Document Review with Unsiloed and TypeSafe Jev
This technical piece describes a workflow using Unsiloed and TypeSafe’s Jev to improve document data extraction accuracy. It explains how to use typed questions and probability scores to filter out OCR errors and implausible data.
Why it matters
Automated document processing is prone to errors, and this methodology provides a framework for verifying data integrity in sensitive fields like healthcare.
Separate uncertain readings from implausible values with source evidence, typed Jev questions, and a prescription walkthrough.
An extraction pipeline can read a value correctly and still pass bad data downstream. A clearly printed extra zero survives excellent optical character recognition (OCR).
Using Unsiloed and TypeSafe’s Jev together helps us check for OCR failures and general mistakes in a document. Unsiloed extracts structured fields and locates them on the page. Jev judges the extracted information against focused questions. We can combine these results to decide which fields need review.
A Jev request separates the information it considers ( state ) from the decisions it makes ( questions ). In our workflow, state contains the records Unsiloed extracted and the context needed to interpret them. Each question tells Jev what to check and which answer type to return; it can also define criteria for the possible answers.
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