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IBM Watson Content AnalyticsDiscover Hidden Value in Your Unstructured Data
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ABOUT PERFICIENT
Perficient is a leading information
technology consulting firm serving
clients throughout North America.
We help clients implement business-driven technology
solutions that integrate business processes, improve
worker productivity, increase customer loyalty and create
a more agile enterprise to better respond to new
business opportunities.
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PERFICIENT PROFILE
• Founded in 1997
• Public, NASDAQ: PRFT
• 2013 revenue $373M
• Major market locations:
Allentown, Ann Arbor, Atlanta, Boston, Charlotte,
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Milwaukee, Minneapolis, New York City, Northern
California, Oxford (UK), Philadelphia, Southern
California, St. Louis, Toronto, Washington, D.C.
• Global delivery centers in China and India
• >2,600 colleagues
• Dedicated solution practices
• ~90% repeat business rate
• Alliance partnerships with major technology vendors
• Multiple vendor/industry technology and growth awards
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INDUSTRIES Healthcare
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PORTALPortal Frameworks
SearchSecurityWeb AnalyticsWeb Content Management
Social & CollaborationMobilityExperience Design
INTEGRATIONIntegration Frameworks
Cloud ArchitectureReference Architecture
Application IntegrationEnterprise Application IntegrationService Oriented Architecture
Process & Content IntegrationBusiness Process ManagementComplex Event ProcessingRules Engines
DATA & CONTENTBusiness Analytics
Business IntelligencePredictive AnalyticsReporting
Structured Data ManagementData Integration, Quality & GovernanceEnterprise Data WarehouseMaster Data Management
Unstructured Data ManagementBig DataContent IntelligenceContent Management
Enterprise Search
CUSTOMER EXPERIENCECustomer 360
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Management Consulting
OUR SOLUTIONS PORTFOLIO
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INTRODUCTIONS
Christine LivingstonSenior Project Manager, Enterprise Content Intelligence, Perficient
Christine Livingston leads Perficient's
Advanced Case Management and Watson
Content Analytics practice and works in
collaboration with IBM to develop industry-
leading solutions that incorporate IBM's case
management, information lifecycle
governance, enterprise content management,
and business process management
technologies.
David MeintelDirector, IBM Channel Development ,
Executive – Healthcare, Perficient
David Meintel has 20 years of experience in
data warehousing and analytics in a wide
range of industries. He has worked with a
large number of provider and health plan
organizations to assist them in better
leveraging their data assets. David manages
the healthcare organizations relationship with
IBM and assists in the development of new
offerings for healthcare clients.
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AGENDA
• Introduction
– Why do we need Content Analytics?
– Where does Watson Content Analytics fit?
• Overview
– How is unstructured content analyzed?
– How is Watson different?
– How is it applied?
• Healthcare Examples
• Healthcare Accelerators
• Demonstration
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“There were 5 exabytes of
information created between the
dawn of civilization through
2003, but that much information
is now created every 2 days,
and the pace is increasing.”
Google CEO Eric Schmidt, August 2010
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BIG DATA: WHY WCA?
90% of the world’s data was created in the
last two years
80%of the world’s data today is unstructured
1 Trillionconnected devices
generate 2.5 quintillion bytes
data / day
CONTENT
Volume
12 terabytes of Tweets created
daily
Velocity
5 million trade events per
second
Variety
Structured, unstructured, multimedia,
textVeracity
Uncertainty from
inconsistency, ambiguities
15 petabytes of new
information daily
80% information
growth is unstructured content……
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STRUCTURED VS. UNSTRUCTURED DATA
Column Value
Patient Joe Brown
Date of Birth 02/13/1972
Date Admitted 02/05/2014
Structured Data
High Degree of organization, such as
a relational database
“The patient came in complaining of chest
pain, shortness of breath, and lingering
headaches…smokes 2 packs a day… family
history of heart disease…has been
experiencing similar symptoms for the past
12 hours….”
