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Big Data – A Starring Role in
Healthcare Information
Architectures
October 26, 2016
2:00 – 3:00 pm ET
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Housekeeping Issues
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Today’s slides will be available for download on our
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About eHealth Initiative
Since 2001, eHealth Initiative has been advocating the value of technology and innovation in healthcare through research and education.
eHI convenes its multi-stakeholder members, from across the healthcare ecosystem, to discuss how to transform healthcare through information and technology.
eHI members released The 2020 Roadmap. The primary objective is enable coordinated efforts by the public and private sector to transform healthcare by the year 2020.
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Multi-Stakeholder Leaders in Every Sector of Healthcare
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The 2020 RoadmapKey Focus Areas in 2016
Interoperability
Privacy & Security
Business & Clinical Motivators
Health IT Policy
Data & Analytics
Innovation
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This webinar was made possible through the
generosity and support of PHEMI!
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AgendaWelcome and Housekeeping
Introductions:
• David Foster, VP Data Insights and Operations,
Healthwise
Speakers:
• Trent Rosenbloom, MD, Vice Chair for Faculty Affairs,
Associate Professor of Biomedical Informatics with
secondary appointments in Medicine, Pediatrics and
the School of Nursing at Vanderbilt
• Kirk Kirksey, VP & CIO, UT Southwestern Medical
Center
• Dr. Paul Terry, CEO and CTO, PHEMI Systems
Big Data - A Starring Role in Healthcare Information Architectures
Using a Large Clinical Data Network to Support Research
S. Trent Rosenbloom, MD MPHAssociate Professor and Vice Chair of Biomedical Informatics
Associate Professor of Internal Medicine, Pediatrics & Nursing
eHealth Initiative Foundation Panel – October 26, 2016
@TrentRosenbloom
PCORNet
• The National Patient-Centered Clinical Research Network (PCORNet), is an innovative initiative of the Patient-Centered Outcomes Research Institute (PCORI).
• PCORNet is designed to make it faster, easier, and less costly to conduct clinical research than is now possible by harnessing the power of large amounts of health data and patient partnerships.
http://pcornet.org
PCORNet
• PCORnet aims to create a “network of networks” that harnesses the power of large amounts of health information and unique partnerships among patients, clinicians, health systems and others.
• In the process, it seeks to transform the culture of research from one directed by researchers to one driven by the needs of patients and other healthcare stakeholders.
Includes: Coordinating Center, CDRNs, PPRNs
http://pcornet.org
PCORNet CDRN Coverage
PCORNet CDRNs
• “Clinical Data Research Networks are networks that originate in healthcare systems and securely collect health information during the routine course of patient care”
- Phase I: 01/2014 – 09/2015, 11 sites
- Phase II: 10/2015 – 09/2018, 13 sites
http://pcornet.org/clinical-data-research-networks/
PCORNet’s CDRN Goals
• Each CDRN should:– engage at least 1 million patients across 2 or more
health systems
– build infrastructure to share data, build novel
informatics tools, and engage key stakeholders
– Perform comparative effectiveness research and
pragmatic clinical trials
Vanderbilt Medical Center: hospitals, >100 clinics engaging 2 million
patients. Meharry/Metro General Hospital: 100,000 patients
VHAN: 7 health systems, 34+ hospitals, 350+ clinics
engaging >3 million patients
Greenway: 1600 clinics engaging 14 million patients
Carolinas Collaborative with > 6 million patients
The Mid-South CDRN
Patient and stakeholder engagement
Large scale enrollment
Standardized data
De-identified
data sharing & regulatory processes
Capability to implement
clinical trials
Three specific cohorts
populated
Efficient biospecimen
