Sarah Oraby, MS, Raj Iyer, MBA Mike Wall, Pharm D, MBA UChicago Medicine, Chicago IL

1. An Enterprise Data Warehouse Solution That Bridges Best Practices in Data Standards and Quality, Data Governance, and Data-warehousing Operations S28: Wrangling with Clinical Data for Insights Sarah Oraby, MS, Raj Iyer, MBA Mike Wall, Pharm D, MBA UChicago Medicine, Chicago IL

2. Disclosure • We have no relevant financial relationships with commercial interests to disclose.

3. Learning Objectives • After participating in this session the learner should be better able to: • To build a scalable infrastructure that can support data growth and hospital system expansion • Develop a data model that is designed to integrate clinical and operational data • Develop a framework that will support first of its kind data standardization through reference data management , metric management and data quality monitoring/anomaly detection • Establish a cross-functional governance framework and processes that will enable the organization to leverage the technology and platform to realize enhanced data output productivity and cost savings

4. About UChicago Medicine • The University of Chicago Medicine, with a history dating to 1927, is a not-for-profit academic health system based on the campus of the University of Chicago in Hyde Park with hospitals, outpatient clinics and physician practices throughout Chicago and its suburbs. UChicago Medicine unites five organizations to fulfill its tripartite mission of medical education, research and patient care:

5. UChicago Medicine Hospital System

6. An Enterprise Data-Warehouse: The Journey • The problem/gap and the vision • The project team • The product • The organizational shift • Lessons learned and take-aways

7. The Driving Force • The Problem/ Gap • Disparate data limits analytics • Uncertain/ no data quality • Redundant preprocessing • Variable data literacy • Lack of governance • Incomplete subject area representation • Limited infrastructure • The Vision • Unified/integrated data sources; better and faster analytic outcomes • Structured/automated data quality management • Streamlined operations • Shared decision and knowledge • Governance & oversight • Built across all departments/subject areas • Scalable infrastructure

8. The Project Team Project Steering Committee Executive Level Sponsors Subject Area Experts Finance, Clinical, IT, Clarity, Reporting Program Manager Project Management oversight Technical Project Oversight Director-Level Management from Data & Analytics, IT Data Architect (3) Data Staging ETL (2) Programmer (1) Quality Assurance (3) Reference Data Mgmt. (1) Business Analyst (1) ETL Modeler (2) Database Admin (1)

9. The Product: A Unified Data Platform (UDP) • The Unified Data Platform (UDP) is the preferredUChicago Medicine data and analytics platform that will be used to support clinical and operational initiatives for the health system • Why is UDP the preferred platform? UDP uncages data! • Unified:A single platform for all clinical and operational initiatives • Native and Normalized: Ability to deliver data in both a native (source data directly in data lake) and normalized (cleansed and standardized data in EDW) form • Consistent: Supports data consistency through standardized metric definitions and data quality management • Available: Ensures the right data is available to the right users through access control • Governed: Facilitates proper data use and data quality through policies, standards, and guidelines • Efficient: Reduces data pre-processing burden of analysts and end users (e.g. repeatable filters, starter queries)

10. 3 Main Deliverables of UDP

11. UDP Capabilities Delivery Roadmap 2018 • Fully operationalize data platform with proper governance across all functions • Sources Integrated: Population health, value based care and other external sources, quality data DW, IT datawarehouse have been retired • All analytics and reporting sourced from UDP • Data integration across all subject areas (HR) • Integrate additional data sources and onboard early adopters • Sources Integrated: hand hygiene data, scheduling, patient logistics data, claims, operational data, supply chain • Reports: research extract, budget, no show prediction, schedule optimization, all go forward reports • Data Quality framework: • EDW with applied data quality and data cleansing • Expanded subject area data integration • Implement Proof of Concept for unified architecture and process • Sources Integrated: Clarity, third-party vendor data, cost-accounting data, Patient satisfaction, SS Death Registry, billing data • Extracts: The largest reports: vendor data benchmarking, CMS quality reporting, IDPH regulatory, PHIS • Data Quality framework (with machine learning capabilities) Reporting & Analytics Output & Efficiency Sep & Beyond May - Aug Jan - April Time Minimal Output & Efficiency High Output & Efficiency

12. UDP architectural vision Governance To Vendors State CMS RDM Late-Binding Metrics Late-Binding Data Quality Analytics and Dash-boarding Tools Presentation Tool Governance DQM Reports RDM Editor Metric Editor DQM Editor Meta Data EDW Data Marts RDM DQ Results Rules OMOP Star DW Lake Data Marts Late Binding Metrics UDP Huddle Other’s DE-ID EDW UDP Oversight DQM Data Governance Council Big Data Data Lake Payer UDP Oversight Unstructured Data Patient Satisfaction Clarity subset Cost Accounting subset Caboodle subset MDM Staging DQM ETL ETL ETL Vendor Payer Cost Accounting Clarity Caboodle Unstructured Data Patient Satisfaction Source MDM DQM Vendor

13. The Data Platform consists of: • Data lake: Raw data that is extracted directly from the source • Enterprise data warehouse (EDW): Integrated data that is high quality, fidelity, and value • Data marts: Series of analytics databases specific to the needs of the data consumers (e.g. reports, extracts)

14. Governance Structure Oversight and Strategy Guide and Promote Steering Committee • Governance Support Liberate, Cleanse and Apply Data Governance Committee Demand Management Committee DG Program Office DM Program Office Data Quality Management Users, Stewards, Experts Tools, Solutions, policies • UChicago Medicine Clinical, Operational, and AdministrativeDepartments IT/UDP Data Science and Analytics Data Quality Engineering

15. The biggest organizational shifts… • The cross functional proof of concept project translated into a cross-pollinated data warehousing team with best practices learned from across departments into one new team • Redefined governance committees provide a platform for the voices of both data professionals and business leaders where data professionals advocate for data quality and business leaders advocate for prioritization of resources and funding towards the most valuable data pursuits • Laying the foundation to support data quality management and related process management with a dedicated team that works with data governance and subject area experts to ensure data integrity of the data warehouse.

16. Lessons Learned • Prioritizing data requests requires a long hard look at the data that exists, how well its understood and full transparency across the organization leaders. Consider all your data request intake channels. • Data Governance council is a decision making body, they cannot review and approve policies and monitor and solve data quality issues. A dedicated team for data quality management must exist to support the data governance council • Start governance with education and literacy, organizational changes and process changes around data may result in culture shifts. Minimize confusion and maximize consensus by ensuring the key audiences are well informed of these changes and the roadmap

17. Practical Application of this Session • Gives the audience an example project structure and framework for building an enterprise data warehouse platform • Gives the audience critical design elements and types of data that should be included in an enterprise data warehouse platform • Gives the audience critical governance structures, framework and roles that are necessary for supporting an enterprise data warehouse

18. AMIA is the professional home for more than 5,600 informatics professionals, representing frontline clinicians, researchers, public health experts and educators who bring meaning to data, manage information and generate new knowledge across the research and healthcare enterprise. AMIA 2019 Clinical Informatics Conference | amia.org

19. Thank you!