Unstructured Data
Information that is difficult to organize
using traditional mechanisms
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WATSON CONTENT ANALYTICS OVERVIEW
80% 100%of enterprise content
is unstructured
of social content
is unstructured
Watson Content Analytics mines unstructured content to provide a
holistic and contextual understanding – the “Why” behind the “What”
• Analyzing structured data only
gives you a partial view of the
world around you
• Only 20 percent of enterprise
content is structured
• Data analytics gives you the who,
what, where and when of a subject
• Mining unstructured content
gives you a comprehensive
understanding of the world
around you
• 80 percent of enterprise content
is unstructured
• Content analytics distinctively
adds the why and the how
What is happening? Why is it happening?
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ANALYZING UNSTRUCTURED CONTENT
explorer
India
In May
1898
India
In May
celebrated
anniversary
in Portugal
In May, Gary arrived in India after
he celebrated his anniversary in
Portugal
Portugal
400th
anniversary
celebrated
Gary
In May, 1898 Portugal celebrated the
400th anniversary of this explorer’s
arrival in India
This evidence suggests
“Gary” is the answer
BUT the system must
learn that keyword
matching may be weak
relative to other types of
evidence
arrived in
arrival in
Legend
Keyword “Hit”
Reference Text
Answer
Weak evidenceRed Text
Answering complex natural language questions requires more than keyword evidence
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THE WATSON DIFFERENCE:
27th May 1498
Vasco da
Gama
landed in
arrival in
explorer
India
Para-
phrases
Geo-
KB
Date
Match
Stronger evidence can
be much harder to find
and score …
… and the evidence is still
not 100% certain
Search far and wide
Explore many hypotheses
Find judge evidence
Many inference algorithms
On the 27th of May 1498, Vasco da
Gama landed in Kappad Beach
400th anniversary
Portugal
May 1898
celebrated
In May, 1898 Portugal celebrated the
400th anniversary of this explorer’s arrival
in India.
Kappad Beach
Legend
Temporal Reasoning
Reference Text
Answer
Statistical Paraphrasing
GeoSpatial Reasoning
LEVERAGING MULTIPLE ALGORITHMS
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WATSON CONTENT ANALYTICS APPLIED
PreProcessing
NaturalLanguageProcessing
ContentAnalysis
PostProcessing
Color Key
Disease / Not a Disease
Symptom / Not a Symptom
Drug / Dosage
Patient / Doctor
Procedure
• Language Identification• Lexical Analysis• Classification• Disambiguation• Entity Extraction• Fact Extraction• Concept Extraction• Relationship Extraction• Inferencing
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WATSON CONTENT ANALYTICS APPLIED
• Accurately identify and extract facts from text including negation
– “55%” = LVEF
– “Patient does not show signs” = Negative Symptom
• Accurately interpret and assign values to ambiguous statements
– “Shows slightly elevated levels” = If condition A=10%, if condition B=20%
• Infer meaning from non-contextual content
– “Cut back from two packs to one per day” = Smoker
• Cleanse, enhance and normalize raw data
– “Myocardial infarction” and “heart attack” = equal same thing
– Enhance or augment by assigning correct RxNorm, SNOMED, ICD-10 or other codes /
terminology
• Preserve and structure facts and concepts from contextual content
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HEALTHCARE USE CASES
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READMISSION PREDICTORS AT SETONTHE VALUE OF UNSTRUCTURED DATA
The Data We Thought Would Be Useful … Wasn’t
• Structured data not available, not accurate enough, without the unstructured data - which was more trustworthy
What We Thought Was Causing 30 Day Readmissions … Wasn’t
• 113 possible candidate predictors expanded and changed after mining the data for hidden insights
New Hidden Indicators Emerged … Readmissions is a Highly Predictive Model
• 18 accurate indicators or predictors
Predictor Analysis % EncountersStructured Data
% Encounters
Unstructured Data
Ejection Fraction (LVEF)
2% 74%
Smoking Indicator 35%(65% Accurate)
81%(95% Accurate)
Living Arrangements <1% 73%(100% Accurate)
Drug and Alcohol Abuse
16% 81%
Assisted Living 0% 13%
49% at 20th percentile
97% at 80th percentile
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1. Jugular Venous Distention Indicator
2. Paid by Medicaid Indicator
3. Immunity Disorder Disease Indicator
4. Cardiac Rehab Admit Diagnosis with CHF Indicator
5. Lack of Emotion Support Indicator
6. Self COPD Moderate Limit Health History Indicator
7. With Genitourinary System and Endocrine Disorders
8. Heart Failure History