banking
Phase I – Key Milestones
Three Initial MS-CDRN Cohorts
• Healthy Weight Cohort– Initial recruitment goal 10,000 individuals for a study
of weight-related health issues
• High Prevalence Cohort– Initial recruitment goal 10,000 individuals with
coronary heart disease
• Rare Disease Cohort– Initial recruitment goal 400 families affected by sickle
cell disease
PCORNet Common Data Model
http://www.pcornet.org/pcornet-common-data-model/
PCORNet Common Data Model
http://www.pcornet.org/pcornet-common-data-model/
PopMedNet
VURDW
VHANRDW
GreenwayRDW
CDM
Mid-South CDRN PCORNet
1. Queries
and Analytic
Software
Packages
from PCORI
2. CDRN
returns
Counts and
Aggregate
resulting data
CDM CDM
UNCRDW
DukeRDW
HSSC
RDW
CDM
Meharry
RDW
CDM CDM CDM
Data Aggregation CDRN-wide
Vanderbilt’s Data Flow Model
Linkage to TN State Health Data (hospitalizations, birth/death data)
Linkage to Tenncare Data
Linkage to CMS Data ( Virtual Research Data Center, RESDAC, CMMI data)
Linkage to Vanderbilt Health Plan (Aetna) health data (claims and PBM data)
Surescripts
Linkage to VU Home Health Data
Linkage to Nursing Home data
Linkages for “Complete” Data
Site Sites in CDM Production CDM Refresh Rate Patients Encounters Dynamic Rate
VanderbiltVanderbilt University Health System
1/09 - 11/15 Quarterly update 1,458,542 13,631,309 Monthly**
VHANWilliamson Medical Center, Maury Regional Medical Center, West TN Health
12/13 - 4/16 Quarterly update 346,241 968,254 Monthly**
Greenway Health
952 sites 1/10 - 12/15 Bi-Annual Update 10,000,000 110,823,801 NA
UNC at Chapel Hill
UNC Health Care System 1/04 – 1/16 Quarterly update 4,078,704 15,270,648 NA
Duke UniversityDuke University 1/05 – 1/16 Quarterly update 2,062,439 34,172,582 NA
HSSC
Greenville Health System (GHS), MUSC Health (MUSC), Palmetto Health (PH), and Spartanburg Regional Healthcare System (SRHS)
GHS: 1/07 – 12/15PH, SRHS: 1/11 – 3/16
MUSC: 1/07 – 9/15Quarterly update 2,879,835 39,068,523 NA
Meharry Medical Center
Meharry Medical College and Nashville General Hospital
1/04 – 6/16 Quarterly update 189,874 315,518 NA
All data nodes have gone through Cycle 1 data characterization
Data Characterization
Prep-to-Research –
Data characterization of data node
has been completed, but did not
complete an interview with the
Coordinating Center prior to the
end of Cycle 1. Sites will complete
this process in Cycle 2.
•HSSC
•Meharry
Research Ready –
Data characterization of data node
has been completed, site has had
interview with Coordinating Center
to review data, and any updates
have been made and reported
back to coordinating center.
•Duke
•UNC
•VHAN
•VUMC
•Greenway
Step 1
DataMart refreshes planned
Step 2
DataMart team responds to
query package and locks SAS
DataMartStep 3
DRN OC approval
(verification of pre-specified requirements)
Step 4
DRN OC analyzes DC
responses and solicits more
information as needed
Step 5
DC Discussion Forums
• All Mid-South sites will run cycle 2 data characterizations which will include
– Labs results
– Prescribing and Dispensing
– Death
• Cycle 2 data characterization begins November 7th
Data Characterization
Data Source Description Dates
Vanderbilt/VHAN Health Plan (Atena)
Universal Medical/ Dental Plan: Service, ICD, CPT, Admission, DRG Codes and Provider information
January 2014-September 2015
Pharmacy Plan: dispensing data, dose, strength duration, refill info, pharmacy location
TennCare Demographics, cause of death, death, diagnosis, dispensing, encounter, enrollment, and procedure
2000-2014
TDOH Statewide hospital/emergency discharge claims, birth/death certificates, demographics, diagnosis, dispensing, encounter, enrollment, and procedure
2011-2013
North Carolina Blue Cross/Blue Shield
All inpatient and outpatient claims, including pharmacy2008-2015
North Carolina Medicaid DUA in process, not available yet. Will be all inpatient and outpatient claims 2008-2015(requested)
South Carolina Claims Discharge database including inpatient, outpatient surgery, emergency, ambulatory free-standing clinic, home health encounters. Demographic, encounter, diagnosis, procedure, and charges.
