The average academic research organization (ARO) and hospital has many systems that house patient-related information, such as patient records and genomic data. Combining data from a variety of sources in an ongoing manner can enable complex and meaningful querying, reporting and analysis for the purposes of improving patient safety and care, boosting operational efficiency, and supporting personalized medicine initiatives. In this webinar, Perficient’s Mike Grossman, a director of clinical data warehousing and analytics, and Martin Sizemore, a healthcare strategist, discussed: -How AROs and hospitals can benefit from a systematic approach to combining data from diverse systems and utilizing a suite of data extraction, reporting, and analytical tools, in order to support a wide variety of needs and requests -Examples of proposed solutions to real-life challenges AROs and hospitals often encounter

1. Combining Patient Records, Genomic Data and
Environmental Data to Enable Translational
Medicine
Martin Sizemore, Principal, Healthcare Strategist
Mike Grossman, Practice Director, Clinical Data Warehousing & Analytics, Life Sciences
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2. About Perficient
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.

3. Perficient Profile
• 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

4. Oracle Partnership
• 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

5. Healthcare Practice
Connected
Health
Experts in Consumer-Driven Healthcare Technology
CONSUMERS
HEALTH PLAN PROVIDER
Business Intelligence
and Analytics
Interoperability
and Integration
Information
Exchange
Regulatory
Compliance
Solutions & Services
Select Clients
Global Delivery Centers/Offshore Delivery
Domestic Delivery Center

6. Life Sciences Practice Practices / Solutions
Deep Clinical and Pharmacovigilance Applications Expertise
Implementation
Migration
Integration
Validation
Consulting
Upgrades
Managed Services
Application Development
Private Cloud Hosting
Application Support
Sub-licensing
Study Setup
Services
Clinical Trial
Management
Clinical Trial Planning and Budgeting
Oracle ClearTrial
CTMS
Oracle Siebel CTMS / ASCEND
Mobile CRA
Clinical Data Management
& Electronic Data Capture
CDMS
Oracle Clinical
Electronic Data Capture
Oracle Remote Data Capture
Oracle InForm
Medical Coding
Oracle Thesaurus Management System
Safety &
Pharmacovigilance
Adverse Event Reporting
Oracle Argus Safety Suite
Oracle AERS / EmpiricaTrace
Axway Synchrony Gateway
Signal Management
Oracle Empirica Signal/Topics
Medical Coding
Oracle Thesaurus Management System
Clinical Data
Warehousing & Analytics
Clinical Data Warehousing
Oracle Life Sciences Data Hub
Clinical Data Analytics
Oracle Clinical Development Analytics
JReview
Data Review and Cleansing
Oracle Data Management Workbench
Clients

7. Introductions

8. Welcome & Introductions
Martin Sizemore, Principal Healthcare Strategist
Martin 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 Analytics
Mike 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.

9. 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

10. 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

11. 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
tests
Carrier
testing
Simple
Mendelian
Pre‐natal
testing
Complex
disease
Cardiology
Immunology
Pathogenic
Pharmacoge
nomics
Adverse
reactions
Dosing
frequency
Dose size
Oncology
Tumor profiling
Residual
disease testing
Progression
analysis
Challenges
• Scalability
• System interoperability
• Speed of knowledge delivery
• Evolution of traditional care models
• Regulatory implications

12. Long Term Reference
Architecture Plan

13. Data Integration and Analytics Vision
Master Person Index
Patients Service
Providers
Source Systems
Epic
Data Staging
(HDI)
Cerner
GE
Centricity
Lawson
Research
Data
Other
Sources
(HDI)
(HDI)
(HDI)
Staging Tables
Integrated Data Storage Data Marts Reporting/
(HDI)
(HDI)
Integr(aHtDeId)
Storage Tables
Analytics
EHA

14. 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?

15. Source
Systems
Healthcare Data Model (EHA)
Lawson
(UCH)
Research &
Other
EPIC
(CHCO)
GE Centricity
(UPI)
An Integration Solution
Analytic Models
End‐User
Analytic
Interface
Analytic Data
Enc
Costing Clinic Billing
Schlg
Svc
Rnd
Adv
Events
Med
Mgmt
Lab
Orders
Atmosph
eric Data
EPIC
(UCH)
Master Data
Pt Demo
Enc Type Fac
Dx Location
Event
Date Meds Svc
Master
Svc Pvdr
Chg
Master
Pt
Familial
Rel
Fee Sch
Insurers
Omics Data
Spec‐imens
Studies Seq‐ Variants
uences
Files
Gene
Compo‐nents
Genes
Species
Proteins
Path‐ways
Chromo‐somes
Nomen‐clature
Personalized Medicine
Anonymizer
Research
Analytic Data Marts
Cohorts Diag‐nosis
Diag test DX
Ethnicity Medicati
ons
History Pro‐cedures
Spec‐imen
Study

16. Structured Patient Data
Re-Used for Research
• 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

17. Role of Omics Samples
• 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 omics
samples 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

18. 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?

19. • 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_softwa
re)
• 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

20. 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)

21. Analysis Lifecycle, Methods and Tools
• 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?
Preparation
Selection &
Exploration
Analytics &
Model
Building
Deployment
& Reuse

22. • 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

23. • 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 Past
and Planned Studies

24. 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

25. Questions?

26. Mike Grossman, Director, Clinical Data Warehousing
Perficient Life Sciences
(617) 447‐2603
Mike.Grossman@perficient.com
Contact Information
Martin Sizemore, Principal, Healthcare Strategist
Perficient Healthcare
(336) 847‐1802
Martin.Sizemore@perficient.com