Presentation "The Impact of All Data on Healthcare" Keith Perry Associate VP & Deputy CIO UT MD Anderson Cancer Center With continuing advancement in both technology and medicine, the drive is on to make all data meaningful to drive medical discovery and create actionable outcomes. With tools and capabilities to capture more data than ever before, the challenge becomes linking existing structured and unstructured clinical data with genomic data to increase the industry’s analytical footprint. Learning Objectives: ∙ Discuss the need to make all data meaningful in order to speed discovery of new knowledge ∙ Provide examples of an analytical direction that supports evolution in medicine ∙ Expose the challenges facing the industry with respect to ~omits

1. The Impact of All Data on Healthcare
Keith Perry, MBA
Associate Vice President
Deputy Chief Information Officer
UT MD Anderson Cancer Center
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2. Discussion Topics
• View of Big Data
• Quick Facts
– Cancer
– MD Anderson
• Evolution of Medicine
– Clinical Decisions
– Genomic
• Big Data Shaping Strategies:
– APOLLO
– Foundation Warehouse
– Shaping Analytics
– Pushing toward Cognitive Learning
• Parting Thoughts
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3. Humanity and Big Data
In 2010 we humans
generated more bits
of information than
there are stars in the
knowable universe.
In 2009 humanity
created more data
than we have in all of
human history.

4. What is the Big Data Problem?
• Diverse perspectives on Big Data (Quoted in LinkedIn
Big Data and Analytics Group):
“analysis of combined differed data”
“mass accumulation of (un)/structured data”
“get insight from infinite data”
“making sense of unlimited non-sense data”…
• Integration, analysis and visualization of large volumes
of unstructured, semi-structured & structured data
generated by/from objects, events, processes, etc.
Stephen Gold, VP, World-wide Marketing at IBM Watson
– “Big data is the fuel – it is like oil. If you have it in the
ground, it doesn’t have much value. As soon as you
extract the oil from the ground and start refine it, it
amplifies not only its usefulness but its value.”

5. Healthcare Big Data McKinsey Global Institute
• Five distinct Big Data
pools exists in the US
healthcare domain
1)Pharmaceutical: R&D, Clinical
Trials
2)Academic: Translational
Research
3)Provider: Clinical Operations
4)Payer: Activity (claims) & cost
5)Patient behavior & sentiment

6. Healthcare Trend -> Future
• Big Data Trends in Healthcare
– Unstructured data and natural language
processes being used as the underlying
technology in healthcare
– Predictive analytics allowing to aggregate
the data to see patterns realistically making a
difference in the decisions
– Cloud-based “Big Data” platforms to
aggregate, analyze, manage and research
data from various sources for better patient
care at a lower price
– Combining social and clinical data streams
to create the world’s real-time behavioral
health record