2000-2015
ADAPTABLE Pilot Inpatient, Outpatient, and Part D Demographics, encounters, diagnosis, procedures, vital, death, labs, prescribing and dispensing
2011-2013
CMS Data Inpatient and outpatient claims In progress
CMMI* All RIFs, Part D, Master Beneficiary summary files, and crosswalks from BENE ID to HIC and SSN
In progress
Data Linkage Projects
Novel Informatics Tools
• Tools for quickly running queries and analyzing electronic health data
• Tools for identifying and contacting patients
– Email, Text, Phone (> 300K emails at VUMC)
– My Research at Vanderbilt (20K)
• New electronic consent process
• Expanded survey tools for collection of patient reported outcomes (via web/mobile platforms, automated phone, embedded video/audio, etc.)
• Integration of PROMIS measures into REDCAP
• Electronic payment processes for study participation
• Potential integration of patient survey data into the EHR for clinical use
• Expansion of clinical decision support tools
ADAPTABLE Trial
• The Aspirin Dosing: A Patient-centric Trial Assessing Benefits and Long-Term Effectiveness Trial will compare the effectiveness of two aspirin doses widely used to prevent heart attacks and strokes in individuals living with heart disease
• Pragmatic Comparative Effectiveness Trial
• PCORI-funded
• First PCORNet demonstration project
http://theaspirinstudy.org
Questions?
trent.rosenbloom@vanderbilt.edu
An Abridged Tour of Select Data Use atUT Southwestern Medical Center. Dallas
Kirk KirkseyVP Information Resources and CIO
Adjunct Professor, University of Texas at DallasChancellor’s Fellow for Health Information Technology, UT System
What I Will Cover TodayA Bit about UT Southwestern
Why We Use Clinical Data the Way We Do
How We Use the Information
How We Want to Use the Information
Our Biggest Barriers
UT Southwestern – An Overview
Not a Hospital. An Academic Medical Center
Three strategic missions:Patient careResearchEducation
Two hospitals. 40+ clinics.14K employees. $2.8B operating budget.
Affiliations with Parkland, Dallas Children’s, and Dallas VA.
Clements University Hospital Opened 12/2014
SW Health Resources – partnership with Texas Health Resources.
UTSW Campus
Clements University Hospital
580 employees, $85M operat ing budget. $18m - $23M capital projects per year.
Major customer groups: Cl inical professionals, Cl inical Scientists, Basic Science Faculty and Professionals , Students, Physician Partners, University Staff.
Oversee al l UTSW cl inical systems (ambulatory, inpatient, revenue cycle. Approximately 600 t ier 0,1 systems with 200+ interfaces).
Most Wired. Level 7 HIMSS designation. Winner 2016 Healthcare Informatics Innovator Award. Coordinate/col laborate with cl in ical aff i l iates. All voice, date, wired, wireless and cel lular. Research admins support (cl in ical tr ials mgt system, eIRB). Central ized server/OS support. Data center operat ions. CoLo/DR operat ions Help Desk. Desktop Support. About 350 basic products and services. University/Health System ERP. Enterprise data warehouse.
IR SCOPEUTSW – Information Resources Overview
1951 - Late 1970s – MonolithicOne System Must Do it All1970 - 2000(?)– Best of Breed
Maximum Business Unit Functionality“Make Systems Talk”. The Rise of HL7
2000 - Today(?) – Best of ClusterData Integration Trumps (sometimes) BU functionality
The Pendulum of Healthcare Information Architecture
2001(?) - The Rise of the Commercial Electronic Medical RecordIt’s Here to Stay.
