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Utilizing ‘Big Data’ in
health care research
Eric D. Peterson, MD,MPH
Executive Director, Duke Clinical Research InstituteMarch 2016
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How Big is Big Data
• Just in 2010, 4 exabytes (4 x 10 18) of unique data was generated, which was more than in the preceding 5,000 years altogether
• "Every 2 days we now create as much information as we did from the dawn of civilization up to 2003” (Eric Schmidt, Google CEO)
• The amount of new data is now doubling every 13 months-and will soon double every 12 hours according to IBM
• For college students in technical degree, half of what they learn in their first year of study will be outdated by their third year
Brett King, Huff Post Tech; June 4, 2014 [http://www.huffingtonpost.com/brett-king/too-much-content-a-world-_b_809677.html]; Ray Kurzweil, The Law of AcceleratingReturns; March 7, 2001 [http://www.kurzweilai.net/the-law-of-accelerating-returns]; David Russell Schilling, Knowledge Doubling Every 12 Months, Soon to be Every 12 Hours; industry tap into news, April 19th, 2013 [http://www.industrytap.com/knowledge-doubling-every-12-months-soon-to-be-every-12-hours/3950]; http://www.youtube.com/watch?v=9XXaZRHhmxY
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Value of Aggregated Medical Data
Database Size Examples
Clinician’s Experience 10-100’s Osler, Harvey
Single Center Database 1,000-10,000’s Duke, Emory
Epidemiological Cohort 10,000’s Framingham, MESA
National Registries 1,000,000’s AHA, ACC, STS
EHR / patient-powered
Registries
10,000,000’s PCORnet, Health
eHeart registry
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Advances that are Changing Medicine
mHealth/Apps
Apple Health App InterfaceA. B. C.
D.Figure 1. Data Flow. A) Patient is seen by health provider and consents to study. B) Patient provided with JawboneUp24, Withingswireless scale, iHealth BP cuff, and iPod with Health App. C) The
patient selects each health monitor (s) that they would like to allow for integration into HealthKit where through the Health App, they can organize data under one dashboard. D) The provider places a request in EPIC for patient to share their data; the patient, through Health App selects any Health data they would like to share with
their physician and sends data to MyChart. This data will then be automatically uploaded to the Duke Epic EMR (Maestro Care). Provider is alerted that data is shared upon opening patient’s chart.
Apple Health App InterfaceA. B. C.
D.Figure 1. Data Flow. A) Patient is seen by health provider and consents to study. B) Patient provided with JawboneUp24, Withingswireless scale, iHealth BP cuff, and iPod with Health App. C) The
patient selects each health monitor (s) that they would like to allow for integration into HealthKit where through the Health App, they can organize data under one dashboard. D) The provider places a request in EPIC for patient to share their data; the patient, through Health App selects any Health data they would like to share with
their physician and sends data to MyChart. This data will then be automatically uploaded to the Duke Epic EMR (Maestro Care). Provider is alerted that data is shared upon opening patient’s chart.
Precision
Medicine
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The Big Data Age of Wonder!
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“Big data is like teenage sex: everyone talks
about it, nobody really knows how to do it,
everyone thinks everyone else is doing it,
so everyone claims they are doing it.”
– Daniel Ariely, Ph.D.
Duke University
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THE BIG CHALLENGES
Research remains too complex,
slow, inefficient, and expensive.
Data, data everywhere.
Yet limited insights and
knowledge
And even when we know what
to do…
Adoption is delayed and
incomplete. Care and
outcomes are highly variable.
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IT and Healthcare…Promise and Challenges
90% of world’s
data has been
created in the last
2 years.
DATA INTEGRATION ANALYTICS ACTION
But 80% of that data
is unstructured…
and stored in
separate systems.
Big data analytics
is coming to
medicine: Google,
IBM Watson…
Will need to
integrate these
data into practice.
FRAGMENTATIONSCALABILITY DECISION SUPPORT LEARNING SYSTEMS
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The Complexity of Big Data in Heath Care
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Cross Sectional Studies
Longitudinal Evaluations
CER – Safety Surveillance
Practical Clinical Trials
Translational Discovery
10
Clinical Registry
Clinical Registry
Clinical Registry
Clinical Registry
Clinical Registry
Claims Data
BiomarkerGenetics Samples
LongitudinalOutcomes
Detailed Pharm + Device Info
Device/DrugRCT
LongitudinalOutcomes
LongitudinalOutcomes
Value of Various Forms of Big Data
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Prediction Tools for Risk of Death after Stroke (circa 2012)
Based on 900 US hospitals and 1 Million patients
Smith EE. Circulation. 2010;122:1496-1504
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The New Era of Precision Science
January 2015
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Baseline Study
A longitudinal cohort study to extensively characterize participants at
baseline and serially using a battery of clinical, imaging, psychosocial,
behavioral, socioeconomic, geospatial, physiometric, and molecular tools.
