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![Page 1: Predictive Analytics Drives Population Health · PDF filePredictive Analytics Drives Population Health Management March 1, 2016 ... STANDARDS: CDA, PMML, HL7 V2, DICES SECURITY P CH](https://reader031.vdocument.in/reader031/viewer/2022030414/5a9f95fa7f8b9a84178d0940/html5/thumbnails/1.jpg)
Predictive Analytics Drives Population Health Management
March 1, 2016
David Seo, MD
University of Miami Health System AVP IT Clinical Applications and CMIO
Chitra Raghu
Lockheed Martin Program Manager and Innovation Officer
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Conflict of Interest
David Seo, MD
Chitra Raghu
Have no real or apparent conflicts of interest to report.
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Agenda
• About UHealth
• Drivers Toward Population Health Management
• Data Infrastructure
• Predictive Analytics Solution
• Predictive Analytics Results at the Point of Care
• Predictive Analytics Via User Portal
• Future Directions
• Q/A
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Learning Objectives
• Discuss how an integrated health system can leverage technology to focus on chronic disease interventions
• Identify strategies for creating and implementing an adaptable technology platform that enables cost-effective population health management
• Demonstrate the value in delivering risk-stratified information to the point of care as well as to research professionals
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Anticipated Benefits of Predictive Analytics Platform
http://www.himss.org/ValueSuite
S = A flexible platform that supports the health system’s goal for population health management. T = Real-time health assessments of their patient population allows clinicians to launch targeted interventions to their most high-risk patients. E = Secure consolidation of health information from over 60 different healthcare technology systems, provides the necessary data to identify patient’s at risk of developing a chronic disease. P = Patients are most satisfied when they feel they have more control over their health. Risk assessment tools provide patients with information and allow them to participate in lifestyle changes to improve their health. S = Potential for significant savings in treatment costs by preventing or delaying the onset of disease and its associated health complications.
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About UHealth • University of Miami Hospital
• Sylvester Comprehensive Cancer Center
• Bascom Palmer Eye Institute
• Affiliated hospitals include: – Jackson Memorial Hospital
– Holtz Children’s Hospital
– Miami VA Medical Center
• We have 30+ outpatient facilities throughout Miami-Dade,
Broward, Palm Beach, and Collier counties, with more than
1,500 physicians and scientists.
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UHealth IT Landscape
• Multiple EHRs installed at the various hospitals and clinics
• 450 different technology systems across the organization; exchanging data with 60 of them
• Working to consolidate on a single EHR platform
• Using Content Document Architecture (CDA) as the data transfer vehicle
– Fully identified CDAs for exchange for patient care
– De-identified CDAs for exchange for research
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Background – The PCMH ACO Population Health Journey
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The Big Shift
FFS P4P Bundled
Payments Shared Savings
Partial/Full Capitation
Risk/Opportunity
Payment Models: Moving Risk from Payers to Providers
Care Delivery Models: Trading Volume for Value
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Lining Up the Enterprise Pieces
IT System Infrastructure
Care Coordination
Patient Engagement
Data/Analytics
Organizational Structure
Contracting
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Data Infrastructure to Support the Journey
Cogito
Data
Warehouse
Epic
Clarity
Meditech Data
Extracts
Pilot Genomic
Data
Epic
Chronicles
Source
Systems
Enterprise
Biorepository
Business Objects
(Echo,
Press Gainey,
Payer Claims,
Etc.)
- Clinical Results
- Business Intelligence
- Standard/Ad-hoc Reporting
- Risk/Opportunity
Identification
- Regulatory Reporting
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Population Health Journey
Part 1 - Doing the Dirty Work
• Decipher then adhere to guidelines
• Gather and analyze the data then argue
• Contracting poker (Texas Hold ‘Em)
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Population Health Journey
Part 2 – Shaking Things Up
• Assess the results then argue
• Manage for performance
• Distribute incentives
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Population Health Journey
Part 3 – Go for the Gusto
• Population health management
• Reduce care variability
• Change provider behavior
• Maximize patient engagement
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Remember the 5/50 and 20/80 Rules
• 2/3 of the sickest folks were healthy the prior year.
• 5% of patients account for 50% of healthcare expenditures
• The sickest 20% of patients account for 80% of costs
# of
People
$$ they
spend
Mark Stanton, Agency for Healthcare Research and Quality (AHRQ). (2006)
Challenge: Build data tools to predict and
identify patients who will make this
unfortunate health transition and attempt
to alter that path.