The Commercial Electronic Medical Recordas a Data Aggregator
Collects traditional ancillary data (LIS,RIS, Rx, etc) in real time.
Collects non-structured data (clinical documentation, reports)
Allows patient engagement(more data for analytics)
Controls some data quality through workflow
Patient centric organization
• Can’t collect lots of non-clinical data (e.g. building systems).
• Minimal capture of meta-data
• Research and teaching challenged
• Patient centric organization
• Not engineered (yet) for Big Data (diagnostic images, genetic sequencing)
The good The Not-So-Good
How We Use the Data
Member – Enterprise Data Warehouse and i2b2 Clinical Data Repository
Multi-system (e.g. ERP, LMS) integration
MS based. Dimensional
Interoperability with other CTSA sites
Teaching
Certification program using non-production EMR environment
Teaching patient integration with Learning Mgt System (in development)
Research
IRB Protocols
Clinical Trials – EMR integration with Clinical Trials Mgt System (Velos)
Operations research
Big Data UTSWFirst and Foremost – What is it?
Recognize the Big Data Holy Trinity
Move Big DataStore Big DataCompute Big Data
Dedicated 10G network connects from selected laboratories toUTSW Data Center and Texas Advanced Computing Center (TACC) at UT Austin
Support of HPC clusters (trained IR personnel)
Dedicated local Co-Lo and DMZ area open to specialized departmental computing personnel
Partner with Bio-informatics. Leading formation of Clinical Informatics Program
CTSA Grant Holder with i2b2 research data warehouse (not the EDW or CDR)
Active remote disaster recovery/Co-Lo site
Some Examples
Duplicative Diagnostic Imaging in the Dallas Ft Worth Region
CHF Patient Readmission and Migratory Patterns
Two Factor Prediction Model for CHF Patient Readmissions
Patient Portal and CHF Patient Outcomes
DashboardsKPIsMgtReports
PredictInterveneEvaluateAdjust
PredictPublishGet CitedUpdate Resume
RetroactiveAnalytics
AcademicPredictive
OperationsPredictive
ProcessAnalytics
PredictAuto-Intervene (e.g. self driving car)EvaluateAdjust
Our (the Royal ‘Our’) Analytics Challenge
Interventions:A Thought Experiment
Register and train CHF patients at point of care
At home get-connected support
Train and support family and caregivers
More education for Home Health professionals
Multiple regression – which type of portal interaction contributes most to better outcomes
Purchase equipment???????
Major Barriers (IMHO)Implement Data Governance
data definitionsdata transformation rulescross-hierarchy categories (tags)political willIT operations implications
Understand Analytics Architecture Components First (not products)loads/interfaceswarehousedata modelsrequired outputdata scrubbingknow your data
Training the Analytics Professional – The New Frontierhypothesis based investigationsound statistical testingwritingprocess reengineering
Dr. John Snow
One Last Thought:What Does Big Data Buy Us?
New kinds of data. High res diagnostic images. Genetic sequence. Building systems output.
Much more of the same data.
New presentation/simulation techniques.
Compute intensive modeling.
Reduced sampling error (?).
But…
Google Uses Big Datato Predict the Incidence ofH1N1 Flu.
Wal-Mart Uses Big Datato Predict Pre-HurricaneBuying Patterns (2004)
Thank You.
#eHIWebinarPHEMI Systems Copyright 2016
Big Data’s Starring Role in Next-Generation Healthcare Information Architectures
Dr. Paul Terry
PHEMI Systems
#eHIWebinarPHEMI Systems Copyright 2016
The Data Dilemma
Healthcare Data is Locked in Silos
Share and Protect?
