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 MPI RECORDS LABS ADTS EMR DW EMR DW EMR DW EMR DW DATA GAIN 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 *Numbers as of 01/13/2016
Patient Overlap Between Health Systems 30% 29% 30% 25% 20% 15% 22% 19% 20% 15% 13% 10% 5% 0% HS 1 HS 2 HS 3 HS 4 HS 5 HS 6 HS 7
Diabetes Star Ratings Analysis Data Related to Diabetic Measures [VALUE] HIE Data EHR Data [VALUE]
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 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 http://www.himss.org/valuesuite
Questions Matthew Hoffman, MD CMIO, Utah Health Information Network mhoffman@uhin.org
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
Distributed Research Network Requirements (AHRQ 2010-2014) Service Oriented Architecture for privacy-preserving analytics in distributed research networks Focus on map-reduce, iterative algorithms based on paralleldistributed processing algorithms Standardized Analytic Data Warehouse at every site Role based access control -> Attribute Based Access Control De-centralized study administration no coordinating center peerto-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)
SCANNER Network Software (AHRQ 2010-2014) Need to select and implement standards for the entire analysis cycle: Data Data Processing Algorithms Data Analysis Algorithms Data Analysis Results
Part I: Multisite Data Standardization
Selecting a Data Warehousing Standard Health Economic Domains are not represented in other research and quality information models, but value was So Cal Health Leadership Priorities
Implementation datamartist.com
Aside on Data Quality & Availability text Dx R x CCD Lab EHR QRDA STANDARDIZED WAREHOUSE Procedures Finance LIS
Standardizing 11 Health Systems CUSTOM PROGRAMMING OMOP OMOP OMOP SHARED TRANSFORMATIO N PROGRAMS Quality Model Quality Model Quality Model OMOP Quality Model OMOP Quality Model OMOP Quality Model OMOP Quality Model OMOP Quality Model OMOP DISTRIBUTED ANALYTICS QRDA Quality Model
Aside on Data Quality & Availability HIE? text Dx R x CCD Lab EHR QRDA STANDARDIZED WAREHOUSE Procedures Finance LIS
Part II: Computation Standardization
Why do we need standards for computation if the data is already standardized?
Implemented standards + process modularity Reproducibility Robustness to innovation update parts without starting from scratch Flexibility to local implementation Transparency Comparability
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 SQL Predictive Model Markup Language
PORTABLE DATA PROCESSING STANDARDS OMOP OMOP OMOP OMOP DISTRIBUTED ANALYTICS OMOP OMOP OMOP OMOP OMOP 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 aka Data Processing Computable Phenotype Cohort Inclusion Criteria Measure Denominator OMOP Reference Data Model Data Set Extraction Program Extracted Data Set ( Flat File ) Analysis Program research Publish Estimated Predictive Model Publish Patient Record CCD care Record Processing Program Standardized Extracted Record Predictive Model Computation Patient Centered Prediction
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 Cohort definition rules HQMF>CQL CMS, ONC, HL7 endorsed Part of EHR certification process New standards in trial use 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 Process Workflow BPML? TBD Developed to represent results Adopted by most stats packages
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 dmeeker@usc.edu