Big Data and Advance Health Analytics Boston, MA

Transcription

1 Big Data and Advance Health Analytics Boston, MA Andrew Rosenberg MD Chief Information Officer University of Michigan Health System & Medical School May 26 th,

2 Rapid Expansion of Digital Data Data production and storage are increasing rapidly across industries. The health care market is expected to see a 660% increase by [4] Increase in Data Production ( ) Projected Increase Worldwide 1993 = 3 Exabytes 2007 = 230 Exabytes 2015 = 7988 Exabytes 2020 ~ Exabytes Projected Increase in Health Care 2013 = 153 Exabytes 2020 ~ 2,300 Exabytes [4] Source: IDC

3 UMHS By the Numbers

4 For Big Data Projects To Succeed At UMHS & We need an Enterprise Analytics plan A roadmap to advance our abilities to support clinical, research and education programs and priorities currently in place or planned. 4

5 Immunization Allergy Diagnosis Vital Problem List Lab Lab Order Imaging Pathology Procedure Meds Clinical Terminologies RxNorm SNOMED Others Inpatient Admission ED Bed Assignment Outpatient Visit/ Service Scheduled Appointment Encounter Patient Monitoring System Cardio Vascular (ECHO) Implantable Devices (ICD) ECG EEG Demographics Account Transactions Payment Charge Adjustment Encounter/Medical Services Master Data Surgery Smoking Flowsheet Clinic Notes Radiation Oncology Charge Claim Line Claim Revenue Cycle Clinical Operations Care Delivery Research Education Telemedicine Consult Payment Claim Claim Rx DRG Organizational Data Locations Buildings Plan Facilities/Locations Consent Survey Patient History Patient Payer Standards Facility Departments Tissue Survival Status Study Biomaterial Bio-Assay Sample Faculty Collaborative Staging Recurrence Metastasis Biomarkers Sample Data Animal Subject Cancer mrna Party Provider Adverse Event Event Findings Bio- DataSet SNP NGS Representative Subject areas Research Registries (Cancer) Services i.e. Care Delivery, Party Research Data Bold Research, Education Staff Academic Rule Learning Unit Instance Program Area of Study Course Experiential Learning Project Based Learning Education Learning Plan Academic Calendar Calendar Learning Object Learning Result Learning Objectives Learning Unit Student Staff Faculty Enterprise Analytics Planning Framework & Roadmap Progression Where we are going How we will get there How we will manage Enabling Pillars 9 Use Cases 3 Domains 54 User-Informed Scenarios Functional Requirements Federated Analytics Architecture Total Cost of Ownership Federated Enterprise Data Governance Over 50 Enterprise Analytics Recommended Projects 5

6 Domain-Specific Technical Architecture The High-Level Technical Architecture represents the composite view of the three domain architectures. Domain 1: Federated Information Management Ex. Quality Analytics High-Level Architecture Domain 2: Big Data and Real-Time Decision Support Ex. M-CIRCC Domain 3: Open Analytics Ecosystem Ex. Digital Health Engine 6

7 Data Integration Data Integration UMHS High Level Technical Architecture- UMHS (Illustrative) has a variety of current technologies that may be considered in establishing the future state enterprise analytics technical architecture. Data Acquisition Layer Data Storage Layer Analytical Data Layer layer Discovery and Analytics Layer Information Sharing Layer Structured Data Sources UMLS Unstructured/ Semi-Structured Data Sources Enterprise Data Assets Master Data Reference Data External Data Catalog EDW Environment (Common Standards) Analytical Application Data Sets Research Applications Quality/Clinical Decision Support Reports, Dashboards & Extracts Data Mining Exploration & Discovery Modeling & Analytics Epic Integration Knowledge Management Portal External Collaboration Portal Finance FinClarity Text Mining Internal Shared Folder External Data Sources NetRev Education Real-Time Applications Cognitive Computing Patient Portal Mobile Devices Other Natural Language Processing Other Secure File Transfer Data Integration Batch Real-time Near Real-time Machine Image Web Services Analytics as Service External UMHS Customers 7

8 Logical Use Case Diagram: Acute Hemodynamic Instability Enterprise Data Warehouse 8

9 Analytics Use Case: Clinical Research UMHS is using the enterprise analytics strategy to support clinical research through the Early Detection of Hemodynamic Decompensation Pilot (M-CIRCC). RDW HSDW 9

