Transforming Health Care Through Big Data Science Innovation
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Transcript of Transforming Health Care Through Big Data Science Innovation
Transforming Health Care Through
Big Data Science Innovations
Georgia Tourassi, PhD
2015 ORAU Annual Meeting Oak Ridge, TN March 5, 2015
A Nation in Crisis
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Predictions about US Health in 2030…
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Projected Popula0on: 365+ million
Predictions about US Health in 2030…
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Projected Popula0on: 365+ million
More than 72 million elderly
Predictions about US Health in 2030…
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Projected Popula0on: 365+ million
More than 72 million elderly
Predictions about US Health in 2030…
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Projected Popula0on: 365+ million
300% increase in healthcare costs
$9 trillion/yr
More than 72 million elderly
1. What are the challenges of healthcare delivery?
2. What is the value of Big Data for healthcare transformation?
3. What are the challenges with Big Health Data?
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Outline
Healthcare Challenges
Be#er delivery
Be#er outcomes
Fewer dispari5es
Lower cost
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$ 2.3 Trillion / yr
The Practice of Medicine is a SCIENCE
“The fundamental problem with the quality of American medicine is that we have failed to view delivery of health care as a science. …That’s a mistake, a huge mistake.”
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Peter Provonost, MD Professor, Anesthesiology and Cri0cal Care Medicine, and Surgery Professor Health Policy & Management Johns Hopkins University
Transform healthcare through effective use of information
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Big Data
Effec5ve use of IT
Proac5ve care
Quality metrics Lower Costs
Government regula5ons
Where is the big health data coming from?
• Typical EHR size – ~1MB healthy young, no images – 40 MB middle-‐aged w/ health issues,
no images – 3-‐5 GB w/ health issues and images
• Es0mated size of current US digi0zed data
– 600 petabytes
• Es0mated different medical devices
– 1.5 million
• 2015: global health data, 20 Exabytes
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Clinical Data Medical History
Imaging Data Expression arrays
Personal Genomics
Telehealth / sensor data
Health Claims Social Media
Healthcare data is growing by 15 Petabytes a day in the US. Up to 80% of healthcare data is currently unstructured.
Where is the big health data coming from?
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Are we ready for healthcare data integration?
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But what about value?
“Big Data are data whose scale, diversity and complexity require new architecture, techniques, algorithms, and analy0cs to manage it and extract value and hidden knowledge from it” IMIA working group on “Data Mining and Big Data Analy0cs” From R. Bellazi, IMIA Yearbook of Medical Informa0cs 2014
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The Maslow Pyramid of Big Data Needs
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Stage 4: Wisdom
Stage 3: Knowledge Genera5on
Stage 2: Informa5on Retrieval
Stage 1: Data Collec5on & Storage
Decisions
Predic0ons Visualiza0on Repor0ng
Sta0s0cs Analy0cs Querying
ETL Data Fusion Data Integra0on
The Big Data Value Proposition
• Health care efficiency via beier access to pa0ent data (par0cularly for addressing health dispari0es)
• Earlier disease detec0on via real-‐0me analysis of mul0modality pa0ent data (e.g. mHealth, ICU)
• Personalized pa0ent care • Popula0on health management by con0nuously aggrega0ng
and analyzing public health data • Fraud, waste, and abuse detection
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McKinsey Global Ins0tute – May 2011: Big Data: The next fron0er for innova0on, compe00on and produc0vity
What are the computing challenges with Big Health Data?
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Real 0me access to data
Data integra0on
Extreme scalability
Radical flexibility
Storage infrastructure
Pa0ent privacy
Data governance
MUST: Holistic View of the Lifecycle of Data-Intensive Discovery
Can we scale up in all three aspects of data-driven discovery?
Opera5onal Flow
Descrip5ve Analy5cs
Diagnos5c Analy5cs
Predic5ve Analy5cs
Prescrip5ve Analy5cs
Concept adapted from Gartner
Analy5cal Flow
History • Formed in 2013 to integrate ORNL’s data-‐
driven, data-‐intensive biomedical research programs.
