Information Management Symposium - OCR and Text analytics for Medical Chart Review Process

1. OCR and Text Analytics for
Medical Chart Review Process
Alex Zeltov
Darwin Leung
Ravi Chawla
Somesh Nigam

2. 2
BIOGRAPHY
Alex Zeltov
 Research Scientist, Advanced Analytics
 Independence Blue Cross
 Lead the development and research of Big Data initiative
and predictive analytics across the Informatics Division for
Independence Blue Cross.
 Contact Info:
Phone:215.241.9885
Email: alex.zeltov@ibx.com

3. 3
BIOGRAPHY
Darwin Leung
 Director, Informatics Application Development and
Operations
 Independence Blue Cross
 Responsible for the development of analytical applications
across the Informatics Division for Independence Blue
Cross.
 Contact Info:
Phone:215.241.2255
Email: darwin.leung@ibx.com

4. Background on Text Analytics and
Medical Documents
 Providers have different levels of technology readiness –
varying from Electronic Medical Records (EMR) to paper
charts.
 We want to apply text analytics to all information available for
different business cases.
 Need to bring all information collected to a level where our
technologies can be applied.

5. OCR for medical documents
 OCR (Optical Character Recognition) for medical documents
is useful because this software provides invaluable benefits in
terms of cost savings and even increases productivity.
 High Speed Provided by OCR
 OCR software can provide very good accuracy rates as
manual data entry but in a fraction of the time

6. DB
OCR + Text Analytics Process
IMG/PDF/TIF
DropBox (Share)
ImageMagic
+ OCR
HADOOP Cluster
Store text
+pdf version
of EMR in
HADOOP
Text Analytics / NLP
processing
Results
Clinical
Ontology
Predictive
Models

7. Custom Distributed OCR Application:
High Performance distributed OCR process runs in the background,
sharing resources with the Informatics Big Data HADOOP cluster.
Customized open source tools used in the OCR process:
• Custom distribution and parrallezation framework for OCR
• PDFtk: for normalizing pdf headers and splitting up the PDF
pages
• ImageMagick: used to resize, rotate, increase dpi, apply
various special effects to enhance quality of images. Creates
an image version of the pdf (single page).
• Tesseract OCR:
• extracts the text from a the image file and generates a text files
• generate searchable pdfs by creating meta-data in original pdf
image files

8. OCR Performance Statistics
Per Each Server Node:
• Image Enhancement and Document Slicing + OCR: ≈ 2 sec/pg
• 1,800 pages/hr on 1 node
18 HADOOP Cluster Nodes that run in parallel OCR process:
• 32,400 pages/hr on cluster
• Assuming typical chart 100 pages ≈ 324 charts/hr

9. Text Analytics Components:
Custom text analysis code using Java and Python
• Lucene – tokenization, shingles, n-gramming
• Weka - collection of machine learning algorithms for data mining.
• Advanced Query Language (AQL) - powerful text analytics engine
developed by IBM and used by IBM Watson. Executes extractors in a highly
efficient manner by using the parallelism provided by Informatics HADOOP
platform.
• OpenNLP - hosts a variety of java-based NLP tools which perform sentence
detection, tokenization, part-of-speech tagging, chunking and parsing,
named-entity detection.

10. 10
Clinical
Ontology
DB Repo
Load Ontology
Terms Per Medical
Condition
Tokenize
Stop Word Filters
Ngram / Shingles
Stemming
Generate Token
Permutations
Intermediate
Ontology Tokens
Per Job Type
Hadoop GPFS
Ontology and Preprocessing
Hadoop Text
Analytics MR
Jobs

11. HADOOP
• HADOOP framework is a mechanism for analyzing huge
datasets, which do not have be housed in a datastore
• HADOOP scales out to myriad nodes and can handle all
of the activity and coordination related to data
processing.
• HADOOP Map Reduce is a way to process large data
sets by distributing the work across a large number of
nodes
.

12. HADOOP Components:
• Common – contains libraries and utilities needed by other Hadoop
modules.
• Hadoop Distributed File System (HDFS)
– Distributed file-system that stores data on commodity machines,
providing very high aggregate bandwidth across the cluster.
– HDFS creates multiple replicas of each data block and
distributes them on computers throughout a cluster to enable
reliable and rapid access.
• MapReduce – a programming model for large scale data
processing.

