Texas Health Resources CBI Journey from Hindsight to Foresight. Natalie Dion and Ed Macko

Transcription

1 Texas Health Resources CBI Journey from Hindsight to Foresight Natalie Dion and Ed Macko

2 Conflict of Interest Disclosure Natalie Dion Ed Macko Have no real or apparent conflicts of interest to report. 2

3 Learning Objectives Describe critical elements of a comprehensive clinical business intelligence (CBI) strategy and roadmap Discuss best practices in executing the CBI strategy Explain the process and technology used to integrate and transform disparate data into actionable information Compare analytical processes before and after the CBI strategy is implemented Summarize the value of CBI to the organization, as well as tangible benefits to clinical and business users and to the IT organization 3

4 Agenda Texas Health Resources The Challenge The Solution Case Study Governance Clinical Business Intelligence (CBI) Roadmap CBI Technology ACO Dashboard Lessons Learned 4

5 Texas Health Resources 5

6 6

7 Texas Health Resources 25 hospitals in North Texas 14 wholly owned hospitals 133,903 Inpatient Visits 1,238,392 Outpatient Encounters 469,309 ED Visits 89,452 Surgeries 27,200 Deliveries 5,500 Active Physicians 7,500 RN s 22,000 Employees 7

8 The Challenge 8

9 Health Care Landscape New Reimbursement Models Bundles and episode payments Reimbursement rewards for top care and penalties for poor care Financial rewards for efficient care New Care Processes Care coordination and population management Patient-centered medical homes Accountable Care Organizations Reporting Requirements Measure effectiveness of care protocols Assess risk factors for a defined population Manage diverse payment models Data mining to develop clinical evidence based protocols 9

10 Different Capabilities for Different Question Types Be Prepared and Plan for Predictive and Prescriptive Analytics Beware the False Summits! 1 Descriptive: What happened? Analyze the Past Reports and graphs Dashboards Drill down/around Degree of Competitive Advantage 2 Predictive: What might happen? Analyze the Future Statistical models Correlation analysis Forecasting Simulations 3 Prescriptive: What should we do? Analyze the Actions Mathematic models Linear programming Constraint programming Three levels of BI maturity, with each level more difficult and more advantageous than the last. Degree of Difficulty Source: The Advisory Board research and analysis. 10

11 BI Is an Essential Capability for Accountable Care Representative Examples for Accountable Care Risk Levels Quality Risk Performance Risk Utilization Risk Descriptive Service line or physician performance Quality measures performance Identify variations in practice Patient chart scanner Patient satisfaction Bundled payments scorecard Evidence-based guidelines compliance PCP attribution Disease dashboards Contract performance scorecards Benchmarks comparisons Ambulatory care sensitive admissions Predictive Readmission risk Fraud detection Financial modeling Patient compliance Population risk Complications risk (e.g., admissions or HAIs) Estimate demand destruction Prescriptive Inventory optimization Nurse scheduling Care pathway optimization Patient discharge planning Patient engagement approaches Throughput optimization Facility and provider network planning Cognitive support for clinical decision-making Source: The Advisory Board research and analysis. 11

12 roviders shifting business models to adapt to change Silos and dysfunction Patients pinball btw providers Adversarial No information sharing Coordination and Integration Patients are navigated to best settings of care Partnering Extensive information 12

13 New care models require actionable insights Better Outcomes Smarter Decisions Actionable Insights Usable Information Relevant Data 13

14 Complete View of Integrated Care Data Reality CEO COO CFO CMO CNO CQO Data Data Data Data Data Data Data Data Data Data Population Health Data Management Payor Partners Data Data Data Data Data Data 14

15 CBI Charter from individual hindsight to enterprise foresight Instinct & Intuition Fact-driven Reactionary Limited & slow Human Insight Decision Support Directive Real-time Validated Insights Action Support Inefficient Optimized 15

16 The Solution 16

17 Analytics Across the Care Continuum Acute Care Initial Diagnosis Pre Acute A D T C V E A D T Post Acute Health Maintenance 30 Day Readmit Improve outcomes Reduce variation Reduce cost Avoid the author 30 day and re-admission do not necessarily penalties 17

