Data Analytics in the Military Healthcare System Reorganization April 15, 2015

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1 Data Analytics in the Military Healthcare System Reorganization April 15, 2015 Col Albert (Al) Bonnema, MD MPH Chief, Information Delivery Division in the Defense Health Agency's Health IT Directorate DISCLAIMER: The views and opinions expressed in this presentation are those of the author and do not necessarily represent official policy or position of HIMSS.

2 Conflict of Interest Albert Bonnema, MD MPH Col, USAF, MC Has no real or apparent conflicts of interest to report. HIMSS 2015

3 Learning Objectives Describe the translation of new strategic plans into analytics requirements for a reorganizing healthcare system Describe the benefits of a new Analytics Capability in executing Military Healthcare System's requirements Examine the analytics technology modernization in the midst of an EHR modernization Discuss the development of a Performance Management System that supports the MHS Strategic Plan

4 Change 1.0 Health IT Legacy Portfolio Sustainment Costs > Budget New requirements exceed IT staff capacity Development costs exponentially increase due to complexity Difficult to define a modernization or innovation starting point on 20 yr old legacy systems Functional Owners and Product Managers fear change at least the old stuff works

5 Change 2.0 Medical System Merger Congress legislates Military Health Care System to consolidate common missions On October 1, 2013, the Defense Health Agency is established Tricare Management Activity (TMA) stands down Army, Navy, Air Force, and TMA aggregate common functions like Pharmacy, Logistics, Research, Health IT into Shared Services Simultaneous establishment of new governance, mobilization of 1000 s of employees, realignment of budgets and resources, and consolidation of duplicate functions

6 Change 3.0 Everyone Wants Data Demand for data/information to support MHS modernization exceeds analysts and data marts capacities 20 years of legacy data marts, tools, and analysts could be described as feudal kingdoms Analysis of data objects of 10 largest data marts show >70% duplication The work of a core group of analysts scattered among data marts can be described as outstanding to cutting edge health services research Meticulous quality control and HIPAA protection processes

7 Building MHS Analytics Capacity Governance Data Governance Business Intelligence Governance Analysis Skills Types of Analysis Cultural/Data Literacy Analytics Business Intelligence Architecture Data Sources/Currency/Latency Data Models Analytic Tools/Infrastructure Analytics Maturity Model from Advisory Board

8 Strategic Re-Alignment of the Health IT Portfolio Medical Devices PACS 3 rd + Gen EHR Core Network & Security Infrastructure Content Mgt Applications Trusted Data Services Health Information Exchange Data Warehouse & Analytics Partnered Data Services Health Information Exchange/Health Information Service Provider Eligible Providers Patients Firewall Internet Hospitals Business Associates Work in Progress 1. Acquire new EHR 2. Acquire HIE 3. Rationalize Applications 4. Consolidate Medical Networks 5. Modernize Analytics

9 MHS Analytics Requirement Scope Direct Care System - ~35% Purchased Care System - ~ 65% The Military Healthcare System is more than a Hospital System it is also an insurance plan, health promotion, research, military mission support, disability system, occupational health, humanitarian, public health, etc. MHS leaders and managers want a single view of their mission Major implications for the new EHR, Portfolio Rationalization, and Health System Modernization

10 Top Ten Success Factors for MHS Analytics Modernization Leadership Joint Governance New EHR Analysts Workflow Data Governance & Management Health IT Shared Service Analytics Operations and Service Offering Analytics Service Catalogue Technology Investment Scalability and Usability

11 Leadership High Reliability Organization Mandate Better Technology Better Information Transform Health Care Goal 5: Achieve Rapid Learning & Technological Advancement Goal 3: Inspire Confidence & Trust in Health IT Goal 2: Improve Care, Pop Health, and Reduce Costs through the Use of IT Goal 1: Achieve Adoption & Information Exchange through Meaningful Use of IT Goal 4: Empower Individuals with Health IT to Improve their Health and Healthcare System 11

