Clinical Data Warehouse Modeling: A Practical Approach to Modeling a Successful Healthcare EDW

1. Clinical Data Warehouse Modeling: A Practical Approach to Modeling a Successful Healthcare EDW

2. Agenda But as long as we plan… Moving fast can be scary… • Landscape Challenges • Clinical Data Warehouse Success Story • a Case Study • Lessons learned • Q & A

3. Landscape: Healthcare BI Drivers 1 2 3 Avoid cost overruns (shared risk) Obtain bonuses and/or incentives (shared savings) Meet metric-based reimbursement requirements 4 5 Fill the revenue loss gap Improve care quality

4. Challenges • Healthcare data complexity • Structural diversity of operational systems • Workflow variations alter the fact storage • Domain-specific knowledge (operational and IT) for data source analysis • Large scale differences in granularity, terminology and perspective in source data • Overlapping/competing solutions and tools

5. Case Study Building for Meaningful Use and the Future • EHR • Deployment • Capture the data Criteria: Regulation Requirements Analysis and Implications • Capability, process and data element analysis and design – Right data, right format enabled by right workflows Measure, monitor and optimize Aggregation and Analytics Reuse the data • Infrastructure plan and design • BI Tool selection and ONC certification • Proof of concept • Create MU compliance and quality reports CMS other entities • Hospital-based MU compliance assessment tools • Test and submit • Optimize systems and processes

6. Context 6

7. Key: Invested Sponsorship • Value Realization Program • The Program purpose is to promote clinical performance improvement and business value, ensuring Tenet qualifies and receives full incentive dollars as a result of the EHR program • The Program serves to identify, act on, report and monitor the CMS Meaningful Use requirements and value based metrics

8. Integrated Clinical BI Strategy Overview Initial Focus • Point of Care • Cerner • Clinical Quality • eMeasures • Clinical • Operational • Financial • Satisfaction

9. Key: Business Objectives to Solve Determine MU Requirements Attest to CMS Identify Content Sources • Capture the right data in the right format enabled by workflow to support Meaningful Use Stages 1-3 and other related initiatives • Support Tenet’s overall BI Objective, joining of Clinical and Operational data in a common repository Develop Processes Workflows Impacted Data Warehouse Capture Data Reg* Cerner* EDW* • MU Dashboard Make Design Decisions * CERTIFICATION REQUIRED

10. Tools and Methods Building for Meaningful Use and the Future Build and Deploy • Data • Maps • Design • Decisions • Workflows, • Content, Order Sets • ETL Design • Data quality analysis • Value set modification • Operational decisions • Application build • Metric impacts • Reference • Library • Descriptions • Issues tracking • Responsibilities and tasks • Metric impacts • Evidence based  Metric definitions  Code sets  Data definitions  Derived data EDW Analytics Dashboard ONC certification

11. Developing the Clinical Quality Dashboard “Metadata” for each meaningful use objective and stage 1 clinical quality measure including source data (knowledge base) Meaningful Use Dashboard Clinical Quality Measures Analysis Reports • Use Cases: • Meaningful Use Compliance • Clinical Quality Measures Analysis Map to business glossary and data model for accelerated data acquisition from source systems

12. BI Architecture Data Warehouse Platform Meta Data Repository Analytics Dashboard Analytics Mart Enterprise DataWarehouse EDW Query ETL Scorecard SourceSystems ODS Layer Reporting ELT Query ETL Meaningful Use Derivations & Measure Calcs SubmissionReporting DAAC Cerner ODS ETL ETL Outbound HIE Quality HIM MUADM • Cerner • H1 – H5 • CPOE, LAB, RX Other… ADT, Billing, Claims Mgt, Admin (PBAR +) MedHost 12

13. Key: Data Model

14. Data Model Approach • Aligneddisparate source data: syntactically and semantically • Enriched information via reintegrated derivations and calculations • Retained relationship of summarized, aggregated information with detail facts • Managed and Documented analytics with embedded metadata Measure Model Derivation Model Metadata Model Core Model

15. EDW Core Model • Normalized, but not Strict 3NF • Captured critical analysis facts • Ignored extraneous data • Conformed source data; structure and semantics

16. EDW Derivation Model • Normalized • Organized by granularity • Relationships to base fact maintained • Derivations treated as columns • Snapshot per calculation

17. EDW Measure Model • Normalized • Organized by granularity • Relationships to base fact & derivation snapshot maintained • Measures treated as dimension • Snapshot per calculation

18. MU Mart Visit Analytics Model • Dimensional Star • Central facts organized by granularity • Summary generated and persisted • Conformed dimensions • Detail evidence retained in dimensions • Dimensional hierarchies ignored

19. Solution Stats Element Delivered Result EDW Dashboard 2 Dashboards Hospital ownership for monitoring EDW – 19 CQM 59 Unique Data Elements 486 Mappings EDW -24 Utilization 75 Unique Data Elements 379 Mappings Physician Order Entry CPOE EHR Volume Oct-Nov 1537 Physicians Clinical Decision Support Non Pharmacy Rules 50 PowerPlans & Order Sets Developed and Rolled Out 488 Start to finish: Jan 2010 to Sept 2011 • 7 Hospitals attested for 2011 • On track to take 13 hospitals live in 2012 including one Epic site • Total number of facilities' live by 2014, 49 • Building out Stroke and VTE quality measures (preparation for expanding CMS Inpt Quality Reporting requirements) Building for Meaningful Use and the Future

20. Clinical Business IntelligenceMeaningful Use Dashboard

21. Clinical Business IntelligenceMeaningful Use Dashboard

22. Clinical Business IntelligenceMeaningful Use Dashboard

23. Clinical Business IntelligenceMeaningful Use Dashboard

24. Proposed Value Realization Dashboard Clinical Operational Financial Satisfaction Overall $61M $25M $25M $61M $330M Example metrics only

25. Adverse Drug Events Warfarin Examples

26. Lessons Learned …

27. … Lessons Learned

28. Thank You