Demystifying Healthcare Data Governance

1. Demystifying Healthcare Data Governance — Dale Sanders

2. Data Governance in Healthcare Data is the new oil!” — Andreas Weigend Former Amazon Scientist © 2014 Health Catalyst www.healthcatalyst.com As the age of analytics emerges in healthcare, health system executives are increasingly challenged to define a data governance strategy that maximizes healthcare data’s value to the mission of their organizations Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. 2

3. A Sampling of My Up & Down Journey © 2014 Health Catalyst www.healthcatalyst.com Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. TOO MUCH DATA GOVERNANCE (1987) MMICS TOO LITTLE DATA GOVERNANCE WWMCCS: Worldwide Military Command & Control System MMICS: Maintenance Management Information Collection System NSA: National Security Agency IMDB: Integrated Minuteman Data Base PIRS: Peacekeeper Information Retrieval System EDW: Enterprise Data Warehouse (1986) WWMCCS (1992) NSA Threat Reporting (1995) IMDB & PIRS (1996) Intel Logistics EDW (1998) Intermountain Healthcare (2005) Northwestern EDW (2009) Cayman Islands HSA 1983 2014 3 Dale Sanders

5. © 2014 Health Catalyst www.healthcatalyst.com Data Governance Cultures HIGHLY CENTRALIZED GOVERNMENT Centralized EDW; monolithic early binding data model BALANCED GOVERNMENT Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. HIGHLY DECENTRALIZED GOVERNMENT Centralized EDW; distributed late binding data model No EDW; multiple, distributed analytic systems 5

6.  Elements of centralized decision making shared values, rules, and laws; then abide by them and act accordingly © 2014 Health Catalyst www.healthcatalyst.com Characteristics of Democracy ● Elected or appointed, centralized representatives ● Majority rules  Elements of decentralized action ● Direct voting and participation, locally ● Everyone is expected to participate in developing Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. 6

7. ● Inconsistent analytic results from different sources, ● Poor data quality, e.g., duplicate patient records rate ● When data quality problems are surfaced, there is no formal body nor process for fixing those problems ● Inability to respond to new analytic use cases and © 2014 Health Catalyst www.healthcatalyst.com What’s It Look Like?  Not enough data governance ● Completely decentralized, uncoordinated data analysis resources-- human and technology attempting to answer the same question is > 10% in the master patient index requirements… like accountable care Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. 7

8.  Unhappy data analysts… and their customers © 2014 Health Catalyst www.healthcatalyst.com What’s It Look Like?  Too much data governance ● Everything takes too long – Loading new data – Changes data models to support new analytic use cases – Getting access to data – Resolving data quality problems – Developing new reports and analyses Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. 8

9. The Triple Aim of Data Governance ● Pushing the data-driven agenda for cost reduction, © 2014 Health Catalyst www.healthcatalyst.com 1. Ensuring Data Quality ● Data Quality = Completeness x Validity 2. Building Data Literacy in the organization ● Hiring and training to become a data driven company 3. Maximizing Data Exploitation for the organization’s benefit quality improvement, and risk reduction Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. 9

10. – Setting the tone of “data driven” for the culture © 2014 Health Catalyst www.healthcatalyst.com Keys to Analytic Success The Data Governance Committee should be a driving force in all three… – Actively building and recruiting for data literacy among employees – Choosing the right kind of tools to support analytics and data governance Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. Mindset Skillset Toolset 10

12. We need a longitudinal analytic view across the ACO of a patient’s treatment and costs, as well as all similar patients in the population we serve.” © 2014 Health Catalyst www.healthcatalyst.com The Different Roles in Each Layer Executive & Board Leadership Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. 12

13. We need an enterprise data warehouse that contains all of the clinical data and financial data in the ACO, as well as a master patient identifier.” We need a data analysis team, as well as the IT skills to manage a data warehouse.” The following roles in the organization should have the following types of access to the EDW.” © 2014 Health Catalyst www.healthcatalyst.com The Different Roles in Each Layer Data Governance Committee Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. 13

14. © 2014 Health Catalyst www.healthcatalyst.com The Different Roles in Each Layer Data Stewards I’m responsible for patient registration. I can help.” I’m responsible for clinical documentation in Epic. I can help.” I’m responsible for revenue cycle and cost accounting. I can help.” Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. 14

