Medical records processing · Production
CodaMetrix automates 92% of radiology coding and cuts denials 65% at Oregon Health & Science University
The problem
Oregon Health & Science University faced high rates of coding-related denials, heavy coder workloads, and a historic coding backlog—challenges compounded by an upcoming major facility expansion.
Workflow diagram · grounded in source
1
Contextual coding automation
ai_action
“CONTEXTUAL CODING AUTOMATION”
2
Automated radiology coding output
output
“automated 92% of radiology coding”
Reported outcome
OHSU automated 92% of radiology coding, cut denials by 65% for high-cost MR cases, cleared a historic backlog, and eased coder workloads—all while preparing for a major facility expansion.
Reported metrics
Radiology coding automated92%
denials reduction for high-cost MR cases65%
Coding backlogcleared a historic backlog
Coder workloadseased coder workloads
Reported stack
CodaMetrix
Frequently asked questions
What did this team achieve with this AI workflow?
OHSU automated 92% of radiology coding, cut denials by 65% for high-cost MR cases, cleared a historic backlog, and eased coder workloads—all while preparing for a major facility expansion.
What tools did this team use?
CodaMetrix.
What results were reported?
Radiology coding automated: 92%; denials reduction for high-cost MR cases: 65%; Coding backlog: cleared a historic backlog; Coder workloads: eased coder workloads (source-reported, not independently verified).
How is this medical records processing AI workflow structured?
Contextual coding automation → Automated radiology coding output.