Combining Patient Records, Genomic Data and Environmental Data to Enable Translational

Medicine

Martin Sizemore, Principal, Healthcare StrategistMike Grossman, Practice Director, Clinical Data Warehousing & Analytics, Life Sciences

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Perficient is a leading information technology consulting firm serving clients throughout

North America and Europe.

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.

About Perficient

• Founded in 1997

• Public, NASDAQ: PRFT

• 2013 revenue ~$373 million

• Major market locations throughout North America• Atlanta, Boston, Charlotte, Chicago, Cincinnati, Columbus,

Dallas, Denver, Detroit, Fairfax, Houston, Indianapolis, Los Angeles, Minneapolis, New York City, Northern California, Oxford (UK), Philadelphia, Southern California, St. Louis, Toronto and Washington, D.C.

• Global delivery centers in China, Europe and India

• >2,200 colleagues

• Dedicated solution practices

• ~85% repeat business rate

• Alliance partnerships with major technology vendors

• Multiple vendor/industry technology and growth awards

Perficient Profile

• Oracle Platinum Partner

• Oracle Certified Education Training Partner

• 12+ year relationship of loyalty and trust

• Hundreds of successful implementations

• Over 200 delivery consultants on-shore and off-shore

• Five pillar practices

Oracle Partnership

Healthcare Practice

ConnectedHealth

Business Intelligenceand Analytics

Interoperabilityand Integration

InformationExchange

RegulatoryCompliance

Solutions & Services

Experts in Consumer-Driven Healthcare Technology

HEALTH PLAN PROVIDER

CONSUMERS

Select Clients

Glo

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Cen

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/Offs

hore

Del

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Dom

estic

Del

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Life Sciences PracticePr

actic

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Solu

tions

ImplementationMigration

Integration

ValidationConsultingUpgrades

Managed ServicesApplication Development

Private Cloud Hosting

Application SupportSub-licensingStudy Setup

Services

Deep Clinical and Pharmacovigilance Applications Expertise

Clinical TrialManagement

Clinical Trial Planning and BudgetingOracle ClearTrial

CTMSOracle Siebel CTMS / ASCEND

Mobile CRA

Clinical Data Management & Electronic Data Capture

CDMSOracle Clinical

Electronic Data CaptureOracle Remote Data Capture

Oracle InForm

Medical CodingOracle Thesaurus Management System

Safety &Pharmacovigilance

Adverse Event ReportingOracle Argus Safety Suite

Oracle AERS / EmpiricaTraceAxway Synchrony Gateway

Signal ManagementOracle Empirica Signal/Topics

Medical CodingOracle Thesaurus Management System

Clinical DataWarehousing & Analytics

Clinical Data WarehousingOracle Life Sciences Data Hub

Clinical Data AnalyticsOracle Clinical Development Analytics

JReview

Data Review and CleansingOracle Data Management Workbench

Clients

Introductions

Welcome & Introductions

Martin Sizemore, Principal Healthcare StrategistMartin Sizemore is a healthcare strategist, senior consultant and trusted C-level advisor for healthcare organizations including both payers and providers. He specializes in clinical data warehousing, clinical data models and healthcare business intelligence for improving operational efficiencies and clinical outcomes.

Mike Grossman, Practice Director, Clinical Data Warehousing and AnalyticsMike Grossman has over 27 years in the life sciences industry including 10 years of experience designing and developing the Oracle Life Sciences hub for Oracle. Since 2010, Mike has been the CDW/CDA practice lead, where he leads the team that implements, supports, enhances and integrates Oracle’s LSH and other data warehousing and analytics solutions. Mike has many years of experience managing data for all phases and styles of clinical trials.

What is Translational Medicine?

• Targeted therapies that address the unique biological mechanisms involved in a patient’s illness

• Medicines will become truly “personalized,” allowing for a fully customized approach to health care

• Translating scientific advances into targeted therapies has not proven to be quick or easy

• Taking advantage of innovative clinical trial designs could lead to more efficient clinical trials that do a better job of matching treatments to specific patient populations and speed the development of targeted therapies

Why is a New Approach Needed?

• Our current clinical trial and drug regulatory process – the formal system by which novel medicines are evaluated and approved by the U.S. Food and Drug Administration (FDA) – has lagged behind advances in scientific research

• Many have suggested that novel clinical trial designs could capitalize on our growing knowledge of patient subpopulations for which a therapy may be more effective without compromising FDA’s rigorous safety standards

• One of the most promising areas for investigation is oncology

Where Do We Start?

• Need for an integrated approach from the electronic medical record to population subgroups (cohorts) and their related genomics, proteomics and biomarkers

• Ability to manage increasing complexity, data volume and computation power necessary for success

Routine testsCarrier testingSimple MendelianPre‐natal testing

Complex diseaseCardiologyImmunologyPathogenic

PharmacogenomicsAdverse reactionsDosing frequencyDose size

OncologyTumor profilingResidual disease testingProgression analysis

Challenges • Scalability• System interoperability• Speed of knowledge delivery• Evolution of traditional care models• Regulatory implications

Long Term Reference Architecture Plan

Data Integration and Analytics Vision

Master Person Index

Patients Service Providers

Epic

Source Systems

Data Staging(HDI)

Cerner

GE Centricity 

Lawson 

Research Data

Other Sources

(HDI)(HDI)

(HDI)Staging Tables 

(HDI)(HDI)

(HDI)Integrated Storage Tables 

Integrated Data Storage Data Marts Reporting/Analytics

EHA

The integration of environmental data is a great example!

• Far too many Americans -- about 25 million people -- are intimately acquainted with the symptoms of an asthma attack. When asthma strikes, your airways become constricted and swollen, filling with mucus. In severe cases, asthma attacks can be deadly. They kill more than 3,000 people every year in the United States.