10 In the Past Year 10

11 Enterprise Data Set Catalog 11

12 Data Model, Data Dictionaries, 12

13 Data Glossary

14 Enterprise Report Catalog 14

15 Detailed review of reports, extracts etc Description The daily 30 Day Primary Care (PC) Flu DX Trend by Age Group report is intended for the Primary Care Clinics. The report displays visit counts of patients who have had a DX of flu within the past rolling 30 days by specific age group. The first page of the report includes a graph for all PC clinics and a summary cross tab with detail. Each individual page thereafter, displays a cross tab for each Department/ACU. This report includes Sunday and Holiday dates. Categories for age groups include 0-4, 5-24, 25-49, and 65+. The diagnosis for flu is based upon ICD-9 and ICD-10 coding. ICD-10 codes will not be in effect until Oct Report Type Outpatient Extraction/Analytics Tool Epic-Crystal Report Group UM AMBULATORY - USER Report Format ACROBAT PDF Epic Template No. MiChart Report No UMHS Source No. UAMBC_ _01 15

16 UMHS Data Warehouses: 60tB data (est)

17 Federated data warehouse info 17

18 UMHS Data Warehouse Strategy- COMPASS effort 18

19 UMHS Web Services- Long History of Development 19

20 UMHS Custom Epic Web Services 20

21 Data Governance Roles and Responsibilities: Data Stewards Data Stewards Group Executive Lead Subject Area Potential Steward Purpose: Suggested Members: Implement data standards, policies, and processes driven down by Data Governance Working Group Finance Directors, Research Directors Administrat ive UMHS CEO Revenue Cycle Integrated Chief Revenue Cycle Officer UMHS CEO Facilities VP Facilities Responsibil ities Resides in business units where they are accountable for all data definitions within subject area Implements and monitors Data Governance standards, policies, and processes for functional area specific systems Acts as advisor to the Enterprise Data Governance Committee and various working groups in establishing and updating Data Governance standards, policies, and processes Works with data users and architects to define and translate requirements needs into technical and data specifications Provides recommendations concerning data access controls ensuring data is shared appropriately and widely Monitors and reports Data Governance metrics to the Enterprise Data Governance Committee Responsible for defining and maintaining Business Terms Glossary, business data definition and master data definition Defines business rules, parameters, and related calculations for their assigned subject area. Ensures processes and controls to manage data throughout the data lifecycle Validates data, reports and data quality test results to ensure data integrity. Participates in root-cause analysis for data defects Clinical UMHS CFO Finance Exec Dir Finance (Hosp), (MGD) Dean or UMHS CMO... TBD Patient Lab Provider Pharm/Meds... TBD UMHS CMO Chair of Pathology UMHS CMO/Office of Clinical Affairs Chief Pharmacy Officer Research Senior Dean, Research Biorepository Director of Bio repository Cancer Center Director, Cancer Center Escalations to: Enterprise Data Governance Committee O mics Director of Bioinformatics Escalations from: Level Business Users, and Working Groups Non-IT leadership, leadership identified business user Education Senior Dean, Education... TBD Learner Associate Dean of Medical Education

22 What We have been able to do with new capabilities 22

23 Risk-based scoring, Modular Build Example; PreDict Trackboard

24 Improved visualization of Data; Quick Data Marts, imbeded BI tools in EMR 24

25 UMich API Management Architecture Overview API and Application Developers firewall Single UMich API Storefront Applications Researchers, Patients Students/Faculty Partners & Other Application Users API Management & Reporting Console API Admins & Resource Auditors DMZ Gateway Management API Management firewall Level 2 network domain Level 1 network domain L2 LDAP L1 LDAP Security Providers IIB ESB MedBus ESB WSO2 ESB Service Providers MiChart WDR RDW M-Pathways Class Scheduling Resource Providers

26 Enterprise Service Bus; Across the entire University MedBus Architecture

27 SMART and FHIR SMART= substitutable medical apps reusable technology FHIR= Fast Health Interoperability Resources Open Source Specifications to integrate apps with EHR, portals and HIEs. -Scopes & permissions= OAuth2 -Sign-in= Open ID Connect -UI integration= HTML5 27

28 Proof of Concept; Epic FHIR Hackathon- Charlson Comorbidity Scoring System App, using Epic FHIR resources; 1. Patient 2. Condition Developed by Steve Fayz UMHS Developer 28

