Interoperability and Analytics February 29, 2016
Matthew Hoffman MD, CMIO
Utah Health Information Network
Conflict of Interest
Matthew Hoffman, MD
Has no real or apparent conflicts of interest to report.
Agenda
• Electronic Secure Data
– Supplementary Clinical Data
– Patient Overlap Between Health Systems
– Diabetes Star Ratings Pilot
• Population Management
– Chronic Disease Populations
– Case Management Analyses
– Geomapping Tools
• Savings
– Encounter Notification Service and FHIR
– Risk Adjustment Analyses
Learning Objectives
• Describe how standards define use cases and parameters for health data
analytics
• Identify the ways in which data analytics can help to support the business
value of health information exchange
• Evaluate how new dimensions in standard definitions will shape data
analysis applications and use cases
The STEPS ™ Framework: Electronic Secure Data The Case for Interoperability -Supplementary Clinical/Hospital Data for
Clinicians
- Star Ratings Analysis
- Shared Identities Numbers
http://www.himss.org/ValueSuite
Supplementary Data from Health Information Exchange
EMR DW EMR DW EMR DW EMR DW
SMFM 23,535 100,315 105,849 236,460 - 501,652 109,608 705,304 6.46
CUC 713,480 1,990,063 585,269 5,369,244 1,301,722 13,425,165 2,240,747 28,182,616 10.11
DATA GAINMPI RECORDS LABS ADTS
*Numbers as of 01/13/2016
Patient Overlap Between Health Systems
0%
5%
10%
15%
20%
25%
30%
HS 1 HS 2 HS 3 HS 4 HS 5 HS 6 HS 7
22%
19%
29%
20%
15%
30%
13%
Diabetes Star Ratings Analysis
[VALUE]
[VALUE]
Data Related to Diabetic Measures
HIE Data
EHR Data
The STEPS ™ Framework: Population Management
Analytic Tools for the Front Line
- The Importance of Standards
- Chronic Disease Populations
- Case Management Analyses
- Geomapping Tools
http://www.himss.org/ValueSuite
UHIN Clinical Architecture
Using Standards to Combine Data
http://www.himss.org/ValueSuite
Cost to Value of Types of Analytics Models
http://www.himss.org/ValueSuite
Clinician Created Analyses
http://www.himss.org/ValueSuite
Custom Filtering Filter Settings
A1C
- Gender: (female, male)
- Age: (0 <= Age <= 124)
- A1C_Val: (1.30 <= A1C_Val <=
21.99)
MicroAlbumin
- Mic_Val: (0.00 <= Mic_Val <=
11.35)
BP BMI Cholesterol
- BP: (82 <= BP <= 205)
- BMI: (0.21 <= BMI <= 73.02)
- Cholesterol: (0 <= Cholesterol <=
251)
http://www.himss.org/ValueSuite
Care Management Tools: Diabetic Population
http://www.himss.org/ValueSuite
Care Management Tools: Diabetic Population
http://www.himss.org/ValueSuite
Geomapping
http://www.himss.org/ValueSuite
The STEPS ™ Framework: Savings For the CFO in All of Us - Encounter Notification Service and FHIR
- Risk Adjustment Analyses
http://www.himss.org/ValueSuite
FHIR ENS Solution Diagram
http://www.himss.org/ValueSuite
FHIR ENS Solution Diagram
http://www.himss.org/ValueSuite
Results
• Anecdotal
– Post Surgery Re-admit reduction
– Decreased Asthma Hospital Admissions
• Documented
– 3 month pilot (Coaction):
• 54% reduction in hospitalization days
• 33% reduction in ED visits
• $178,547 estimated savings
– Medicare Patient Population (NY)
• 2.9% reduction in likelihood of 30 day readmission
• $1.24 million savings in admissions
Risk Adjustment Analysis Architecture
http://www.himss.org/ValueSuite
ICD-X from Clinical Note
http://www.himss.org/ValueSuite
STEP Summary
http://www.himss.org/ValueSuite
Exchange • 21% Avg Overlap Between Systems
• 17% Increase in Star Ratings Data
Population Health • Care Managers
• Physicians
• Public Health
Savings • Alerts: Decrease in Hospital Days,
Readmissions and ED Visits
• Risk Assessment:
• Increased Risk Reimbursement
Questions
• Matthew Hoffman, MD
• CMIO, Utah Health Information
Network
Interoperability and Analytics February 29, 2016
Daniella Meeker, PhD
University of Southern California, Keck School of Medicine
Conflict of Interest
Daniella Meeker has no real or apparent conflicts of interest to report.
Funding
PCORI CDRN-1306-04819
Overview
• Multi-Institutional Learning Health Systems
• Data Standards and Standardization Process
• Computation Standards for Analysis
• Result Standards for Dissemination and Application
Learning Objectives
• Describe how standards define use cases and parameters for health data
analytics
• Identify the ways in which data analytics can help to support the business
value of health information exchange
• Evaluate how new dimensions in standard definitions will shape data
analysis applications and use cases
Standards for Data Exchange and Persistence are Necessary but Not
Sufficient to Obtain Value from Multisite Data Infrastructure.
Products of a Learning Health System
• Analysis models for causal inference and program evaluation
– Program X is 15% more effective at preventing readmission than Program Y
– Drug A has a greater risk for adverse events than Drug B
• Quality measurement reports
– Practice N is achieving 80% of quality indicators
– Practice L is achieving 60% of quality indicators
• Predictive models for patient-centered medicine
– For patients like John, Drug C is safer and more effective than Drug D.