A comprehensive study of human health and the transition to disease
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Baseline: Human Health and Transition to Disease
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Post Market Safety Surveillance
27 Institutions, 200 experts, 1 CC
> 60 million people
FDA, The Sentinel Initiative
July 2010
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SENTINEL BY THE NUMBERS
180M
4B
137
4
Individuals in the Sentinel Distributed Database
Prescription drug dispensings
Assessments of products, conditions, and
product-outcome pairs
FDA drug safety communications
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Big Data and the Pragmatic Clinical Trial
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Informatics Solutions: Developing EHR-based
Clinical Research Networks
Internal Data
WarehouseResearch
Datamart
Research
Datamart
Data
WarehouseEHRResearch
Datamart
Study specific
Clinical
Research
Network
Research
Site A
Internal Data
Warehouse
Centralized
disease
registry
Research
Site B
Research
Site C
Clinical Study
Database
EHRs can contribute some basic data to CT
database
EHR
EHR
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PCORI-Supporting Research
Selby JV et al. Sci Transl Med 2013;5:182fs13
Clinical Data Research
Networks
• $56 million
• 8 networks
• 1 million pts/network
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Pre-study• Utilize EHR to identify
local
subjects/population
• Feasibility dashboard
• Embed encounter
instructions and site
content into EHR
• Pre-consent & study
specific consent
• Model outcomes
• Assess sites’ use of
EHR to facilitate
research
• Usability of inclusion
and exclusion criteria
• Define & refine cohort
• Cohort’s interaction
profiles with health
system
• Feasibility analysis
• Recruitment plan
Study SetupRecruitment
Study Conduct
• Incorporate screening
criteria into EHR for
• Scheduling subjects
• Contacting subjects
• Recruiting subjects
• Alert provider of patient
eligibility
• EHR Health Portals
• Patient opt in/out for types
of studies
• Trials specific data
capture at care delivery
• Auto-populated CRFs
fields from EHR
• Extract data to facilitate
work of study
coordinator
• Rules, Alerts & Checks
• Data completeness
• Quality compliance
• Hospitalization/AEs
• Event rates
• Patient retention and
education
Opportunities to Leverage the EHR for RCTs
20
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IOM, November 27, 2012
Swedish Registry-Trial Hybrids
TASTE Trial: Thrombus-Aspiration in MI
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Randomization• Demographics
• Medical Hx
• Procedural data
Auto-populate
Unique pages for trial
U.S. Registry-Trial Hybrid: Safe PCI in Women
Analytic
Database
*Enrichment factors
• age > 65 years
• creatinine > 1.5
• diabetes
• known 3-vessel
coronary artery
disease
• current cerebro-
vascular disease
and/or peripheral
artery disease,
• known ejection
fraction <50%
• current smoker
Study
designPatients with known coronary artery disease
(MI, or CAD or Revasc) + ≥1 “enrichment factor”*
Identified through EHR/direct pt. consenting in clinics and hospitals through
CDRNs/PPRNs (PPRN pts. would need to connect through a CDRN to participate)
Pts. contacted electronically with trial information and eConsent;
treatment assignment will be provided directly to patient
ASA 81 mg QD ASA 325 mg QD
Electronic F/U Q 4 months;
supplemented with EHR/CDM/claims data
Duration: Enrollment over 24 months;
maximum f/u of 30 months
Primary Endpoint: Composite of all-cause mortality,
nonfatal MI, nonfatal stroke
Primary Safety Endpoint: Major bleeding complications23
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ADAPTABLE: Streamlining Trial Operations
123 9 30
Call Center
• Contact those patients who
stop following up
Baseline
Data
ADAPTABLE
Patient
615
CMS and private health plans FOLLOW-UP
• Longitudinal health outcomes
Patient Portal
• Info/eConsent
• Randomization
• Medication use
• Patient Reported Outcomes
PCORNet Coordinating Center FOLLOW-UP
• Uses Common Data Model
• Longitudinal health outcomes
National Death Index
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Learning to promote the rapid and complete uptake of clinical research
findings into routine practice, leading to improved quality of health care
and outcomes.
25
Using Clinical Data to Transform Care Practice
Bench Patients Populations
First Block:Translation from concept into first
human studies
Second Block:Translation from clinical trials into
practice
21
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Using Data, Provider Feedback to
Drive Quality Improvement
Mehta RH, et al AHJ 2007
Co
mp
osit
e A
dh
ere
nce R
ate
s
60%
70%
80%
90%
Q1 '02 Q1 '03 Q2 '04 Q3 '05 Q4 '06
AcuteDischarge
Provider-led (QI) effortscan improve CV care! NRMI, CRUSADE
AHA GWTG
NCDR-ACTION ACS
QI Tools Motivated local champions
Timely, valued feedback
Simple toolsStandardized orders, Chart reminders
Collaborative Teams
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Implementation Research
• Site Screening:
- Use National Clinical Registries
- Rapid screening of 100’s of centers
to find “outlier performance”
• Qualitative/Quantitative Research:
- Identify care processes linked
to better outcomes
• Empirical Evaluation and QI
- Formally test using cluster RCTs
- Disseminate what works through system!
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Impact of Target Stroke: Care & Outcomes
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Big Data and Population Health:The final frontier
• Top layer — concentrations of diabetes
patients.
• Next layer down — percentage single
female head of household.
• Below that in purple, another indicator of
economic status.
• The bottom layer maps the county
boundary and streets.
• Vertical green spines — longitude ad
lattitude coordinates of where diabetes
patients live and locations of key social
or commercial institutions, that can be
used to link all of these disparate data
sets together based on shared
geography.
Miranda, Ferranti, Strauss, Neelon, Califf. Health Affairs 2013;32:608-1615
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Conclusion: The Future of Big Data
• We are entering a new era where expanded data sources
offer the potential to create learning health systems
• However, progress will not occur without the development
of novel IT and informatics, more efficient research, and
more rapid and effective learning health systems
• We need to harness the emerging data deluge to create
new knowledge and translate this into better care…
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