• HDSI members include biomedical researchers, system architects, data scien0sts, computer scien0sts, IT services, HPC opera0on experts
Vision • Accelerate data-‐driven biomedical discoveries
and healthcare delivery advancement Mission • Develop innova5ve, scalable, and robust
technologies for organizing, integra0ng, and analyzing complex data at scale
Priori5es: • Deliver methodological and applied scien0fic
innova0ons, informa0cs tools, and compu0ng infrastructure to enable effec0ve use of data for individual and public benefit.
• Advance a broad range of sponsor and health policy priori0es while serving as a neutral en5ty.
• Build health data science community capacity via a User Facility for collabora0ve engagement and targeted educa0on and training.
Health Data Sciences Institute at ORNLAdvancing the Utility of Data to Achieve Better Health Outcomes at Lower Cost
Innovate
Incubate
Accelerate
Research focus areas
Health Informa5cs Computa0on
for personalized solu0ons
Mul0-‐modality data analy0cs
Computer-‐aided decision support
Informa0ve visualiza0on
Health Economics and Policy
Computa0on for transla0onal impact
in healthcare management and policy
Detec0on and early predic0on of misuse, underuse, or overuse of health services
Healthcare system M&S: Analysis of pa0ent,
provider, and health system
interac0ons
Popula5on Health Dynamics Collec0on, analysis,
and modeling of data for disease and human health behavior surveillance
Social media analy0cs to study disease spread, health behaviors,
informa0on dissemina0on
Digital epidemiology
Analysis of heterogeneous unstructured big health data
Key contribu5on: A scalable, interac0ve analy0c and visualiza0on plasorm for exascale data
Data analysis
Exploratory data analysis
Confirmatory analysis
Evidence gathering
Visual analysis
CMS: Fraud detection
Descrip5ve Analy5cs
Diagnos5c Analy5cs
Predic5ve Analy5cs
Prescrip5ve Analy5cs
Descriptive Analytics: What happened ?
Diagnostic Analytics: Why did it happen ?
Predictive Analytics: What will happen ?
Prescriptive Analytics: How to stop it from happening ?
Individual Provider Payer
Health state
Demographics
Cogni5ve characteris5cs
Behavioral traits
News Media
Social Media
Individuals
Diffusion processes • Thresholds • Beliefs • Attudes • Preferences • Behaviors
Group communica5on processes, social networks
Decision to seek treatment
Disease Progression
Health outcomes
Care Progression
Diagnosis, Treatment selec5on
Decision to seek insurance
Claims
Pa5ent Compliance
Payment
Diagnos5c models
Care Progression models
Type
Specialty
Quality of Care
Cogni5ve characteris5cs
Provider Organiza0ons
(ACO)
Payment models
Regional varia5on
Professional Organiza0ons
Providers
Diffusion processes • Treatment paierns • Base rates
Deduc5bles
CoPays
Extent of coverage
Modeling and simulating healthcare delivery futures at scale
Digital epidemiology: Environmental Risk Factors
Smart Crawler
WWW Profiles
Match
NLP
Detect Iden0ty: Name, Job, Loca0on
Survivor’s LinkedIn Profile
Gender: First Name
Age: High school or College Year
Lifeline: Educa0on and Work History
Crawled 5,000,000 Pages
37,500 PaFent Stories 12,500 IdenFFes Found
1,200 LinkedIn Profiles Matched
Link EPA data Compare aggregate environmental exposure profiles between cancer and controls
Summary
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Gordon Gekko, Wall Street (1987) • The most valuable commodity I know of is informaFon (data).
Lao Tzu • To aQain knowledge, add things every day. To aQain wisdom, subtract things every day.
Ronald Coase (Nobel Prize in Economics, 1991) • Torture the data and nature will always confess.
Thank you
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Georgia Tourassi,
PhD
Director,
Health
Data Sciences Institute
Oak
Ridge
National Laborato [email protected]
Georgia Tourassi, PhD Director, Health Data Sciences Ins5tute
Oak Ridge Na5onal Laboratory [email protected]