Oregon Health & Science University (OHSU)

OHSU faced a persistent shortage of medical coders that was degrading operational efficiency and suppressing revenue. Growing case backlogs increased coder workload to unsustainable levels, while manual coding processes introduced errors that resulted in claim denials and lost reimbursement.

OHSU partnered with CodaMetrix to implement AI-driven autonomous coding for radiology. The platform automated the end-to-end radiology coding workflow, replacing manual code assignment with algorithmic processing to reduce backlog accumulation and minimize human error in claim submissions.

The initiative achieved a 92% automation rate for radiology coding, directly reducing coder workload by nearly 28%. Coding-related denials for autonomously coded cases dropped by 70%, improving both staff capacity and net revenue capture.

• AI autonomous coding can achieve high automation rates in well-defined specialties like radiology, where procedure coding patterns are more standardized and predictable.
• Automation simultaneously addresses two revenue cycle pain points: labor capacity and claim accuracy, producing compounding financial benefit.
• Deploying AI in a single high-volume specialty (radiology) is a low-risk entry point that builds organizational confidence before broader rollout.