15. © 2014 Health Catalyst www.healthcatalyst.com The Different Roles in Each Layer Data Architects & Programmers We will extract and organize the data from the registration, EMR, rev cycle, and cost accounting and load it into the EDW.” “Data stewards, can we sit down with you and talk about the data content in your areas?” “DBAs and Sys Admins, here are the roles and access control procedures for this data.” Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. 15

16. © 2014 Health Catalyst www.healthcatalyst.com The Different Roles in Each Layer DBAs & System Administrators Here is the access control list and procedures for approving access to this data. Let’s build the data base roles and audit trails to support these.” Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. 16

17. © 2014 Health Catalyst www.healthcatalyst.com The Different Roles in Each Layer Data access & control system When this person logs in, they have the following rights to create, read, update, and delete this data in the EDW.” Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. 17

18. © 2014 Health Catalyst www.healthcatalyst.com The Different Roles in Each Layer Data Analysts I’ll log into the EDW and build a query against the data in the EDW that should be able to answer these types of questions.” “Data Stewards, can I cross check my results with you to make sure I’m pulling the data properly?” “Data architects, I’ll let you know if I have any trouble with the way the data is organized or modeled.” Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. 18

19. The clinical data owners The financial and supply chain data owner Representing the researchers’ data needs © 2014 Health Catalyst www.healthcatalyst.com Who Is On The Data Governance Committee? Representing the analytics customers The data technologist Chief Analytics Officer CIO CMO & CNO CFO CRO Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. 19

20. Data Governance Committee Failure Modes Wandering data governance committees do so because they lack something tangible to govern, and lack the experience to recognize their wandering. To succeed they must develop data management and awareness skills. © 2014 Health Catalyst www.healthcatalyst.com Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. 20

21. Data Governance Committee Failure Modes Technical overkill is very common when a well-intended and overly passionate CIO chairs the data governance committee. A lack of experience with data management and systems is a recipe for agendas that tend to drive inflated or unrealistic design. © 2014 Health Catalyst www.healthcatalyst.com Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. 21

22. Data Governance Committee Failure Modes Politics and political infighting can manifest as passive-aggressive participation in the data governance process. Members pretend to be data-driven and selfless during committee meetings but fall back into territorial or defensive behaviors when returning to their department. © 2014 Health Catalyst www.healthcatalyst.com Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. 22

23. Data Governance Committee Failure Modes Red tape is common within authoritarian forms of data governance. It is the inherent nature of bureaucracy. Committee members behave like bureaucrats of the data, rather than governors and stewards of the data, trying to maximize the data’s value to the organization. © 2014 Health Catalyst www.healthcatalyst.com Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. 23

24.  Data Governance Committee: Constantly pulling for broader data access and more data transparency  Information Security Committee: Constantly pulling for narrower data access and more data protection  Ideally, there is overlapping membership that helps with the balance © 2014 Health Catalyst www.healthcatalyst.com Data Governance & Data Security Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. 24

25. Data Quality = Validity x Completeness To achieve the Triple Aim of Data Governance, the governance committee needs reports that exposes data quality. Data stewards use these reports in their efforts to close the gaps in data quality for the systems of their responsibility. © 2014 Health Catalyst www.healthcatalyst.com Tools for Data Governance Data quality reports Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. 25

26. The data governance committee will also need reports for understanding how the data warehouse is being used. • Who’s using the data? • When is the data being used? • Why acquire the data? © 2014 Health Catalyst www.healthcatalyst.com Tools for Data Governance CRM tools for the data warehouse Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. 26

27. For capturing and filling-in computable data missing from source systems. Sometimes this white space data is manually abstracted and manually integrated on desktop computers using Excel or Access. These tools replace spreadsheets and databases by providing an easy-to-use data entry tool that is tightly coupled with the EDW. © 2014 Health Catalyst www.healthcatalyst.com Tools for Data Governance “White Space” data management tools Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. 27

28. The metadata repository serves as the “Yellow Pages” for the EDW. It is the tool used to browse the EDW data and attributes. – What’s in the data warehouse? – Are there any data quality problems? – Who’s the data steward? – How much data is available and over © 2014 Health Catalyst www.healthcatalyst.com Tools for Data Governance Metadata Repository what period of time? – What’s the source of the data? Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. 28

29. Healthcare Analytics Adoption Model © 2014 Health Catalyst www.healthcatalyst.com Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. 29 Modeled after the HIMSS Analytics EMR Adoption Model, the Healthcare Analytics Adoption Model provides a framework for evaluating an organization’s adoption of analytics. It also provides a roadmap for developing analytics strategies, both for vendors and for internal use by healthcare delivery organizations.

