Labs still receive critical information in formats built for people rather than systems: PDFs, certificates, scanned forms, emailed spreadsheets, instrument reports, and free-text requests. AI document processing can remove a large amount of re-keying, but the useful system is more than an extraction prompt.
Treat extraction as the first step, not the product
A document workflow usually needs five stages: intake, extraction, validation, routing, and review. The model can help turn unstructured content into a proposed structured record, but ordinary software should enforce required fields, allowed values, identifiers, and downstream rules.
The result should be a queue a person can understand: what arrived, what the system found, what confidence or validation checks failed, and what will happen after approval.
Separate facts from generated interpretation
Extracted values should retain a link back to their source document and, when possible, the page or region they came from. Generated summaries should be labeled as generated text rather than mixed into source fields. This makes review faster and prevents a fluent summary from being mistaken for the original record.
Design for exceptions from day one
Real document sets contain rotated scans, missing pages, revised templates, handwriting, duplicated attachments, and values that conflict with another system. A production workflow should assume exceptions exist and route them intentionally instead of treating every file as a clean demo document.
Use the workflow to create better data over time
Once documents become structured at intake, the lab can reuse the same data for search, status reporting, reconciliation, and analytics without repeatedly opening the original file. The long-term benefit is not only faster extraction; it is a cleaner operational data layer around documents that previously lived as attachments.