18 Hypertension Case Study 18

19 Joe s Medical Encounters Joe goes to Dr. Smith for a physical. Diagnosed with Hypertension. Has chest pain and goes to Flower Mound ED where tests are run. Needs heart surgery. Goes to THFW for surgery performed by Dr. Jones Follow up visits by Dr. Jones Continued care by Dr. Smith Each visit is recorded in a separate system with a different identifier for Joe. Dr. Smith THPG Physician THPR Flower Mound THR Fort Worth Dr. Jones THPG Physician Joe Medical Record 001 Medical Record 503 Medical Record 145 Medical Record 430 Epic Epic AllScripts (64 versions) CPSI 19

20 Steve s Information Needs Steve needs to analyze physician adherence to new care redesign protocols and measure the effectiveness of the outcomes. He requests data extracts from multiple systems. Uses fuzzy logic to match and merge patient records. Integrates and aligns data that was stored differently in each source system. Creates non-standard business rules to identify patient groups The process is personalized for Steve and cannot be reused by others. THR Fort Worth Dr. Jones THPG Physician Dr. Smith THPG Physician THR THPR Flower Mound Medical Record 145 Medical Record 430 Epic Epic Medical Record 503 SAS Medical Record 001 CPSI Access AllScripts (64 versions) Excel Steve 20

21 Future State CBI THPG Physician THR WO Hospital Data from disparate source systems is integrated into a single repository A unique patient ID is assigned Enterprise data definitions and metrics are applied to the data once and shared across THR. Consistent business rules are applied to the data and shared for enterprise use. AllScripts (64 versions) EMPI Data Integration CBI Data Quality Epic THR is empowered with self serve reporting capabilities. Minimal reliance on IT to create reports and data extracts THPR JV Hospital Enterprise Business Rules THPG Physician More time to analyze information versus gather, clean and integrate data. expressed in this presentation CPSI are those of 21 Epic

22 Governance 22

23 CBI Steering Committee Members Cross-section of Executive Leaders across the System Charter Define the CBI Vision Assure alignment with the THR vision & strategies Allocate resources and control funding expressed in this presentation are those of 23

24 Responsibilities Approve prioritization of project objectives and outcomes Budgetary approval Empower key individuals to enforce CBI Guiding Principles Help balance conflicting priorities at the organizational level Foster and support positive communication across the organization Act as the escalation point to enable accountability and action across the enterprise Provide active sponsorship across the system User and stakeholder outreach and advocacy Enterprise-wide standards and strategies 24

25 CBI Guiding Principles 1. A single source for all analytical data in the company 2. A standard definition and common usage of all company metrics 3. A self-service mechanism to access and utilize the data 4. A focus (by the business) on governing the processes surrounding the creation and usage of the data 5. A focus on analysis of the data rather than the collection of it 6. There is only one authoritative source of the data and CBI will be populated using that source. 7. View information as an organizational asset to be utilized for the good of the company, rather than hoarded as a source of power. 8. Investments in CBI will be guided by return of value to the organization and therefore DISCLAIMER: must The views be and opinions metrics-driven. 25

26 CBI Roadmap 26

27 Governance Establish Use Cases RFP Process Requirements Approval Business Case and Roadmap 27

28 CBI Use Cases Population Health Analytics H&V Procedural Analytics H&V Care Redesign Referral Analytics 28

29 CBI Roadmap Q1 Q Q Q Q , etc. Optime Other Use Cases Referral Analytics CVIS Cedaron Procedural Analytics (H&V) Care Redesign (H&V) ACO Dashboard Release 3 FirstView Release 1 Release 2 Post Acute Relationships Post-Acute Claims Model IBM Material Labor Model Material and Labor CareConnect AllScripts NextGen Post Acute Data Dependencies EMPI Ext Benchmarks (STS, ACC) Referrals & Claims Data Surveys, Scheduling Orders, Procedures, Labs, Medications, Diagnosis, Clinical Findings Foundational Data: Encounter, Patient, Provider, Facilities, Reference Data 29 1/2013

30 Establish CBI Roadmap Requirements for new use cases RDDC approval of new uses cases Re-establish uses cases CBI Journey Integrate AS Foundational Data, Orders, Labs, Proc, Diag, Meds, Clinical Findings Integrate CC Orders, Labs, Proc, Diag, Meds, Clinical Findings ACO Measures AS ACO Measures CC First View Analytics Integrate CC Foundational Data Established architecture & process standards Trained team on data model & tools Convert legacy environment Upgrade InfoSphere Install Software Acquire and install hardware Signed contract RDDC approval RFP Process Q Q Q Q Q Q Q Q Q CC = CareConnect AS = AllScripts Use Case Requirements Original Use Cases Q Q