• Asthma is a chronic, sometimes debilitating condition that has no cure. It keeps kids out of school (for a total of more than 10 million lost school days each year, according to the Centers for Disease Control) and sidelines them from physical activity. Employers lose 14 million work days every year when asthma keeps adults out of the workplace. The disease is also responsible for nearly 2 million emergency room visits a year.

• Roughly 30 percent of childhood asthma is due to environmental exposures, costing the nation $2 billion per year.

What About External Data?

Source Systems

Healthcare Data Model (EHA)

EPIC(CHCO)

Research & Other

Lawson(UCH)

GE Centricity(UPI)

An Integration Solution

Analytic M

odels

End‐User Analytic Interface

Analytic Data

Enc

Costing BillingClinic Schlg

SvcRnd

AdvEvents

Med Mgmt

Lab Orders

Atmospheric Data

EPIC(UCH)

Master D

ata

Pt Demo

Event Date MedsSvc 

Master

Enc Type FacLocationDx

Svc Pvdr

ChgMaster

Fee Sch

PtFamilial Rel

Insurers

OmicsD

ata Spec‐imens

Studies VariantsSeq‐uences

FilesGene 

Compo‐nents

Genes

Species

Proteins

Chromo‐somes

Path‐ways

Nomen‐clature

Anon

ymize

r

Personalized Medicine

Research

Analytic Data Marts

Cohorts Diag‐nosis

Diag test DX

Ethnicity Medications

History Pro‐cedures

Spec‐imen Study

• Pre-defined models such as Oracle’s EHA already has the data structured from the patient record and other systems

• Vocabulary (for example ICD-10) should be unified as part of the loading process to allow for aggregated analysis across data sources

• Domain areas selected for other purposes like encounter and complaint may be used for analysis along with genomics and proteomics sample results

• Are there additional domains of clinical data that we need to add to enable effective research analysis?

• Pre-existing analysis data marts downstream form the data storage such Oracle’s Translational Research Center provide analytical models and can be extended as needed

Structured Patient Data Re-Used for Research

• In the long run, omics can play a big role in personalizing the treatment of patients

• Research looking for patterns in genomic and other variants can greatly improve the targeting of research results to specific patient populations

• What is the current policy and approach on when and omicssamples are taken and stored?

• The goal is to take full advantage of existing approaches before requiring any changes

• Pathology results where the data has already been curated are necessary before looking at non-curated omics samples

Role of Omics Samples

Integration, PHI and Anonymization

• In the Translational Research Center, patient data can be linked to the omics data

• How do we link the information?

• The use of both patient data and omics data can potentially reveal PHI that is not explicitly needed for the research.

• Depending on how the analysis performed, some results could go down to the patient level

• The data marts should detenify some simple information such as birth date

• What processes, procedures and controls need to be put into place to use the research data for research without compromising PHI? How has this been handled in the past?

• What role does consent play in the delivery of research data and does it need to be enforced electronically? If so, are the desired algorithms defined?

• What are the sources for the omics and other sample data?

• What format will that data be available in?

• There are potentially > 100 different possible data formats (http://en.wikipedia.org/wiki/List_of_sequence_alignment_software)

• This can be based on the highest priority set of sample sources. For example, if the desired samples are being analyzed using an illumina HiSeq 2500, you will get a different selection of output formats than a machine from Roche.

• What will the transport mechanism be? Files (most likely) or direct integration?

Consolidation of Cross Source Studies

Reference Data for Human Genome

• When analyzing omics data, most analysis is performed by comparing your samples to a set of references and variants

• There are several reference variants available for example

• Mutation Annotation Format (MAF) (From NCI)

• miRbase (mirbase.org)

• dbSNP (ncbi.nlm.nih.gov/SNP/)

• RefGene (refgene.com)

• The following life cycle is typical for analysis

• Prepare a question to create a cohort of patients based on clinical criteria

• Refine that cohort based on some genomics characteristics

• Look at a series of hypotheses based on that refined cohort looking across a broader set of clinical characteristics

• Draw conclusions and refine

• Formalize results

• What tools are required to access the data?

• What analytical methods are commonly used?

Analysis Lifecycle, Methods and Tools

Preparation

Selection & Exploration

Analytics & Model Building

Deployment & Reuse

• Once an analysis has been completed, where are the results stored?

• Are the cohorts and methods used recorded as part of the analysis?

• Are these methods and cohorts available for future use by other users and studies?

Analysis Results Management

• We need to set the initial priorities for preparing and integrating the clinical and samples data in order to create an implementation plan

• Are there some immediate drivers or studies planned that can help with the prioritization?

• Are there some past studies where we can improve the overall approach?

• Are there some key subject matter experts within your organization to help guide this prioritization?

Prioritization Based on Pastand Planned Studies

Recommended Direction Forward

• Prioritize data sources for answering key translational research questions

• Identify the reference data model and tools to build a production level translational research center system

• Integrate the samples data with the clinical domains that are identified for other purposes (i.e. encounters, observations, procedures, concerns) and add new domains as required

• Establish rules for ananomyzation/de-identification

• Use the analysis data marts as the basis for research analysis

• Establish methods for direct access to data marts using a verity of tools

• Predefined analytics dashboards can follow in a later phase

• Management and re-use of methods and analytic results can follow at a later phase

• Perficient can assist in all stages and aspects of implementing a translational research center

Questions?

Mike Grossman, Director, Clinical Data WarehousingPerficient Life Sciences(617) 447‐2603Mike.Grossman@perficient.com

Contact Information

Martin Sizemore, Principal, Healthcare StrategistPerficient Healthcare(336) 847‐1802Martin.Sizemore@perficient.com