13. HADOOP Components:
• Hbase – is a distributed, column oriented NOSQL database.
• Hive – is a data warehouse system for Hadoop that facilitates easy data
summarization, ad-hoc queries, and the analysis of large datasets.
• Sqoop – is a tool designed for efficiently transferring bulk data between
Hadoop and structured datastores such as relational databases.
• Pig – Scripting platform .
• Oozie – Workflow scheduler.
• Zookeeper – Cluster coordination.
• Mahout – Machine learning library.

14. Map Reduce
14
Map Reduce is a way to process large data sets by distributing the
work across a large number of nodes
• Map:
o Master node partitions the input into smaller sub-problems
o Distributes the sub-problems to the worker nodes
o Worker nodes may do the same process
• Reduce:
o Master node then takes the answers to all the sub-problems
o Combines them in some way to get the output

15. Map Reduce - Word Count Example
http://www.cs.uml.edu/~jlu1/doc/source/report/MapReduce.html

16. Business Cases
 Product Recall
 Entity Extraction from Medical Charts
 Nurse Chart Review Process

17. Business Case 1: Product Recall

18. • The text mining process helps identify the manufacturers that
are on recall list.
• Scheduled report alerts with potential identified members that
match the recall manufacturers.
• Create a database of extracted patient and manufacturer
information.
• The OCR + Text mining process analyzes charts 300+ pages
long on average
Business Case 1: Product Recall

19. Business Case 1: Product Recall
• Generated reports on the OCR results
• BigSheets - Web-based spreadsheet look and feel

20. Business Case 1: Entity Extraction
• Generated reports on the Entity Extraction results
• Create a database of extracted entity information accessible
via jdbc/odbc.

21. Business Case 2: Nurse Chart Review
Process
• The text mining process helps identify conditions and
diagnoses based on the medical ontology matches for the
nurse review.
• The text analytics priorities the charts for nurse review, the
highest scored EMR charts are presented first for the nurse
review process.
• The nurse has the ability to open the text version of the chart
that was created part of the OCR process to the exact
location of the matched terms in the scanned version of chart.

22. Summary
 OCR software
 It can operate at high speeds and often can process batches of
medical documents in various formats (jpg, tiff, gif, pdf, etc.)
 The text data can be stored in a database and then be used for
analytics, predictive modeling and data mining
 This technology provides invaluable benefits in terms of cost
savings and productivity.

23. Q & A

24. Appendix
 HADOOP Ecosystem
 AQL

25. HADOOP
Ecosystem

26. AQL: Advanced Text Analytics
• Powerful Text Analytics engine developed by IBM and used by IBM
Watson on the Jeopardy quiz show.
• A declarative Annotation Query Language (AQL) with familiar SQL-
similar syntax for specifying text analytics extraction programs (or
extractors) with rich, clean rule semantics.
• A runtime engine for executing extractors in a highly efficient
manner by using the parallelism provided by the IBM InfoSphere
BigInsights engine using HADOOP platform.
• Built-in multilingual support for tokenization and part-of-speech
analysis.
• The text analytics system extracts information from unstructured and
semi structured data.

27. AQL

28. Sample AQL
/* Dictionary of minor conditions */
create dictionary minorConditions
from file 'minorConditions.dict'
with language as 'en';
/* Dictionary of major conditions */
create dictionary majorConditions
from file 'majorConditions.dict'
with language as 'en';
/* Extract instances of minor conditions and 'score' 1 for each instance */
create view minor as
extract 1 as disposition,
dictionary 'minorConditions' on R.text as match
from Document R;
/* Extract instances of major conditions and 'score' 2 for each instance */
create view major as
extract 2 as disposition,
dictionary 'majorConditions' on R.text as match
from Document R;
/* Union together all instances */
create view RawDisposition as
(select * from minor)
union all
(select * from major);
/* Aggregate per document score */
create view ConsolidatedDisposition as
select Sum(R.disposition) as disposition
from RawDisposition R;
export view ConsolidatedDisposition;

29. Developing/Testing AQL query

30. Entity Integration

31. END