Interoperability and Analytics February 29, 2016

Transcription

1 Interoperability and Analytics February 29, 2016 Matthew Hoffman MD, CMIO Utah Health Information Network

2 Conflict of Interest Matthew Hoffman, MD Has no real or apparent conflicts of interest to report.

3 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

4 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

5 The STEPS Framework: Electronic Secure Data The Case for Interoperability -Supplementary Clinical/Hospital Data for Clinicians - Star Ratings Analysis - Shared Identities Numbers

6 Supplementary Data from Health Information Exchange MPI RECORDS LABS ADTS EMR DW EMR DW EMR DW EMR DW DATA GAIN SMFM 23, , , , , , , CUC 713,480 1,990, ,269 5,369,244 1,301,722 13,425,165 2,240,747 28,182, *Numbers as of 01/13/2016

7 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

8 Diabetes Star Ratings Analysis Data Related to Diabetic Measures [VALUE] HIE Data EHR Data [VALUE]

9 The STEPS Framework: Population Management Analytic Tools for the Front Line - The Importance of Standards - Chronic Disease Populations - Case Management Analyses - Geomapping Tools

10 UHIN Clinical Architecture

11 Using Standards to Combine Data

12 Cost to Value of Types of Analytics Models

13 Clinician Created Analyses

14 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)

15 Care Management Tools: Diabetic Population

16 Care Management Tools: Diabetic Population

17 Geomapping

18 The STEPS Framework: Savings For the CFO in All of Us - Encounter Notification Service and FHIR - Risk Adjustment Analyses

19 FHIR ENS Solution Diagram

20 FHIR ENS Solution Diagram

21 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

22 Risk Adjustment Analysis Architecture

23 ICD-X from Clinical Note

24 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

25 Questions Matthew Hoffman, MD CMIO, Utah Health Information Network

26 Interoperability and Analytics February 29, 2016 Daniella Meeker, PhD University of Southern California, Keck School of Medicine

27 Conflict of Interest Daniella Meeker has no real or apparent conflicts of interest to report.

28 Funding PCORI CDRN

29 Overview Multi-Institutional Learning Health Systems Data Standards and Standardization Process Computation Standards for Analysis Result Standards for Dissemination and Application

30 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

31 Standards for Data Exchange and Persistence are Necessary but Not Sufficient to Obtain Value from Multisite Data Infrastructure.

32 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

33 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

34 Distributed Research Network Requirements (AHRQ ) 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

35 Distributed Research Network Requirements (AHRQ ) 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)

36 SCANNER Network Software (AHRQ ) Need to select and implement standards for the entire analysis cycle: Data Data Processing Algorithms Data Analysis Algorithms Data Analysis Results

37 Part I: Multisite Data Standardization

38 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

39 Implementation datamartist.com

40 Aside on Data Quality & Availability text Dx R x CCD Lab EHR QRDA STANDARDIZED WAREHOUSE Procedures Finance LIS

41 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

42 Aside on Data Quality & Availability HIE? text Dx R x CCD Lab EHR QRDA STANDARDIZED WAREHOUSE Procedures Finance LIS

43 Part II: Computation Standardization

44 Why do we need standards for computation if the data is already standardized?

45 Implemented standards + process modularity Reproducibility Robustness to innovation update parts without starting from scratch Flexibility to local implementation Transparency Comparability

46 Direct and Quantifiable Comparisons dataset A dataset A 30 day readmission

47 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

48 PORTABLE DATA PROCESSING STANDARDS OMOP OMOP OMOP OMOP DISTRIBUTED ANALYTICS OMOP OMOP OMOP OMOP OMOP PORTABLE DATA ANALYSIS STANDARDS

49 Part 3: Result Standardization

50 Part 3: Result Standardization

51 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

52 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

53 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.

54 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

55 What Next? FHIR Clinical Quality Framework Data Access Framework

56 Questions? Daniella Meeker, PhD Assistant Professor, USC Keck School of Medicine Director, Clinical Research Informatics Southern California Clinical Translational Sciences Institute