9. High BNP Indicator
10. Low Hemoglobin Indicator
11. Low Sodium Level Indicator
12. Assisted Living
13. High Cholesterol History
14. Presence of Blood Diseases in Diagnosis History
15. High Blood Pressure Health History
16. Self Alcohol / Drug Use Indicator
17. Heart Attack History
18. Heart Disease History
0123456789
101112131415161718
0 1 2 3 4 5 6
Ran
kingofStrengthofModelV
ariable
ProjectedOddsRa o
18 17 16 15 14 13 12 11 109 8 7 6 5 4 3 2 1
READMISSION PREDICTORS AT SETONTOP 18 FACTORS
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• Top indicator JVDI not on the original list of 113 - as well as
several others
• Assisted Living and Drug and Alcohol Abuse emerged as
key predictors - only found in unstructured data
• LVEF and Smoking are significant indicators of CHF but not
readmissions
• A combination of actionable and non-actionable risk factors
READMISSION PREDICTORS AT SETONNEW INSIGHTS UNCOVERED BY COMBINING CONTENT & PREDICTIVE ANALYTICS
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RADIOLOGY DIAGNOSIS NOTES
Case: Patient sent for a chest scan to determine if pneumonia exists.
Radiologist examines scan results and documents findings in a combination of
structured and unstructured data.
No sign of fluid or other indicators of pneumonia are present within patient. Observed
suspicious dark area that should be followed up with primary physician for further diagnostics.
Without content analytics reviewing
unstructured notes, the secondary
finding might go unnoticed leading to
further complications.
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MEMBER CHURN FOR HEALTH PLANS
Call center operator fields calls from Members on a wide range of topics. Operator
documents important aspects of the call in the notes as call is resolved.
Leverage content analytics to analyze unstructured notes to identify members with a
high-risk of changing health plans.
Case: Desire to reduce Member Churn by
identifying unsatisfied Members
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WCA AND EPIC INTEGRATION
• Care providers are adopting electronic medical records but traditional doctors’ notes still play an important role in tracking & managing patients
• Q1 2014: Integration testing with the Epic EMR 2014 release & IBM Advanced Care Insights for Natural Language Processing (NLP) has been successfully completed, solidifying leadership of both companies in their respective markets
What’s new?
• Traditionally a manual process, IBM’s software can analyze doctors’ notes & transform them into a format that can be readily uploaded into the patient record, including automatically adding industry standard diagnosis & treatment codes
• Allows doctors to accurately capture information from unstructured text in real-time, to improve patient outcomes & simplify administrative processes
What value does this provide?
• Empowers the Health Systems that have adopted Epic to capture actionable insight from IBM’s NLP capabilities – the same technology utilized in the revolutionary Watson cognitive system
What does this mean?
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ACCELERATORS &
DEMONSTRATION
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HEALTHCARE ACCELERATORS
• Problems
– Result of a series of interim annotations that identify diseases, symptoms, and disorders
– Normalize to standard terms and standard coding systems including SNOMED CT, ICD-9, HCC, CCS
– Capture timeframes of the problem
• Past or current problem
– Determine confidence
• Positive, Negative, Rule Out
• Negation example
• “abdominal pain”
• Procedures
– Identify compound procedures
– Normalize to standard terms and standard coding systems including SNOMED CT, CCS, CPT
– Capture timeframes of the procedure
• Medications
– Series of interim annotations that identify drugs, administrations, measurements
– Normalize to standard terms RxNorm
• Demographic and Social
– Patient Age
– Living Arrangement
– Employment status
– Smoking status
– Alcohol use
• Compliance & Noncompliance
– Patient's history of medication compliance
with directions such as "take all doses,
even if you feel better earlier“
– Noncompliance - Patient's history of
medication noncompliance with
directions.
• Labs results
– Type of lab test performed, unit of
measure, result value
• Ejection Fraction – in support of CHF use
cases
• Coding Systems – can identify these codes
– CPT
– CCS
– HCC
– NDC ( National Drug Codes)
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WCA INTERFACES
Content Analytics Miner – Primary user interface for unstructured content analysis. Provides
customizable dashboard views to identify deviations, trends, patterns, etc.
