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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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

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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

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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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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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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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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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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

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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.

DNA Sequencing is Just the Beginning of

(Really) Big Omics Data

• DNA

• Epigenetics

• RNA

• Proteomics

• Metabolomics

• Interactome

• Microbiome

• Connectome!

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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

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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

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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.

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DEMO – Cohort Explorer Use Case 1

45 Patients

MDS ONLY

15 Patients

MDS with

progression

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DEMO – Cohort Comparison

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• 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!

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