Medical records processing · Production

ABBYY Recognition Server Helps Jeghers Medical Index Create a Searchable Digital Archive of 1,000,000 Files

The problem

Jeghers Medical Index held over a million paper medical journal articles stored across physical filing cabinets and folders, accessible only through slow manual lookups, which limited the archive's usefulness even as it aimed to bring the library online.

Workflow diagram · grounded in source
1
Begin scanning paper articles
trigger
“the library began scanning articles into tiffs in 1997”
2
Build search database with Thunderstone
integration
“They then engaged with Thunderstone to create an SQL relational database management system integrating a customized search device.”
3
OCR conversion via ABBYY Recognition Server
ai_action
“And for DocuSyst, this meant using ABBYY Recognition Server.”
4
Initial batch accuracy verification
validation
“After verifying it exceeded the agreed accuracy benchmarks, the remaining articles, nearly a million of them, were sent over in a series of 7 batches over the course of a year.”
5
Batch quality control sampling
validation
“Every batch completed was subjected to verification, based on random sampling of the converted files.”
6
Integrate PDFs into digital archive
integration
“the resulting archival PDFs have been integrated into JMI’s digital archive – and their new search appliance powered by Thunderstone’s Texis relational database management system.”
7
Appliance indexes documents from OCR
ai_action
“uses an algorithm to take the content of a document and identify it based on the OCR results. It crawls all the PDFs, collects all the information, then tags it so that everything is properly indexed in the system.”
8
Multi-facet search output
output
“The archive can be searched by author, title, subject, journal, publication date, by words in an article, by adjacency (meaning the words can be in the same sentence), paragraph, page, wildcards, truncation and much more.”
Reported outcome

DocuSyst converted the archive to searchable PDF/A files using ABBYY Recognition Server, finishing on time and below budget within agreed quality parameters, and JMI's new search system is far faster and more comprehensive than the old manual process.

Reported metrics
Pages processedover 5,000,000
Project plan length108-page
Digitization duration1 year
Files indexed1,000,000 Files
Show all 9 reported metrics
pages processedover 5,000,000
project plan length108-page
digitization duration1 year
files indexed1,000,000 Files
annual searches20,000 searches every year
physical archive scale165 filing cabinets and 44,000 folders
initial test batch size25,000 files
conversion batches7 batches
total articles in libraryover one million
Reported stack
ABBYY Recognition ServerThunderstoneTexisSQL relational database management system
Source
https://www.abbyy.com/customer-stories/automated-ocr-helps-pioneering-medical-archive-create-searchable-index-of-1000000-files/
Read source ↗

Frequently asked questions

What did this team achieve with this AI workflow?

DocuSyst converted the archive to searchable PDF/A files using ABBYY Recognition Server, finishing on time and below budget within agreed quality parameters, and JMI's new search system is far faster and more comprehe…

What tools did this team use?

ABBYY Recognition Server, Thunderstone, Texis, SQL relational database management system.

What results were reported?

Pages processed: over 5,000,000; Project plan length: 108-page; Digitization duration: 1 year; Files indexed: 1,000,000 Files (source-reported, not independently verified).

How is this medical records processing AI workflow structured?

Begin scanning paper articles → Build search database with Thunderstone → OCR conversion via ABBYY Recognition Server → Initial batch accuracy verification → Batch quality control sampling → Integrate PDFs into digital archive → Appliance indexes documents from OCR → Multi-facet search output.

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