Data Available
analysis, insights,
and new services
Analytics
Applications
#eHIWebinarPHEMI Systems Copyright 2016
How do I share data
for secondary use
without compromising privacy,
security, and governance?
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Digital Library
#eHIWebinarPHEMI Systems Copyright 2016
Managing Digital Assets
Index of a Book Library Card
#eHIWebinarPHEMI CONFIDENTIAL
Turning Your Data into Strategic Assets
Collect
Index and catalog for findability
Protect and Share
Privacy, Security & Governance
Transform
Analytics-ready
METADATA
#eHIWebinarPHEMI Systems Copyright 2016
Collect Everything
Gene SequenceX-Ray
EMR Form
Code
Fragment
Database
Web Pages
Documents
Virtual MachineSocial Media
Internet
of Things
Video
Customer Service Call Audio
Emails
#eHIWebinar
Index and Catalog with MetadataKnow what complex data you have and be able to find it
Rate = 72
Derived
Rhythm =
Sinus
Derived
LastName =
Smith
Derived
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TransformMake complex or multi-structured data analytics-ready
Semi-structured StructuredEchocardiogram Reader
Simplified Distributed Computing Analytics-Ready
Aorta 39 mm
LA 31 mm
LVd 48 mm
LVs 25 mm
FS 48 %
LVEF 65 %
VS 9 mm
PW 9 mm
Ht 170 cm
Wt 77 Kg
#eHIWebinar
Protect and ShareControl access to protect private and sensitive information
Automatically de-identify, mask, and
process data, then publish to 3rd party
applications or Analytics Tools
Enforce consent and data sharing
agreements
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Automatically Support Different Data Views for Different Users
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Use Cases
#eHIWebinarPHEMI Systems Copyright 2016
• Early warning system
• Prevent, delay, mitigate
• Quarterly molecular screening
• Grow to 25,000 patients
• 15+ varied data sources
• Integrate “omics” with
• clinical data
• Longitudinal study
• Rich research
“How can we integrate genomics information into regular care
so we can advise clients on better behavior for overall
wellness?” Molecular You Co.
#eHIWebinarPHEMI Systems Copyright 2016
Analytics-Ready Data
How do we offer better service while reducing our call center
costs? Global Insurance Company Uses PHEMI Big Data
• Use Data Science to make data
actionable
• Sentiment analysis
• Natural Language Processing
(NLP)
• Replace ODS
• Publish Datasets
• Active Archive
Claims Voice
Recording
Customer
Survey
Social Media Clickstream Call Center
Records
Self Serve Data
Catalog
Custom
ApplicationsAnalytics
Tools
“How do we offer better customer service while reducing call
center costs?” Global Healthcare Insurer
#eHIWebinarPHEMI Systems Copyright 2016
“How do we find targets in all other cancer
patients?” Province of British Columbia
Picture: cbc.ca
#eHIWebinarPHEMI Systems Copyright 2016
“What are the best intervention strategies that drive better
outcomes and have less strain on the system?”
Diabetes Research
#eHIWebinarPHEMI Systems Copyright 2016
“Is there a connection between autism and the microbiome in
the gut?” Autism Research
#eHIWebinarPHEMI Systems Copyright 2016
PHEMI Central Big Data Warehouse
How do I dynamically manufacture datasets for different users?
Filter, Transform, and
Release
Users
Secure
Research
Environment
Structure
Data Marts
Data Cubes
Tables
Applications
Collect
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Big data works with your existing information architectures
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Big data is here now. It is here to stay.
pterry@phemi.com
#eHIWebinar
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Questions and Answers!
Please use the chat feature to ask questions
Today’s slides will be available for download on our
homepage at www.ehidc.org
If you have any questions, please contact Claudia
Ellison, Claudia.Ellison@ehidc.org
65
This webinar was made possible through the
generosity and support of PHEMI!
Slides are available at www.ehidc.org/resources
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