58.7%; Annual savings: millions annually (source-reported, not independently verified).
What failed first in this deployment?
Mass General Brigham's prior CAC system had stalled and was abandoned.
How is this medical records processing AI workflow structured?
Contextual coding automation → Denial reduction output.