7. Big Data and the Creative
“Reconstruction” of Medicine
Modality
Megabytes
HL7 CDA Doc
0.025
Health Patient Chart
5
Chest Xray
16
MRI
45
PET Scan
100
Mammography
160
CT Scan (64 slice)
3,000
Genome (seq data only)
3,000
Cellular Pathology Study
25,000
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8. Global Cancer crisis demands bold action
• The disease is projected to become the nation’s leading killer
over the next decade as the population ages and increases
• More than 500,000 people in the U.S. die every year
• Lifetime cancer risk: 1 in 2 men, 1 in 3 women
• World’s costliest disease
• Nearly $1 trillion annually
in losses to death
and disability
• 95% failure rate
in cancer drug
development
• We must reverse
this situation
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9. Our Mission
To eliminate cancer in Texas, the nation and the world through
outstanding programs that integrate patient care, research and
prevention, and through education for undergraduate and graduate
students, trainees, professionals, employees and
the public.
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10. MD Anderson Quick Facts
MD Anderson has been ranked the nation’s No. 1 cancer hospital for ten
of the past 12 years in U.S. News & World Report’s “Best Hospital”
survey.
• The largest critical expertise of scientists and clinicians in every key
area, rare or common
• Exemplary science – most NCI grants; $648 million in research
annually
• Leading clinical research program:
nearly 8,500 patients enrolled in
1,000 clinical trials exploring
novel treatments
• More than 115,000 patients treated
each year
• 19,000 employees and 1,300
volunteers with a single mission:
eliminate cancer
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12. What is a moon shot?
• A rigorous, multidisciplinary, highly focused
and milestone-driven effort to overcome a specific cancer
• Each project combines the latest genomic knowledge
and technologies with a comprehensive, systematic
approach to identify and advance the most promising
cancer-fighting strategies
• Define the future of cancer
research and drive discoveries to
our patients more efficiently
and faster
• Foremost, the moon shots
are about helping patients
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13. The goals
Steered by genomics and executed with engineering precision,
the moon shots aim to dramatically reduce incidence and
mortality of the cancers.
• Short term (5-10 years): Convert current knowledge into
prevention and early-detection strategies, and more
effective combinations of existing drugs.
• Longer term: Discover a moon shot cancer’s root causes;
identify all genetic targets that drive and sustain it; translate
resulting knowledge into risk-control strategies and new
medicines..
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14. Fascinating Times
“Clinical practice will never be the same. The
endpoint will not be does this drug combination
extend the life of a patient, but does the
algorithm for choosing the best triple
combination extend lives.”
Mary Edgerton, M.D., Ph.D., Associate Professor, Pathology,
The University of Texas MD Anderson Cancer Center
“Let the patient teach us what is important”
Gordon Mills, M.D., Ph.D., Chair, Systems Biology, Director, Kleberg Center for Molecular
Markers, M. D. Anderson Cancer Center.
Scientific progress depends increasingly on the management, sharing, and analysis
of data from diverse sources. In cancer centers, informatics expertise and
resources are critical shared resource functions.
The Office of Cancer Centers of the National Cancer Institute
Policies and Guidelines Relating to the Cancer Center Support Grant

15. Clinical Domain is complicated
Facts per Decision
1000
Proteomics and
Other effector molecules
100
Functional Genetics:
Gene expression
profiles
10
Structural Genetics:
e.g. SNPs,
haplotypes
5
Decisions by
Clinical Phenotype
1990
2000
2010
2020
With appreciation to William W. Stead, M.D., 2007 AMIA Panel Presentation, “Why We Need Internal Development”, November 11, 2007

16. Big Data Supports More Precision
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17. Precision Disease Classification
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Source: Genzyme Genetics, as presented in Allison, Malorye,
“Is Personalized Medicine Finally Arriving?”, Nature Biotechnology,
Vo.l 26, No. 5, May 2008, p 517.

18. DNA Sequencing is Just the Beginning of
(Really) Big Omics Data
DNA →RNA→Protein→Metabolism →You
•
•
Epigenetics
•
RNA
•
Proteomics
•
Metabolomics
•
Interactome
•
Microbiome
•
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DNA
Connectome!

19. Cost of Sequencing

21. APOLLO enables adaptive learning
Patient Consent, Biospecimen
Collection, QC, Banking,
Biomolecule Processing
Clinical
Information and
Data
Treatment
Decisions
&
Response
Assessment
Omics &
Research Data
Big Data Warehouse
Big Data Analytics
TCGA/ICGC
Pubmed
Patent database
Social media
Big Data Warehouse as a single
source of longitudinal patient
data (clinical and research)
Watson Solutions
Insight discovery
Clinical decision support
Business Analytics
Proprietary and Confidential
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22. Big Data Architecture
Oncology Expert
Advisor
IBM WATSON
NeXT Bio
Translational
Research Center
Interactive
Genomics Viewer
Dashboards &
Analytics
BIG DATA ANALYTICS
Healthcare Data
Warehouse Foundation
Computing Power –
Data Warehouse Appliance
Big Data Storage –
Database File System
Natural Language
Processing Pipeline
BIG DATA WAREHOUSE COMPONENTS
BIG DATA PLATFORM
Treatment
Decisions
Response
Assessment
Clinical Data
Genomic
Data
Research
Data
Patient
Database
Patient Consent, Biospecimen Collection, QC, Banking, Biomolecule Processing
Primary Patient Data
TCGA/ICGC
PubMed
Social Media
Security and
Governance Controls