Content Analytics Studio – Eclipse interface to create custom UIMA annotators, rules,
dictionaries, noise filters, etc.
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IBM WATSON CONTENT ANALYTICS
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THINGS OF INTEREST
10 Tech Trends Impacting Healthcare in 2015
Download the Trend Guide Today
www.Perficient.com > Thought Leadership > White Papers
HIMSS15 – April 12-15, 2015 – Chicago, IL
Our healthcare experts will be on hand to answer your questions.
Email [email protected] to set up a meeting, or stop by booth #4460.
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QUESTIONS?
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APPENDIX
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WCA DATA SOURCES (BUILT-IN CRAWLERS)
• Content Management
– IBM Content Manager
– EMC/Documentum
– FileNet P8
– SharePoint
• Database Platforms
– DB2
– IBM IMS
– Microsoft SQL Server
– MySQL
– Oracle
– Sybase
• IBM Case Manager
• Email, File Systems, Web
– Microsoft Exchange Server
– UNIX file systems
– Windows 2008 Server
– Web servers
• WebSphere Portal
– IBM WebSphere Portal
– IBM Web Content Manager
– Lotus Quickr
• IBM Connections
• Lotus Domino
– Lotus Notes
– Lotus Quickr for Domino
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WCA INTEGRATION• Cognos
– Dynamically search and explore content for new business insight
– Quickly generate Cognos BI reports
– Integrate unstructured content with structured content to deliver key
insight
• SPSS Analytics Systems
– Combines the power of analyzing the past and present with the
predictive analysis capabilities of SPSS
• IBM Content Classification
– Improve search quality to return highly relevant documents
– Train and improve classification in ICC by exporting from WCA
• Advanced Case Management
– Performs full text index unstructured analytics on content objects
– Provides a connector that crawls case folders indexing both metadata
and document contents
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WCA – HOW IT WORKS
Analyzed Content (and Data)
“Owner” “reports” “check engine lite”“flashes” “after refueling”
...
Source InformationCorporate (Contact Center, Test Data, Dealer notes, ECM, etc.) and External (NHTSA, Edmunds, Consumer Reports,
MotorTrend etc.)
Noun Verb Noun Phrase Prep Phrase
Person Issue Warning Driver action
Component Issue: “Engine Light”Situation: “Refueling”
ExtractedConcept
Content AnalyticsUIMA Pipeline + Annotators
Fine grain control over the entities and facets that are created
Content Analytics Crawlers
IBM Master Data Mgmt
RDB
Real-time NLP REST API
Content Push API
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WCA v3.5 SYSTEM ARCHITECTURE
DocumentCache
Raw DataStore
Scheduler LoggingControl ConfigurationMonitor Security
Common Infrastructure
Exporter
Crawler Framework
ThumbnailIndex
Facet CountSub Index
TaxonomyIndex
SearchIndex
CustomCrawler
QuickPlaceCrawler
Domino Doc Crawler
NotesCrawler
SharePointCrawler
ExchangeCrawler
NNTPCrawler
DB2Crawler
ContentIntegratorCrawler
DB2Content Mgr
Crawler
FileNet P8Crawler
WebCrawler
Seed ListCrawler
WebContent Mgr
Crawler
WebSpherePortal
Crawler
Agent forFile System
Crawler
Global Processing
Web LinkAnalysis
ThumbnailGeneration
Collection
Export
Plu
g-in
Contents Miner UI
Admin UI
EnterpriseSearch UI
RESTApplication
Real-time NLPApplication
Document Processor
Document Processor
Document Processor
ParserDocument Generator
Annota
tor
Annota
tor
Annota
tor
UIMA
Text Analytics& SearchRuntime
Inspector
CustomPoint
RDB
Cra
wle
r
Plu
g-in
JDBC DBCrawler
Win FSCrawler
Unix FSCrawler
Importer Framework
CSVImporter
Case MgrCrawler
DocumentCategorizer
DocumentCluster
Term ofInterest
SIAPIApplication
CA Studio
Cognos BIIntegration
Cognos BI
XML CSV
CSV
Social MediaCrawler
RDF
Indexer
Indexer Service
RDFStore