12 Leadership Partnership for Improvement (P4I) By July 15, 2015, I want a report that clearly demonstrates the PMS capability to drive system wide improvement for the identified common executable goals against common standards and for the dashboards to have measures identified in all areas covered by the MHS Review. (SECDEF Memo, 1 OCT 2014) Demonstrating PMS Capability to Drive System- Wide Improvement: Governance (upper circle) selects focus areas for improvement, informed by PMS support. Once approved, focus areas are communicated to the Execution components (lower circle). In addition to selecting focus areas, governance decides roles and responsibilities, and what elements of change package should be developed centrally (e.g., evidence based guidelines, simulation, communications). PMS provides Governance and Execution customers with information to monitor improvement. Approved 30 Core Measures with 5 initial Focus Areas Action Plan groups own the implementation and dissemination

13 New Governance Functional Owners -New Joint Committee structure with decision authority Medical Operations Group -No longer dependent on portfolio boards Partnership for Improvement Steer Cmte Data Management Board Enterprise Intelligence Steer Cmte Clinical Analytics Council Business Analytics Council

14 New EHR Today, the MHS operates 6 different EHRs with many different instances. Aggregating the data for analytics and interoperability is a negatively impacts latency, processing, and storage Data governance practices work for the local EHR instance but not for enterprise use cases Future, the MHS should have a single patient record Data governance will be implemented from the beginning Data exchange will benefit from modern data exchange and virtualization technologies

15 New Analysts Workflow Current most data work is completed by Super Analysts and completed by hand monthly Pros deep understanding of data; meticulous Cons little automation; large storage volume; proprietary SQL code; reduced ad hoc availability In the Future discovery measures are automated when governance approves Pros experts in ELT/ETL, modeling, BI automate; cost/measure decreases; more ad hoc and special studies Analysis Workflow Process c/o SAS Cons disconnects between analysts/it; data quality unknowns 15

16 New Data Management & Governance The DHA Data Manager resides within the DHA HIT Information Delivery Division and is organizing the catalogue of service offerings. The Data Services Branch is organized into four Sections: Governance, Architecture, Acquisitions, and Operations MHS data activities have been fragmented and ineffective as evidence by portfolio duplication, numerous failed IT projects, duplicate patient issues, and millions of non-standard terms in EHRs. Data Management is a critical core mission capability and every strategic initiative is dependent on its management EHR, HIE, Interoperability, Portfolio Rationalization, P4I, and Analytics Governance and process issues are far greater impediments to success than the technical issues that must be confronted during the process of creating shared services. Gartner - Shared Services in Government: Critical Success Factors, Kost, Published: 10 August

17 New Health IT Shared Service Army TMA DHA HIT Navy Shared Service USAF Defense Health Agency s Health IT Directorate is the consolidation of IT resources from the Services and TMA. Mr. Dave Bowen, SES, is the HIT Director/CIO.

18 New MHS Analytics Service Offering Data Services Web Strategies & Collaboration Information Delivery Division Enterprise Intelligence Health Information Exchange Registries

19 New MHS Analytics Service Offering Function Target Activity Functional Communities E-Web Strategies Wisdom Presentation Layer Collaboration Web Platform Office Workflow E-Intelligence Data Services Knowledge Information Data Logic Layer Data Layer Analytics Measures Reporting Decision Support Modernization Governance Interoperability UNCLASSIFIED 19

20 New Analytics Service Catalogue Better Technology Better Information Transform Health Care Goal 2: Improve Care, Pop Health, and Reduce Costs through the Use of IT Surveys Goal 4: Empower Individuals with Health IT to Improve their Health and Healthcare System Goal 3: Inspire Confidence & Trust in Health IT Research Analytics CDS Data Information Knowledge Action 20

21 New MHS Analytics IT Requirements Visualization Data Drill Down Data Quality Data Discovery Data Latency Analytic Tools Analytic Logic New EHR Alignment

22 New Infrastructure for Scalability and Usability Enterprise Data Warehousing and Big Data MHS Enterprise Portal approved for HIPAA Data Reporting, Business Intelligence, and Advanced Analytics Enterprise Registries for Research, Population Health, and Immunizations MHS Cloud Computing MHS Enterprise Health Information Exchange 22

23 Questions? Col Albert (Al) Bonnema, MD MPH Chief, Information Delivery Division O:

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