31 CBI Technology 31

32 Advanced analytics requires a robust, comprehensive information platform Trusted Relevant Governed Transactional & Collaborative Applications Analyze Integrate Business Analytics Applications Content Big Data Manage Master Data Cubes Warehouse Streams Data External Information Sources ODS Content Data Model Information Governance Streaming Information Govern Security & Privacy Lifecycle DISCLAIMER: The views and opinions Quality Standards 32

33 CBI Solution Overview EXTERNAL SYSTEMS (Premier, etc.) Core Warehouse Model IBM Healthcare ESB/HL-7 IBM Data Model Physical Models InfoSphere Warehouse EDW Data Mart Layer Conformed Dimensional Model Cognos Nursing Dashboard Physician Profiling Dashboard Quality Dashboard Core WH Layer SPSS Information Server Staging Layer SOURCE SYSTEMS (EPIC Clarity, etc.) 33 Smart Analytics System

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35 ACO Dashboard 35

36 Purpose Analytical tool for facilitating population health management (e.g., ACO) Quality measurement At risk populations Preventive care Care coordination Patient experience Cost of care 36

37 Purpose Analytical tool for facilitating population health management (e.g., ACO) Quality measurement At risk populations Preventive care Care coordination Patient experience Cost of care 37

38 CBI Content Statistics 2.7+ Billion rows of data Data Elements 100+ Tables 3 TB data Historical data back to 2009* Extract data from 3 source systems 38

39 CBI Content Subject Areas Phase Subject Area # Tables # Data Elements Record Count Clinical Findings ,500,888,722 Clinical Orders ,449,007 Population Pharmacy Orders ,000,000 Heath Analytics Medications ,000,000 Phase 1 Diagnosis ,531,506 Foundations Procedures ,660,909 Groupers Encounter ,792,653 Patient ,683,494 Practitioner ,000 39

40 Complexity 40

41 ACO 33 Initial set of performance measures for ACOs Pay for reporting (Y1), for performance (Y2+) Overlaps with Care Maps, HEDIS, PQRS, Meaningful Use 4 Domains At risk populations (12) Preventive health (8) Care coordination safety (6) Patient experience (7) Data needs Care coordination: Healthways PMT Reporting to CMS: TBD Analytics: Texas Health CBI 41

42 ACO Dashboard Demonstration The following screen shots contain test data and do not reflect actual performance. The screen shots are only intended for demonstration purposes. 42

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51 Future Work Data sources Allscripts Plus 2 Pioneer and other ACO populations Quality Measures: Remaining at risk population measures Preventive health measures ACO definitions* 2013 THPG targets Patient experience Care coordination Cost of care 51

52 Data Governance Case Study OMB Categories CareConnect AllScripts Race Count Race Count White WHITE 1,719,932 White 345,622 WHITE-HISPANIC 63,039 CAUCASIAN 55,368 Black or African American BLACK 309,151 Black or African American 63,853 American Indian or Alaska Native BLACK-HISPANIC 3,381 AFRICAN AMERICAN 5,599 NATIVE AMER-HISPANIC 1,940 American Indian or Alaska Native NATIVE AMERICAN 10,374 Asian ASIAN 62,263 Asian 9,303 CHINESE 36 FILIPINO 64 JAPANESE 7 ASIAN-HISPANIC 1,045 Native Hawaiian or Other NATIVE HAWAIIAN-PI 2,655 Native Hawaiian or Other 20 Pacific Islander Pacific Islander Not supported UNKNOWN 825,877 Undefined 773,360 1,186 OTHER 1,242 Refused to 132,763 report/unreported UNKNOWN-HISPANIC 76,015 More than one race 3,987 TWO OR MORE RACES 9,583 HISPANIC 6,180 52

53 Lessons Learned Align roadmap to strategic plan Active business sponsor Engaged clinicians and business Data governance Incremental delivery 90 days 53

54 Natalie Dion Thank You! Ed Macko