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Solution Infrastructure
• Agnostic architecture for flexibility and growth
• Built on Free and Open Source Software (FOSS) platform
• Easy processing and creation of disease models
• Provide results at the point of care
• Sustainable in an academic medical center IT shop (i.e. limited money)
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Predictive Analytics Process
Cogito Data Warehouse
Hadoop
Risk ScoreRisk Score
Risk ScoreRisk Score
Risk ScoreRisk Score
Epic EMR
Clarity
Meditech EMR
Genomic Data
Epic EMR
Chronicles
Source System
Data Epic EMR
SMS
ALERT ENGINE
Business Objects
Data
Aggregation
Data Analysis
Timely Useable
Data
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Technical Alignment of Tools
Data
Aggregation
Pattern
Recognition
Visualization
& Actuators
Integration
STANDARDS: CDA, PMML, HL7 V2, DICES
SECURITY
HA
DO
OP
ELA
ST
IC S
EA
RC
H
MO
NG
O
OP
EN
SC
OR
ING
DD
S
MA
HO
UT
MLib
RA
TT
LE
KIB
AN
A
SH
INY
MIR
TH
CA
ME
L
MU
LE
X X X X X X X
Tools
Evaluated
Tools Used
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Architecture Overview
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Model Promotion Process
Identify event
to be predicted. Survey ex ist ing
models
Compare
models to
ex ist ing data
Identify model
to promote
Write algorithm
for model
Encode model
in PMML
Test model
using patient
data
Deploy and
integrate model
Evaluate
efficacy
Develop
pract ical
criteria
Develop
select ion
criteria
Identify data
available
Add to
available data
Start
Finish
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Predictive Analytics Platform Prototype
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Data Quality Analysis
Sample One Million Patients:
* Unknown ETL issue at the time
Data Element % Found in Patient Sample
Age ~100%
Gender ~100%
Body Mass Index (BMI) ~35%
History of Hypertension ~41%
Diabetes Family History* ~0.26%
Physical Activity ~0%
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Point of Care Predictive Analytics
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EHR Order Entry – Ordering the Score
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EHR Results – Risk Score Returned
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EHR Result –Risk Scores Stored & Trended
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REDCap and EHR – Data Collection
Link to REDCap data
instruments and surveys
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REDCap Survey Results in the EHR
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Predictive Analytics via User Portal
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Predictive Analytics via User Portal
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Future Directions – Putting It All Together
Cogito Data Warehouse
Hadoop
Risk ScoreRisk Score
Risk ScoreRisk Score
Risk ScoreRisk Score
Epic EMR
Clarity
Meditech EMR
Genomic Data
Epic EMR
Chronicles
Source System
Data Epic EMR
SMS
ALERT ENGINE
Business Objects
Care Coordination
Algorithm Developmentand Testing
Clinical Care
Patient Care Intervention
Evaluate Response
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Future Directions – Fine Tuning
Cogito Data Warehouse
Hadoop
Risk ScoreRisk Score
Risk ScoreRisk Score
Risk ScoreRisk Score
Epic EMR
Clarity
Meditech EMR
Genomic Data
Epic EMR
Chronicles
Source System
Data Epic EMR
SMS
ALERT ENGINE
Business Objects
Care Coordination
Algorithm Developmentand Testing
Clinical Care
Patient Care Intervention
Evaluate Response
Tune Model
Performance for
Our Populat ion
Design
Interventions by
Risk Score
Validate EHR
Clinical
Phenotypes
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Future Directions
• Individualized Alerts and Treatment
- Heart Rate
- Warfarin
• EHR-based Observational Analysis
- Complex Clinical Decision Support
- Population Health is local
• Patient Engagement
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Anticipated Benefits of Predictive Analytics Platform
http://www.himss.org/ValueSuite
S = A flexible platform that supports the health system’s goal for population health management. T = Real-time health assessments of their patient population allows clinicians to launch targeted interventions to their most high-risk patients. E = Secure consolidation of health information from over 60 different healthcare technology systems, provides the necessary data to identify patient’s at risk of developing a chronic disease. P = Patients are most satisfied when they feel they have more control over their health. Risk assessment tools provide patients with information and allow them to participate in lifestyle changes to improve their health. S = Potential for significant savings in treatment costs by preventing or delaying the onset of disease and its associated health complications.
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Questions
David Seo, MD
University of Miami Health System Associate VP IT Clinical Applications and Chief Medical Informatics Officer
Chitra Raghu
Lockheed Martin Sr. Program Manager and Innovation Officer
(443) 348-2800