Cutting manual data entry and claim denials in the lab
Most lab data problems start upstream: records arrive in inconsistent formats and get re-keyed by hand, so errors surface later as denials and rework. The fix is validating data before it becomes a downstream problem.
By The Igentify Team
Labs lose time and money to a quiet problem: clinical data arrives in a dozen formats from a dozen sources, someone re-keys it by hand, and the mistakes don't show up until later, as claim denials, accessioning delays, and rework.
Stop fixing data, start using it
The goal isn't to move data faster or extract it more cleanly. It's to make data usable and correct before it reaches your LIS and billing. Validation at the source catches the errors that would otherwise become denials and remediation tickets downstream.
Why OCR and ETL don't solve it
OCR reads documents and ETL moves data, but neither understands what the data means, so both pass errors through silently. AI Sync reads clinical data in context: it validates against expected values, flags only what needs human judgment, maps findings to the right patient, and outputs clean, structured records.
Connect without rip-and-replace
Integration shouldn't be the blocker. AI Sync can take data by API or directly from an S3 bucket, which clears the integration barrier that traditional pipelines can't for most labs. One normalized feed replaces multiple portals and per-lab integrations.
Clean data upstream means fewer denials, less manual entry, and faster turnaround as volume grows.
on your own workflows.