– For patients with Debbie’s goals and comorbidity profile, Program Y is better than Program X
pSCANNER Network – 21M patients
• 5 University of California Medical Centers
• Cedar’s Sinai Hospital
• Pacific NW Rural Health Practice-Based
Research Network
• Los Angeles Department of Health
Services
• 5 multi-site FQHCs
• Children’s Hospital of Los Angeles
• Keck Medicine of USC
• Service Oriented Architecture for privacy-preserving analytics in distributed research networks
– Focus on map-reduce, iterative algorithms based on parallel-distributed processing algorithms
• Standardized Analytic Data Warehouse at every site
• Role based access control -> Attribute Based Access Control
• De-centralized study administration – no “coordinating center” peer-to-peer analytics.
• GUI for menu-driven queries for multiple activities
– Proposing collaboration partners, data sets, and protocols
– Specifying regression analytics
– Data set extractions
– Cohort discovery
Distributed Research Network Requirements (AHRQ 2010-2014)
• Methods repository for contributing and sharing analytic algorithms for classical statistics and machine learning
• Result repository for contributing and sharing analytic results –(e.g. causal models, predictive models, quality reports)
Distributed Research Network Requirements (AHRQ 2010-2014)
Need to select and implement standards for the entire analysis cycle:
• Data
• Data Processing Algorithms
• Data Analysis Algorithms
• Data Analysis Results
SCANNER Network Software (AHRQ 2010-2014)
Part I: Multisite Data Standardization
Health Economic Domains are
not represented in other research
and quality information models,
but value was So Cal Health
Leadership Priorities
Selecting a Data Warehousing Standard
Implementation
datamartist.com
Aside on Data Quality & Availability
Dx
R
x
Lab
Procedures
text
CCD
LIS
Finance
EHR QRDA
STANDARDIZED WAREHOUSE
OMOP
OMOP
OMOP
OMOP
OMOP
OMOP
OMOP
OMOP
OMOP
SHARED
TRANSFORMATIO
N PROGRAMS
Quality
Model
Quality
Model
Quality
Model
Quality
Model
Quality
Model
Quality
Model
Quality
Model
Quality
Model
Quality
Model
CUSTOM
PROGRAMMING
QRDA
DISTRIBUTED
ANALYTICS
Standardizing 11 Health Systems
Aside on Data Quality & Availability
Dx
R
x
Lab
Procedures
text
CCD
LIS
Finance
EHR QRDA
STANDARDIZED WAREHOUSE
HIE?
Part II: Computation Standardization
Why do we need standards for computation if the data is already standardized?
• Reproducibility
• Robustness to innovation – update parts without starting from scratch
• Flexibility to local implementation
• Transparency
• Comparability
Implemented standards + process modularity
Direct and Quantifiable Comparisons
dataset A dataset
A
30 day readmission
Selecting a Standard for Computation Specification
• Sufficiently expressive to represent data processing concepts for transactional, time-series data (e.g. interval logic)
• Sufficiently expressive to represent data processing concepts that are specific to healthcare (e.g. time of administration, age of onset)
• Sufficiently expressive to represent basic statistics and data analysis algorithms
• Supported/Adopted
Quality Data
Model Data
Processing
Semantics
Predictive
Model Markup
Language
SQL
OMOP
OMOP
OMOP
OMOP
OMOP
OMOP
OMOP
OMOP
OMOP
DISTRIBUTED
ANALYTICS
PORTABLE DATA PROCESSING STANDARDS
PORTABLE DATA ANALYSIS STANDARDS
Part 3: Result Standardization
Part 3: Result Standardization
Products of a Learning Health System
• Analysis models for causal inference and program evaluation
– Program X is 15% more effective at preventing readmission than Program Y
– Drug A has a greater risk for adverse events than Drug B
• Quality measurement reports
– Practice N is achieving 80% of quality indicators
– Practice L is achieving 60% of quality indicators
• Predictive models for patient-centered medicine
– For patients like John, Drug C is safer and more effective than Drug D.
– For patients with Debbie’s goals and comorbidity profile, Program Y is better than Program X
Disseminating Products of a Learning Health System
OMOP
Reference
Data Model
Extracted
Data Set
(“Flat File”)
Data Set
Extraction
Program
Analysis
Program
Estimated
Predictive
Model
aka “Data Processing”
“Computable Phenotype”
“Cohort”
“Inclusion Criteria”
“Measure Denominator”
research
Patient
Record
Predictive
Model
Computation
Standardized
Extracted
Record
Patient
Centered
Prediction
Publish
care
Publish
Record
Processing
Program
CCD
Selecting Standards for Disseminating LHS Products
• Quality Indicators can be shared & Reported with QRDA
• Predictive Models can be shared & Reported with PMML (but not part of Health IT standards)
• Causal inference & program evaluation results are (still) disseminated on paper.
Summary: Standards for Exchanging Analysis Process and Products
Process we need to
represent
Standard Rationale
Data processing rules HQMF>CQL CMS, ONC, HL7 endorsed
Part of EHR certification process
New standards in trial use
Cohort definition rules HQMF>CQL 100’s of established data sets
1000s of cohort criteria
Data set description QRDA
PMML
QRDA – Quality Measurement EHR Certification & CMS
PMML – Data analysis
Data Analysis Methods PMML UCSD Data Mining Group
Extensible to support model specifications
Data Analysis Results
(Estimated Models,
Produce Predictions)
PMML Developed to represent results
Adopted by most stats packages
Process Workflow BPML? TBD
What Next?
• FHIR
• Clinical Quality Framework
• Data Access Framework
Questions?
• Daniella Meeker, PhD
Assistant Professor, USC Keck School of Medicine
Director, Clinical Research Informatics
Southern California Clinical Translational Sciences
Institute
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