Semantic Technology for Provider-Payer-Pharma Cross-Industry Data Collaboration Building Intelligent Health Data Integration The cost to cover the typical family of four under an employer health insurance plan is expected to top $20,000 this year. The integration of health data (including electronic health records, health insurer records, pharma research and clinical data, and real-world evidence) will increase transparency and efficiency, improve individual and population health outcomes, and expand the ability to study and improve quality of care. Traditional approaches to data integration and analytics depend on widely understood data and well-defined use cases for analyzing that data. The integration of pharma, provider, payer, and real-world data will identify new ways in which health data can be combined and analyzed to improve quality of care. Semantic technology can speed integration of health data, while supporting an evolutionary approach to developing and leveraging expertise.

1. Semantic Technology
for Provider-Payor-Pharma
Data Collaboration
Building Intelligent Health Data Integration
©2013, Cognizant

2. Healthcare Expenditure as a % of GDP
th
United States ranked 1st in st in Expenditure,in Life Expectancy
United States ranked 1 Expenditure, 27 27th in Life Expectancy
Health expenditure as a share of GDP, OECD countries, 2012
Strong need to drive down the cost of Healthcare while improving Outcomes
Source: OECD Health data, June 2012
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3. Shift to Personalized Medicine and Targeted Therapies
The emerging patient-centric healthcare services will need to be outcomes-driven,
service oriented, and adaptive to respond to human behaviors
Patient Wellness & Quality of
Life Personalized Healthcare
and improved Disease
Management
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Compliance
Connected Personal
Health
Engaging Customers
Interactive & game-based
activity to connect and engage
better with patients to drive
adherence and compliance
01
03
Patient
Centric
Improved Patient
Outcomes
Connected Health
Using Technology to provide
Healthcare remotely
(Care Management)
Cost Containment
04

4. Remote Health Monitoring is a Key Element of Connected Health
Collect
Transmit
Evaluate
Patients
+
Data
Insight
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Engage
Intervene

5. Patient-Centric Integrated Health Data
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6. Big Data in Healthcare
Four distinct big data pools exist in the U.S. health care domain today with little
overlap in ownership and low levels of integration.
Owner
Owner
Pharmaceutical companies;
Academia
Example datasets
Various, including
stakeholders outside of
healthcare
Pharmaceutical
R&D Data
Clinical Trials; Compound
Libraries
Patient
Behavior
Data
Integration of
Data Pools
Required for
Major
Opportunities
Example datasets
Utilization of Care; Costs
Estimates
Claims
and Costs
Data
Patient behaviors &
preferences; Exercise data
captured in running shoes
and wearable health monitors
Owner
Owner
Payers, Providers
Example datasets
Clinical
Data
Providers
Example datasets
Electronic Medical Records;
Medical Images; Prescription
Data
Source: Big Data: The Next Frontier for Innovation, Competition and Productivity; McKinsey Global Institute, May 2011
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7. Semantic Technology “Super Charging” Health Data Integration
Intelligent Health Data Integration Technology Stack
Health Data Exchange Technology Stack
Semantic Technology
CDISC
Expert Knowledge
PRM
Entity Resolution
CDASH
Patient
Behavior
Data
ODM
SDTM
ADaM
SHARE
SEND
Patient
Privacy
Data Virtualization
Data Federation
Claims EDI
Eligibility
Linked Data
Claim
Submission
Claim Status
Services Review
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CDA
CCD
RIM
CCOW
HL7
QRDA
GELLO
ICSR
SPL
Provenance

