athenahealth and ABBYY FineReader Engine at #ABBYYSummit16
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Transcript of athenahealth and ABBYY FineReader Engine at #ABBYYSummit16
© ABBYY Confidential
ABBYY TechnologySummit2016
ABBYY NAHQ, 2016
Fuchang Yin
athenahealth, Inc.
© ABBYY Confidential
Million-Scale OCR at athenahealth
clinical document processing
© ABBYY Confidential
What does athenahealth do?
Cloud-based Electronic Health Record (EHR) that allows doctors to delegate work, document easily and stay focused on patients
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More specifically…
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What does an EMR look like?
Mail, phone, fax, & internet technologies carry information across the healthcare system
• Providers prefer established, fast, convenient, and secure communication technologies
• Extraordinary amounts of information are exchanged between patients, providers, pharmacies, labs, hospitals, insurance companies, and the government even in the course of routine care
• Fax is King – and the volume of paper is bewildering
www.sthlny.com/news/viewarticle.asp?a=2264
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Clinical Document Transmission Methods
• External documents:– FAX– Interface– Secure email
• Internal documents:– FAX– Scan– Upload– Mobile capture
Too Much Volume, Even for “Best Practices”
All of this volume ends up in a chart!
www.drchrono.com/blog/new-york-times-article-on-ehr-benefits/
Practices employ people, whose only job day-in and day-out, is to take documents from the fax machine and file them in charts…
www.sthlny.com/news/viewarticle.asp?a=2264
External FAX for Clinical Practices
© ABBYY Confidential
Document Automation Powered by OCR
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Document Classification
• 15 document classes
• 43 document sub classes
• Use natural language processing techniques to classify documents
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Demographic Data Capture
• Content:
– Provider– Patient– Clinical Provider– Other information
• Methods:
– Templates– Mouse select– OCR assisted key-in
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The Switch to ABBYY FineReader Engine
• Comparison:– Tesseract– Other commercial OCR engines– ABBYY FRE
• Performance Gain:– 50% automation gain over Tesseract, without tweaks– Eliminates spelling-check steps– Automatic page direction detection and layout analysis– Greatly reduced errors
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Implementation Insights
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Large OCR Volume at athenahealth
Jan-15 Feb-15 Mar-15 Apr-15 May-15 Jun-15 Jul-15 Aug-15 Sep-15 Oct-15 Nov-15 Dec-15 Jan-16 Feb-16 Mar-16 Apr-16 May-16 Jun-16 Jul-16 Aug-16 Sep-16 -
2,000,000
4,000,000
6,000,000
8,000,000
10,000,000
12,000,000
14,000,000
16,000,000
18,000,000
20,000,000
20M
Per Month
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FAX and OCR System
• 100’s of telecom channels at multiple data centers
• Tens of thousands inbound FAX numbers
• Queueing system
• Distributed storage
• Scale up to hundreds of CPUs to perform OCR
• Batch processing in each ABBYY FRE process
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A Day of OCR Activity at athenahealth
12:00 AM 2:00 AM 4:00 AM 6:00 AM 8:00 AM 10:00 AM 12:00 PM 2:00 PM 4:00 PM 6:00 PM 8:00 PM 10:00 PM 12:00 AM0
100
200
300
400
500
600
700
800
900
Time of Day
Num
ber o
f Acti
ve F
RE P
roce
sses
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Distribution of OCR Time on Each Page
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 300%
5%
10%
15%
20%
25%
30%
35%
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Exception Handling in Workflow
• Long running OCR for a document– Over soft limit, keep OCR running and move on to next step– Over hard limit, terminate OCR process
• OCR errors– Fuzzy match with expected values– Aggregate multiple fields to make decision
• Human QA– Results of all automated steps are reviews by human QA
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Growing Use Cases of ABBYY OCR
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Making Doctors More Efficient
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• Automated Document Processing– Document label recognition– Clinical lab result capture – Duplicate document removal
• Full Document Search
• Many Others
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Questions?
ABBYY NAHQ, 2016
Fuchang Yin
athenahealth, Inc.
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Thank You!