Transcript Slide 1

Big Data Analytics in
Health Care
Making Sense and Getting
Value from the “Tsunami” of
Health Data
Presented by:
Alex J Mair, Director, Architecture
Mark Nenadovic, Group Director
Emerging Technology Group, Canada Health Infoway
eHealth 2013 Conference
Copyright © 2013 Canada Health Infoway
May 27, 2013
Faculty/Presenter Disclosure
•
Presenters: Alex J Mair, Mark Nenadovic
• Relationships with commercial interests:
− Nothing to disclose
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Agenda
Big Data Analytics in Health Care
• Setting the context
• Introduction to Big Data Analytics
• Enablers for Success
• Call to Action
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Data Never Sleeps
• Consider that the
average GP must stay
on top of:
− 10,000+ diseases &
syndromes
− 3,000+ Rx
− 1,100+ lab tests
• PubMed has over 22.6
million records
− 500K added each year
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Problem?
-
“Practice of medicine”
vs. the “science of
medicine”
•
Promise?
-
Technology...
-
−
−
mitigates human deficiencies;
amplifies strengths
increases access to complete,
current and synthesized medical
and social sciences research
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“Man versus Machine”
•
Computers excel at:
− organizing and recalling information
− remembering complex information
− integrating and balancing considerations
• Computers have larger memories, and make far
fewer mistakes than humans.
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Introduction of Big Data Analytics in Health
• The second white paper in the Emerging
•
•
Technology Profile series
Designed to address Big Data Analytics
(BDA) from a Canadian health care
perspective
Intended to provide:
− Insight on the potential applicability for this
new technology
− Context for business opportunities and
challenges
− Encouragement to advance activities in this
space
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BDA Defined
•
“Big data” characteristics
include: high volume, high
velocity and variety of types of
information that demand costeffective and innovative forms
of information processing.
•
“Analytics” is the process of
examining large amounts of big
data to deliver new insights
that can enable decisions in
real or near real time.
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Origin of BDA
• Key frameworks
Web based &
Social Media
Data
− Hadoop, MapReduce
− Data Streaming
Data
Streaming
• Key investments and enablers
− Cloud
Unstructured
− R (Data Mining)
& clinical data
− Natural Language Processing
Publication &
Reference data
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Public & other
data
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“Big” vs “Small” Data Analytics
Does not replace traditional analytics
capabilities, as it is complementary and
feeds results to traditional tools
Generate
Insights
Predict
& Model
Big Data
Mine
Experimental,
discovery, systemic
& predictive
Information management
processes are different than
traditional processes
Analytics
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Economics of BDA in Health Care
Note: Figure used with permission of McKinsey Global Institute
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Current state of BDA in Canadian healthcare
• Industry research indicates progress has been slow
− Activity is largely on the periphery of health care (i.e., research
studies); not yet in mainstream practice
− Benefits are still to be proven and documented
− Privacy and security are important considerations affecting the
pace of adoption
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− Canada tends to lag behind other countries
• Infoway believes
− Industry focus, for the last decade, has been on digitizing points
of service & the interoperable EHR
− Majority of Canadian health care organizations are small; few
have the financial scale to fund a BDA program
1. IDC White Paper: Survey of Big Data in Canada: Challenging Complacency for Competitive Advantage, December 2012
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BDA Opportunities in Health Care
Building on the EHR investments, Infoway foresees
opportunities for BDA to create new insights.
• Public and population health solutions
−
Google.org Flu counts specific search terms in queries to
estimate the intensity of flu levels in a geographic area,
often learning from the data itself.
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BDA Opportunities in Health Care
Building on the EHR investments, Infoway foresees
opportunities for BDA to create new insights.
• Public and population health solutions
−
Google.org Flu counts specific search terms in queries to
estimate the intensity of flu levels in a geographic area,
often learning from the data itself.
• Clinical operations based solutions
−
Toronto Sick Kids Artemis project uses big data from
monitors in real-time to provide insights which allow
researchers to predict when a baby is at risk of infection, so
that clinicians can intervene to prevent its occurrence.
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BDA Opportunities in Health Care
Building on the EHR investments, Infoway foresees
opportunities for BDA to create new insights.
• Clinical decision support based solutions
−
Memorial Sloan-Kettering Cancer Center, use of “Question
and Answer” probabilistic algorithms to analyze millions of
pages of unstructured text in patient records and medical
literature to locate and rank the most relevant answers to
diagnostic and treatment-related questions.
• Personalized (genomics) care solutions
−
Use of Genome 1000 and biomarkers investments in cancer
care research to predict individual disease risk, enable early
detection of disease, and improve diagnostic classification to
better inform individualized treatment.
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BDA Opportunities in Health Care
Building on the EHR investments, Infoway foresees
opportunities for BDA to create new insights.
• Policy and administrative business operations and
solutions
−
Seton Healthcare Family mines unstructured data using
natural language processing and search technologies to
predict which chronic health failure patients are most at risk
for readmission, by identifying known risk factors like
smoking, and the lack of social supports, so health
professionals can focus their efforts on keeping high risk
patients at home
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Predictions re: BDA Gaining Traction
• The healthcare transition will start incrementally
and develop slowly in sophistication
− Early adopters will focus on use case development,
experimentation and collaboration
• Key areas of application include:
−
−
−
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Cancer treatment & genomics
Clinical Decision Support
Streaming data & remote patient monitoring
Predictive analytics
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BDA Readiness
Infoway predicts:
•
Larger health delivery organizations particularly those
with a:
─ Research base
─ Advanced level of maturity with their EHRS, DW/BI
─ Established information governance capabilities
•
HDOs that have demonstrated some practical success
with knowledge management initiatives and are ready
to take the leap to BDA
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BDA Requires Enablers to Ensure Success
It is more than a technology project.
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Business model – sustainability, business case & ROI, in-house vs. hosted service
Governance – appropriate use of patient information, custodianship frameworks
Leadership – vision, strategy roadmap, culture, investment
Skilled resources – mathematical sciences, applied statistics, machine learning,
operations research, natural language processing
• Information management practices –
processes, data collection,
knowledge generation, context and visualization
• Privacy and security policy –
PHI protection, legal, policy, legislation
interpretation
• Ethics –
use, research, legal
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Call to Action
Communications
Opportunities to
Collaborate
• Identify
• Don’t go it
• Conduct due
• Look to
champions
diligence to
understand BDA
• Share research,
lessons learned
alone
leverage
others’ work
• Collaborate
on use case,
business
cases
Leadership
• Address policy,
legal, ethics,
business model,
sharing, legacy
issues
Cautious
Execution
• Limited scope
pilots, solving
specific clinical or
business
problems
• Enable a supportive • Iterative, stepculture, processes,
wise investments
staffing for BDA
that limit capital
requirements
• Create opportunities
to bring together
• Evaluate the
several emerging
feasibility and
technologies
utility of BDA
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Emerging technology resource documents:
https://www.infoway-inforoute.ca/index.php/resources/presentations/emerging-technology
Discussion & questions?
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