8. Type 2 Diabetes Research using Semantic Technology
Mayo Clinic used Semantic Web technologies to develop a framework for high
throughput phenotyping using EHRs to analyze multifactorial phenotypes
1
4
Diseasome
Mapped Clinical
Database
to Ontology Model
DBPedia
ChemBL
Find Genes or Biomarkers associated
with T2D, as Published in the Literature
2
5
RxNorm
DailyMed
Clinical DB
Find All FDA-approved T2D Drugs;
Find All Patients Administered these Drugs
Diseasome
RxNorm
ChemBL
DrugBank
Clinical DB
Selected Genes have Strong Correlation to T2D. Find All Patients
Administered Drugs that Target those Genes.
3
6
RxNorm
SIDER
Clinical DB
Find Which of these Patients are having a
Side Effect of Prandin
Diseasome
RxNorm
ChemBL
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Clinical DB
Find All Patients that are on Sulfonylureas, Metformin,
Metglitinides, and Thiazolinediones, or combinations of them
Reprinted with permission from Jyotishman Pathak, Ph.D., Mayo Clinic
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DrugBank

9. Semantic Technology Components
User interface and applications
Trust
Proof
Unifying logic
Querying:
SPARQL
Ontologies:
Predicate
QWL
Rules:
Object
RIF/SWRL
Taxonomies: RDFS
Data interchange: RDF
Syntax: XML
Identifiers: URI
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Character set: UNICODE
Cryptography
Subject

10. Semantic Technology Components
SNOMED Clinical Terms Ontology
Integrating Expertise: Selecting for Hypothyroidism
User interface and applications
Case Medications
Levothyroxine, synthroid,
levoxyl unithroid, armour
thyroid, desicated thyroid,
cytomel, triostat,
liothyronine, synthetic
trilodothyronine, liotrix,
thyrolar
ICD-9 Codes for Hypothyroidism
244, 244.8, 244.9, 245, 245.2, 245.8, 245.9
Abnormal Lab Values
TSH > 5 OR FT4 < 0.5
Trust
ICD-9 Codes for Secondary
Causes of Hypothyroidism
244.0, 244.1, 244.2, 244.3
}
Cryptography
ICD-9 Codes for Post
Surgical or Post Radiation
Case Definition
Hypothyroidism
All three conditions required:
Proof 40930008
sno:40930008for hypothyroidism OR abnormal TSH/FT4
ID
193*, 242.0, 242.1, 242.2,
1. ICD-9 code
sno:40930008 Preferred Name use
Hypothyroidism
242.3, 242.9, 244.0, 244.1,
2. Thyroid replacement medication
244.2, 244.3, 258*
3. Require at least 2 instances of either medication or lab
Pregnancy Exclusion
SPARQL queryID between the first244 last
(abbreviated)
with at least 3 months
and
ICD-9 Codes
icd9:244
CPT Codes for Post
Unifying lab
instance of medication andlogic
Any pregnancy billing code
icd9:244
Preferred Name Acquired hypothyroidism
Radiation Hypothyroidism
or lab test if all Case
77261, 77262, 77263, 77280,
icd9:244.8
ID
244.8
Definition codes, labs, or
77285, 77290, 77295, 77299,
Ontologies:
Rules: acquired
icd9:244.8 DISTINCT ?patientID, ?patientName
medications fall within 6
{Case Exclusions Preferred Name Other specified
SELECT
77300, 77301, 77305, 77310,
QWL
RIF/SWRL
months before pregnancy
Exclude
at any time in
etc.
Querying: if the following information occurshypothyroidism
to one year after
the record:
SPARQL
WHERE
pregnancy
ind:4093008 causes of hypothyroidism
ID
40930008
• Secondary
Exclusion Keywords
V22.1, V22.2, 631, 633,
Taxonomies: RDFS
{
• Post surgical or post radiation hypothyroidism
Multiple endocrine neoplasia,
ind:4093008
Defined By
sno:40930008
633.0, 633.00, 633.1,
• Other thyroid diseases ICD “HYPOTHYROIDISM”
MEN I, MENII, thyroid cancer,
?patient Inclusion
?indication icd9:244
ind:4093008
633.10, 633.20, 633.8,
• Thyroid altering medication
thyroid carcinoma
icd9:244.8
633.80, 633.9, 633.90,
}
645.1, 645.2, 646.8, etc.
DataExclusion ICD RDF
interchange: icd9:631
ind:4093008
icd9:633
Thyroid-Altering Medications
Case Exclusions
Exclusion Keywords
Phenytoin, Dilantin, Infatabs,
Time dependent case exclusions:
Optiray, radiocontrast,
Dilantin Kapseals, Dilantin-125,
Syntax: XML
• Recent pregnancy TSH/FT4
iodine, omnipaque,
Phenytek, Amiocarone
• Recent contrast exposure
visipaque, hypaque,
Pacerone, Cordarone, Lithium,
Conway et al.; Denny et al.
ioversol, diatrizoate,
Eskalith, Lithobid,
iodixanol, isovue,
Methimazole, Tapazole,
Identifiers: URI
Character set: UNICODE
iopamidol, conray,
Northyx, Propylthiouracil, PTU
iothalamate, renografin,
sinografin, cystografin,
Source: SNOMED-CT Ontology, IHTSDOpermission from Jyotishman Pathak, Ph.D., Mayo Clinic
conray, iodipamide
Reprinted with
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11. Semantic Technology Components
User interface and applications
Linked Open Drug Data
(LODD) Cloud
Trust
Proof
Unifying logic
Rules:
RIF/SWRL
Taxonomies: RDFS
Data interchange: RDF
Cryptography
Querying:
SPARQL
Ontologies:
QWL
Source: SemanticSyntax: XML
Web for Health Care and Life Sciences Interest Group
Identifiers: URI
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Character set: UNICODE