29 Proof of Concept; Epic FHIR Hackathon- Charlson Comorbidity Scoring System 29

30 Proof of Concept; Epic FHIR Hackathon- Charlson Comorbidity Scoring System Developed by Steve Fayz UMHS Developer 30

31 Monitoring in the ICU: Filtering noise from real and meaningful data Using APIs 31

32 Logical Use Case Diagram: Acute Hemodynamic Instability Mark Salamango CIO, MCIRCC 32

33 UMHS Epic EMR APIs (Foundation, Custom vs FHIR) for use by 3 rd party Vendor Data Requirements for Airstrip & UMHS API Allergies - FHIR Flowsheets - Custom Vitals - Custom Labs - Custom Meds - Epic (copied as custom) or FHIR Patient Lists - Custom Pt. Infp - Custom Care Team - Custom Notes - Custom Patient Search - Custom -Problems/Diag s FHIR We started studying the AirStrip's server API. 3 rd party vendors are not typically able to use foundation epic APIs This is the API their mobile clients call. Behind this API, AirStrip writes an adaptor to different EMRs like Epic, Cerner, Allscripts etc. Our goal is to provide an array of web services for the AirStrip's server to call. 33

34 Big Data Analytics Architecture: initial focus hemodynamic instability MCIRCC Big Data Analytic Platform Pilot Mid-Point Report, Fall

35 Creating Critical Care DataMart Fully Integrated and Highly Consumable

36 AHI Analytic for Hemodynamic Instability Continuous Monitoring and Assessment of Hemodynamic Conditions Using Computer-Aided Analysis of Physiological Signals Novel Complexity Measures from Heart Rate Variability (HRV) Novel Morphology Based ECG Contour Analytics

37 LHSNet Project Update Phase I: Sprint 11/9/15 5/9/16 April 29, 2016

38 PCORnet PCORnet: ~85m patients in 34 Networks 13 Clinical Data Research Networks (CDRNs) (LHSNet is one of these 13) 21 Patient Powered Research Networks (PPRNs) Committed to collaboration Shared governance (IRB/Consent/Data Use) Leverages organized electronic patient data

39 PopMedNet Common Query System

40 9 partner sites of the LHSNet CDRN Essentia Health Allina Health Medica Health Plans Intermountai n Healthcare Olmsted County Public Health Mayo Clinic University of Michigan Ohio State University Arizona State University Potential access to ~ 9.5 million linked health records

41 LHSNet: Sprint Achievements One of two Phase II PCORnet Clinical Data Research Networks 6 months to catch up to 11 other Phase I established CDRNs: 11/9/15 5/9/16 Governance / Engagement Umbrella IRB approval Reliance IRB w/ Mayo Policies and Procedures Engagement strategy Data Governance LHSNet Workgroups PCORnet Participation Data sourcing / Data management infrastructure Datamart built to CDM V3 Sourcing Data Claims/EHR Data Linkage Establish a node, install PopMedNet software Run Data Quality Checks Execute a Diagnostic Query Execute a Data Characterization Query Research planning / Clinical trials infrastructure Create cohorts: Obesity, Heart Failure, O.I. Work toward research integration in care processes Common consent forms Prepare to participate in network-enabled research = complete = in progress

42 Research Open Data Sharing Networks Patient-Centered Outcomes Research Institute (PCORI) University of Michigan Architecture

43 Detail Accomplishments COMPLETE. LHSNet Governance Policy Approved LHSNet Data Governance Policy Approved LHSNet Research Policy Approved UM Technical Infrastructure Compliance Approval Received UM Authorization to Operate (Security Approval) Received LHSNet Cohort Workgroups Established for HF, OI and OBS LHSNet Cohort Criteria/Technical Specifications Approved for HF, OI and OBS LHSNet OI Patient and Family Engagement Plan Developed LHSNet Research Intake Portal Launched LHSNet.org) LHSNet Research Tracking Tool Developed UM Production DataMart Established UM PopMedNet Installed UM SAS Installed UM PCORnet/CER Research Town Hall Event UM Patient Engagement in Research Town Hall Event UM Formal Diagnostic Query Executed UM Umbrella IRB Complete UM Reliance IRB Complete

44 Detail Accomplishments CLOSE. UM Formal Data Characterization Query LHSNet demonstrate progress in linking claims/coverage data to EHR/clinical data for an unselected population of 1 million individuals through documented partnership with 1 external data partners* *UM DUA with BCBSM pending Network Member Name n Linkable Agreements CMPL? Essentia CMS MSSP 32,290 N Allina Medica Claims 271,161 N Michigan BCBS 266,461 N Intermountain Select Health 528,178 N Mayo Mayo Medical 123,000 Y Ohio State OSU Health Plan 32,000 Y Totals 1,253,090 LHSNet Patient and Stakeholder Engagement Plan

45 Questions?/Discussion 45