23. Foundation Warehouse Overview
•
Create a comprehensive centralized clinical data repository
supporting clinical/institutional analytics, decision making,
and business intelligence needs
• Central repository for historical clinical and genomic data
• Break-down data silos
Dashboards
Pharmacy
Radiology
KPI’s
Labs
Analytic
Reports
Periop
EMR
Source Systems
Healthcare
Data Model
Analytic
Structures
Analytics
& Reporting
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24. Big Data Warehouse Components
Health Data Warehouse Foundation Database
Natural
Language
Pipeline
Data Warehouse Appliance
Big Data Storage Database File System

25. Big Data Volumes to Date
1,014,548 Patients (1944)
23,146,101 Medications (2011)
68,919,788 Lab Results (2011)
1,131,182 Billing Diagnoses
453,837 NLP Documents
5,660 Molecular Diagnostic Lab Samples
4,000 Genomic Level 3 Files
And Growing Daily!
Big Data
Warehouse

26. Natural Language Processing (NLP)
Natural Language Processing extracts valuable clinical information,
embedded in transcribed notes to:
•
•
•
Enhance electronic patient records Decrease error rates
• Facilitate integration
Decrease manual effort
Clinical Notes
Text Parsing
Context
Analysis
Disease
Confirmation
Disease
Categorization
New NLP Pipeline Established
Comorbidity Loaded
to Big Data

27. Typical Research Process
Researcher
has
Hypothesis
Who has
the
Data?
Researcher
Submits
Question
Analyst
Gathers
Data
Analyst
Submits
Results to
Researcher
Researcher
Reviews
and Asks
Follow-up
Question
Protocol
Submission
/ IRB Approval
Researcher
Pursues
Hypothesis in
Greater Depth
Find Data
and
Acquire
Access
Profile
and
Integrate
Data
Standardize &
Prepare Data
Hypothesis is
Confirmed or
Disproved
Cohort selection process can take weeks for one
iteration
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28. Enhanced Research Process
Researcher
has
Hypothesis
Researcher
Asks
Question
Researcher
Reviews and
Asks Follow-up
Question
TRC (Translational Research
Center)
Protocol
Submission
/ IRB Approval
Researcher
Pursues
Hypothesis in
Greater Depth
FIRE (CDM/ODB)
Find Data
and
Acquire
Access
Profile
and
Integrate
Data
Standardize &
Prepare Data
Hypothesis is
Confirmed or
Disproved
Cohort selection process takes minutes
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29. Oracle Cohort Explorer - Selection
Clinical Research Need:
Identify patients with similar comorbidity and genomic copy number variation
characteristics to my current patient, so that past treatment options can be
reviewed and applied effectively.
Cancer Patients
Cohort Explorer allows clinicians
and researchers to quickly identify
a similar cohort of patients across
various criteria to meet the clinical
research need.
Leukemia Patients
With a Comorbidity of
Diabetes
With Genomic
Copy Number
Variations

30. Cohort Explorer – Genomic Use Case 1
• Identify two patient cohorts:
Cohort 1) Patients with MDS that progressed
Cohort 2) Patients with MDS that did not progress
• Compare the copy number variation of
these two cohorts to see if there are any
differences.
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31. DEMO – Cohort Explorer Use Case 1
15 Patients
MDS with
progression
45 Patients
MDS ONLY
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32. DEMO – Cohort Comparison
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34. Oncology Expert Advisor
• Cognitive Clinical Decision Support
• Deliver today’s best to all
• Patient-centric
• Standardization & adoption
EvidenceBased
Learning
Natural
Language
Processing
• Today’s best is not good enough
• Patient-oriented discovery research
• Learning from every patient; n=all
• Convert knowledge into improved care
standard
Hypothesis
Generation

35. Dynamic summary of patient profile
Patient Evaluation
Rx & Management Plan
Care Pathway Advisory
Patient-Driven Research

36. In the era of Big Data, amid the country’s
medical, economic and policy challenge
and as modern technology heads toward
the "1,000 genome" one main biomedical
challenge will be finding ways to actually
use it in the clinical setting, by providing
unique risk profiles or a basis for
customized therapy.
NIH makes big deal of big data
Healthcare IT News, Jan14, 2013

37. Summary Thoughts.
• It is cliché but this really is an awesome time to be in
technology!
• We need to share this excitement and encourage new
thought leaders to innovate in this uncharted space
• We are on a journey (albeit one step off the starting line)
where it is possible to leverage more data to:
– speed knowledge discovery;
– disseminate, collaborate and share best practice; and
– impact the quality of healthcare today!
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