12. Linked Data Case Study Highlights
Detecting off label
prescribing based on
adverse events
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Monitoring emerging
therapies for
growing disorder
populations

13. Adding Semantic Technology to Health Data Integration
Gets Us Closer to Solving Connected Health
Semantic Technology
Connected Health
Collaboration
• Population Registry
• Care Management
• Dynamic Care Plan
• Medical Management
• Productivity Management
• Workflow Automation
• Alerts
• Providers, Members
• Community Organizations
Analysis
• Risk Stratification
• Care Engine Rules
• Utilization Trends
• Population Management
• Care Gaps (Trigger)
• Episode Grouper
• Predictive Analysis
• Patient Adherence
• EMPI (Master Person
Record
• Relationships across
data
• Claims
• Lab
• Pharmacy
• Unstructured to structured
usable data
• Extended EMRs
• Member Messaging Engine
• External EHR
• Self-Reported
• Next Gen
• Creation of Cleanest Record
• Identify Opportunities for Action
• Identify Clinical Concepts
Integration
Data
Expert Knowledge
Linked Data
Data Virtualization
HL7
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• Cerner
• Internal EHR
Provenance
Entity Resolution
CDISC
Data Federation
Claims EDI

14. Call for Action
1
2
3
4
Assess
Identify
Define
Execute
how the patientcentric model affects
your programs
the relevant patient
behavior data that
you can use
use cases that drive
from the disease
state perspective
projects that rapidly
achieve capabilities,
but don’t try to boil
the ocean



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Pilots
Proofs of Concept
Agile, Incremental
Development

15. Q&A
©2013, Cognizant

16. Speakers
Nagaraja Srivatsan, Senior Vice President, Cognizant
Srivatsan has more than two decades of experience in the Information
Technology industry and deep knowledge of the Healthcare & Life Sciences
domain. Srivatsan drives Cognizant’s strategy in Healthcare and Life
Sciences.
Srivatsan was recognized as one of the top 100 most inspiring people in the
life sciences industry award by PharmaVOICE publication and has been
regularly quoted in national and global magazines like CIO, PharmaVoice,
and CNNFn.
Thomas (Tom) Kelly – Practice Director, EIM Life Sciences
Thomas is a Practice Leader in Cognizant’s Enterprise Information
Management (EIM) Practice, with over 30 years of experience, focusing on
leading Data Warehousing, Business Intelligence, and Big Data projects that
deliver value to Life Sciences and related health industries clients.
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17. Thank you
©2013, Cognizant