©2013, Cognizant

Semantic Technology for Provider-Payor-Pharma

Data Collaboration

Building Intelligent Health Data Integration

| ©2013, Cognizant

Healthcare Expenditure as a % of GDP

1

Source: OECD Health data, June 2012

United States ranked 1st in Expenditure, 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

United States ranked 1st in Expenditure, 27th in Life Expectancy

| ©2013, Cognizant

Shift to Personalized Medicine and Targeted Therapies

2

Connected Health

Using Technology to provide Healthcare remotely

(Care Management)

Engaging Customers

Interactive & game-based activity to connect and engage

better with patients to drive

adherence and compliance

Patient Wellness & Quality of

Life Personalized Healthcare

and improved Disease

Management

Patient

Centric

Imp

rove

d P

atie

nt

Ou

tco

me

s

Compliance

Co

nn

ec

ted

Pe

rso

nal

Hea

lth

Cost Containment

01

02

03

04

The emerging patient-centric healthcare services will need to be outcomes-driven,

service oriented, and adaptive to respond to human behaviors

| ©2013, Cognizant

Remote Health Monitoring is a Key Element of Connected Health

3

Collect Engage Transmit Evaluate Intervene

Insight

Patients

+

Data

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Patient-Centric Integrated Health Data

4

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Big Data in Healthcare

5

Integration of

Data Pools

Required for

Major

Opportunities

Patient

Behavior

Data

Clinical

Data

Pharmaceutical

R&D Data

Claims

and Costs

Data

Owner

Example datasets

Various, including

stakeholders outside of

healthcare

Patient behaviors &

preferences; Exercise data

captured in running shoes

and wearable health monitors

Owner

Example datasets

Providers

Electronic Medical Records;

Medical Images; Prescription

Data

Owner

Example datasets

Pharmaceutical companies;

Academia

Clinical Trials; Compound

Libraries

Owner

Example datasets

Payers, Providers

Utilization of Care; Costs

Estimates

Four distinct big data pools exist in the U.S. health care domain today with little

overlap in ownership and low levels of integration.

Source: Big Data: The Next Frontier for Innovation, Competition and Productivity; McKinsey Global Institute, May 2011

| ©2013, Cognizant

Semantic Technology “Super Charging” Health Data Integration

6

Patient

Behavior

Data

GELLO

CDA

RIM

CCD

QRDA

CCOW SPL

ICSR

HL7 Eligibility

Claim

Submission

Claim Status

Services Review

Claims EDI

SEND

PRM

SDTM

ODM

CDASH

SHARE

ADaM

CDISC

Patient

Privacy

Health Data Exchange Technology Stack Intelligent Health Data Integration Technology Stack

Semantic Technology

Expert Knowledge

Data Federation Data Virtualization

Linked Data

Entity Resolution

Provenance

| ©2013, Cognizant

Type 2 Diabetes Research using Semantic Technology

7

Mapped Clinical

Database

to Ontology Model

Find All FDA-approved T2D Drugs;

Find All Patients Administered these Drugs

Mayo Clinic used Semantic Web technologies to develop a framework for high

throughput phenotyping using EHRs to analyze multifactorial phenotypes

RxNorm DailyMed Clinical DB

Find Which of these Patients are having a

Side Effect of Prandin

RxNorm SIDER Clinical DB

1

2

3

4

5

6

Find Genes or Biomarkers associated

with T2D, as Published in the Literature

Diseasome DBPedia ChemBL

Selected Genes have Strong Correlation to T2D. Find All Patients

Administered Drugs that Target those Genes.

Diseasome RxNorm ChemBL DrugBank Clinical DB

Find All Patients that are on Sulfonylureas, Metformin,

Metglitinides, and Thiazolinediones, or combinations of them

Diseasome RxNorm ChemBL DrugBank

Reprinted with permission from Jyotishman Pathak, Ph.D., Mayo Clinic

Clinical DB

| ©2013, Cognizant

Semantic Technology Components

8

User interface and applications

Trust

Unifying logic

Proof

Cry

pto

gra

ph

y

Rules:

RIF/SWRL

Ontologies:

QWL Querying:

SPARQL Taxonomies: RDFS

Data interchange: RDF

Syntax: XML

Identifiers: URI Character set: UNICODE

Subject Predicate Object

| ©2013, Cognizant

User interface and applications

Trust

Unifying logic

Proof

Cry

pto

gra

ph

y

Rules:

RIF/SWRL

Ontologies:

QWL Querying:

SPARQL Taxonomies: RDFS

Data interchange: RDF

Syntax: XML

Identifiers: URI Character set: UNICODE

Semantic Technology Components

9

Integrating Expertise: Selecting for Hypothyroidism

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

ICD-9 Codes for Secondary

Causes of Hypothyroidism

244.0, 244.1, 244.2, 244.3

Abnormal Lab Values

TSH > 5 OR FT4 < 0.5

Case Definition

All three conditions required:

1. ICD-9 code for hypothyroidism OR abnormal TSH/FT4

2. Thyroid replacement medication use

3. Require at least 2 instances of either medication or lab

with at least 3 months between the first and last

instance of medication and lab

Case Exclusions

Exclude if the following information occurs at any time in

the record:

• Secondary causes of hypothyroidism

• Post surgical or post radiation hypothyroidism

• Other thyroid diseases

• Thyroid altering medication

Case Exclusions

Time dependent case exclusions:

• Recent pregnancy TSH/FT4

• Recent contrast exposure Conway et al.; Denny et al.

