health it summit austin 2013 - presentation "the impact of all data on healthcare" keith...
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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 ~omitsTRANSCRIPT
The Impact of All Data on Healthcare
Keith Perry, MBA
Associate Vice President
Deputy Chief Information Officer
UT MD Anderson Cancer Center
1
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 2
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.
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.”
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
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
Big Data and the Creative
“Reconstruction” of Medicine
7
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
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
8
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.
9
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
10
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
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..
14
Fascinating Times
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
“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
Gordon Mills, M.D., Ph.D., Chair, Systems Biology, Director, Kleberg Center for Molecular
Markers, M. D. Anderson Cancer Center.
“Let the patient teach us what is important”
Clinical Domain is complicated
Decisions by
Clinical Phenotype
Structural Genetics:
e.g. SNPs,
haplotypes
Facts
per
Decis
ion
1000
10
100
5
2000 2010 1990 2020
Proteomics and
Other effector molecules
With appreciation to William W. Stead, M.D., 2007 AMIA Panel Presentation, “Why We Need Internal Development”, November 11, 2007
Functional Genetics:
Gene expression
profiles
Big Data Supports More Precision
18
Precision Disease Classification
19
Source: Genzyme Genetics, as presented in Allison, Malorye,
“Is Personalized Medicine Finally Arriving?”, Nature Biotechnology,
Vo.l 26, No. 5, May 2008, p 517.
DNA Sequencing is Just the Beginning of
(Really) Big Omics Data
• DNA
• Epigenetics
• RNA
• Proteomics
• Metabolomics
• Interactome
• Microbiome
• Connectome!
20
DNA →RNA→Protein→Metabolism →You
Cost of Sequencing
APOLLO enables adaptive learning
Clinical
Information and
Data
Treatment Decisions
& Response
Assessment
Patient Consent, Biospecimen
Collection, QC, Banking,
Biomolecule Processing
Omics &
Research Data
TCGA/ICGC Pubmed Patent database Social media
Big Data Warehouse
Big Data Analytics
Watson Solutions
Big Data Warehouse as a single source of longitudinal patient
data (clinical and research)
Proprietary and Confidential
Insight discovery
Clinical decision support
Business Analytics
23
Security and
Governance Controls
Primary Patient Data
BIG DATA PLATFORM
Big Data Architecture
Clinical Data Treatment
Decisions
Response
Assessment
Genomic
Data
Research
Data TCGA/ICGC
PubMed
Social Media
Patient
Database
Patient Consent, Biospecimen Collection, QC, Banking, Biomolecule Processing
BIG DATA WAREHOUSE COMPONENTS
Healthcare Data
Warehouse Foundation
Computing Power –
Data Warehouse Appliance
Big Data Storage –
Database File System
Natural Language
Processing Pipeline
BIG DATA ANALYTICS
Oncology Expert
Advisor
IBM WATSON
NeXT Bio Translational
Research Center
Interactive
Genomics Viewer
Dashboards &
Analytics
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
Healthcare
Data Model EMR
Periop
Radiology
Labs
Pharmacy
Source Systems
Analytic
Structures
Dashboards
KPI’s
Analytic
Reports
Analytics
& Reporting 25
Big Data Warehouse Components
Health Data Warehouse Foundation Database
Data Warehouse Appliance
Natural
Language
Pipeline
Big Data Storage Database File System
Big Data Volumes to Date
23,146,101 Medications (2011)
453,837 NLP Documents
68,919,788 Lab Results (2011)
1,014,548 Patients (1944)
1,131,182 Billing Diagnoses
5,660 Molecular Diagnostic Lab Samples
4,000 Genomic Level 3 Files
Big Data
Warehouse
And Growing Daily!
Natural Language Processing (NLP) Natural Language Processing extracts valuable clinical information,
embedded in transcribed notes to:
New NLP Pipeline Established
Clinical Notes
Text Parsing
Comorbidity Loaded
to Big Data
Disease
Categorization
Disease
Confirmation
Context
Analysis
• Enhance electronic patient records
• Decrease manual effort
• Decrease error rates
• Facilitate integration
Typical Research Process
Researcher
has
Hypothesis
Researcher
Submits
Question
Analyst
Gathers
Data
Standardize & Prepare Data
Profile and
Integrate Data
Find Data and
Acquire Access
Analyst
Submits
Results to
Researcher
Researcher
Reviews
and Asks
Follow-up
Question
Cohort selection process can take weeks for one
iteration
Who has
the
Data?
Researcher
Pursues
Hypothesis in
Greater Depth
Hypothesis is
Confirmed or
Disproved
Protocol
Submission
/ IRB Approval
31
Enhanced Research Process
Researcher
has
Hypothesis
Researcher
Asks
Question
Researcher
Reviews and
Asks Follow-up
Question
Researcher
Pursues
Hypothesis in
Greater Depth
Hypothesis is
Confirmed or
Disproved
FIRE (CDM/ODB)
Standardize & Prepare Data
Profile and
Integrate Data
Find Data and
Acquire Access
TRC (Translational Research
Center)
Protocol
Submission
/ IRB Approval
Cohort selection process takes minutes 32
Cancer Patients
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.
Leukemia Patients
With a Comorbidity of
Diabetes
With Genomic
Copy Number
Variations
Cohort Explorer allows clinicians
and researchers to quickly identify
a similar cohort of patients across
various criteria to meet the clinical
research need.
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.
34
DEMO – Cohort Explorer Use Case 1
45 Patients
MDS ONLY
15 Patients
MDS with
progression
35
DEMO – Cohort Comparison
36
• Cognitive Clinical Decision Support
• Deliver today’s best to all
• Patient-centric
• Standardization & adoption
• Today’s best is not good enough
• Patient-oriented discovery research
• Learning from every patient; n=all
• Convert knowledge into improved care
standard
Natural Language Processing
Hypothesis Generation
Evidence-Based
Learning
Oncology Expert Advisor
Dynamic summary of patient profile
Care Pathway Advisory Rx & Management Plan Patient Evaluation Patient-Driven Research
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
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!
44