Reprinted with permission from Jyotishman Pathak, Ph.D., Mayo Clinic

Pregnancy Exclusion

ICD-9 Codes

Any pregnancy billing code

or lab test if all Case

Definition codes, labs, or

medications fall within 6

months before pregnancy

to one year after

pregnancy

V22.1, V22.2, 631, 633,

633.0, 633.00, 633.1,

633.10, 633.20, 633.8,

633.80, 633.9, 633.90,

645.1, 645.2, 646.8, etc.

Exclusion Keywords

Optiray, radiocontrast,

iodine, omnipaque,

visipaque, hypaque,

ioversol, diatrizoate,

iodixanol, isovue,

iopamidol, conray,

iothalamate, renografin,

sinografin, cystografin,

conray, iodipamide

ICD-9 Codes for Post

Surgical or Post Radiation

Hypothyroidism

193*, 242.0, 242.1, 242.2,

242.3, 242.9, 244.0, 244.1,

244.2, 244.3, 258*

CPT Codes for Post

Radiation Hypothyroidism

77261, 77262, 77263, 77280,

77285, 77290, 77295, 77299,

77300, 77301, 77305, 77310,

etc.

Exclusion Keywords

Multiple endocrine neoplasia,

MEN I, MENII, thyroid cancer,

thyroid carcinoma

Thyroid-Altering Medications

Phenytoin, Dilantin, Infatabs,

Dilantin Kapseals, Dilantin-125,

Phenytek, Amiocarone

Pacerone, Cordarone, Lithium,

Eskalith, Lithobid,

Methimazole, Tapazole,

Northyx, Propylthiouracil, PTU

Source: SNOMED-CT Ontology, IHTSDO

SNOMED Clinical Terms Ontology

sno:40930008 ID 40930008

sno:40930008 Preferred Name Hypothyroidism

icd9:244 ID 244

icd9:244 Preferred Name Acquired hypothyroidism

icd9:244.8 ID 244.8

icd9:244.8 Preferred Name Other specified acquired

hypothyroidism

ind:4093008 ID 40930008

ind:4093008 Defined By sno:40930008

ind:4093008 Inclusion ICD icd9:244

icd9:244.8

ind:4093008 Exclusion ICD icd9:631

icd9:633

{ SELECT DISTINCT ?patientID, ?patientName WHERE { ?patient ?indication “HYPOTHYROIDISM” } }

SPARQL query (abbreviated)

| ©2013, Cognizant

User interface and applications

Trust

Unifying logic

Proof

Cry

pto

gra

ph

y

Rules:

RIF/SWRL

Ontologies:

QWL Querying:

SPARQL Taxonomies: RDFS

Data interchange: RDF

Syntax: XML

Identifiers: URI Character set: UNICODE

Semantic Technology Components

10

Source: Semantic Web for Health Care and Life Sciences Interest Group

Linked Open Drug Data

(LODD) Cloud

| ©2013, Cognizant

Linked Data Case Study Highlights

11

Detecting off label prescribing based on

adverse events

Monitoring emerging therapies for growing disorder populations

| ©2013, Cognizant

Adding Semantic Technology to Health Data Integration

Gets Us Closer to Solving Connected Health

12

Semantic Technology

Expert Knowledge

Data Federation

HL7 CDISC Claims EDI

Data Virtualization

Provenance Linked Data

Entity Resolution

Connected Health

Collaboration

Analysis

Integration

Data

• Population Registry

• Care Management

• Dynamic Care Plan

• Medical Management

• Productivity Management

• Workflow Automation

• Alerts

• Providers, Members

• Community Organizations

• 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

• Unstructured to structured

usable data

• Extended EMRs

• Member Messaging Engine

• Creation of Cleanest Record

• Identify Opportunities for Action

• Identify Clinical Concepts

• Claims

• Lab

• Pharmacy

• External EHR

• Self-Reported

• Next Gen

• Cerner

• Internal EHR

| ©2013, Cognizant

Call for Action

13

1

Assess

2 3 4

Identify Define Execute

how the patient-centric 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

Pilots Proofs of Concept Agile, Incremental

Development

©2013, Cognizant

Q&A

| ©2013, Cognizant

Speakers

15

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.

©2013, Cognizant

Thank you