30. Healthcare Analytics Adoption Model © 2014 Health Catalyst www.healthcatalyst.com Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. Level 8 Level 7 Level 6 Level 5 Level 4 Level 3 Level 2 Level 1 Level 0 Personalized Medicine & Prescriptive Analytics Clinical Risk Intervention & Predictive Analytics Population Health Management & Suggestive Analytics Waste & Care Variability Reduction Automated External Reporting Automated Internal Reporting Standardized Vocabulary & Patient Registries Enterprise Data Warehouse Fragmented Point Solutions Tailoring patient care based on population outcomes and generic data. Fee-for-quality rewards health maintenance. Organizational processes for intervention are supported with predictive risk models. Fee-for-quality includes fixed per capita payment. Tailoring patient care based on population metrics. Fee-for- quality includes bundled per case payment. Reducing variability in care processes. Focusing on internal optimization and waste reduction. Efficient, consistent production of reports & adaptability to changing requirements. Efficient, consistent production of reports & widespread availability in the organization. Relating and organizing the core data content. Collecting and integrating the core data content. Inefficient, inconsistent versions of the truth. Cumbersome internal and external reporting. © Sanders, Protti, Burton, 2013 30

31. The progressive patterns at each level – Adding new sources of data to expand our understanding of care delivery and the patient Data timeliness increases – To support faster decision cycles and lower “Mean Time To Improvement” Complexity of data binding and algorithms increases © 2014 Health Catalyst www.healthcatalyst.com Progression in the Model Data content expands – From descriptive to prescriptive analytics – From “What happened?” to “What should we do?” Data governance and literacy expands – Advocating greater data access, utilization, and quality Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. 31

32. 2-4 years 1-2 years © 2014 Health Catalyst www.healthcatalyst.com Six Phases of Data Governance You need to move through these phases in no more than two years Level 8 Level 1 Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. 32 3-12 months – Phase 6: Acquisition of Data – Phase 5: Utilization of Data – Phase 4: Quality of Data – Phase 3: Stewardship of Data – Phase 2: Access to Data – Phase 1: Cultural Tone of “Data Driven” Personalized Medicine & Prescriptive Analytics Enterprise Data Warehouse

34. © 2014 Health Catalyst www.healthcatalyst.com Master Data Management Master data management is comprised of processes, governance, policies, standards, and tools that consistently define and manage the critical data of an organization to provide a single point of reference. The data that is mastered includes: - Wikipedia – Reference data - the dimensions for analysis – Analytical rules – supports consistent data binding Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. 34

35. © 2014 Health Catalyst www.healthcatalyst.com Data Binding & Data Governance “systolic & diastolic blood pressure” Pieces of meaningless Analytics Software Programming Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. data 115 60 Binds data to Vocabulary Rules “normal” 35

36. © 2014 Health Catalyst www.healthcatalyst.com Why Is This Binding Concept Important? Comprehensive Agreement Persistent Agreement Data Governance needs to look for and facilitate both Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. 36 Knowing when to bind data, and how tightly, to vocabularies and rules is CRITICAL to analytic success and agility Is the rule or vocabulary widely accepted as true and accurate in the organization or industry? Is the rule or vocabulary stable and rarely change?

37. © 2014 Health Catalyst www.healthcatalyst.com Vocabulary: Where Do We Start? Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. Charge code CPT code Date & Time DRG code Drug code Employee ID Employer ID Encounter ID Gender ICD diagnosis code ICD procedure code Department ID Facility ID Lab code Patient type Patient/member ID Payer/carrier ID Postal code Provider ID In today’s environment, about 20 data elements represent 80-90% of analytic use cases. This will grow over time, but right now, it’s fairly simple. Source data vocabulary Z (e.g., EMR) Source data vocabulary Y (e.g., Claims) Source data vocabulary X (e.g., Rx) 37

38. Where Do We Start, Clinically? We see consistent opportunities, across the industry, in the following areas: © 2014 Health Catalyst www.healthcatalyst.com Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. • CAUTI • CLABSI • Pregnancy management, elective induction • Discharge medications adherence for MI/CHF • Prophylactic pre-surgical antibiotics • Materials management, supply chain • Glucose management in the ICU • Knee and hip replacement • Gastroenterology patient management • Spine surgery patient management • Heart failure and ischemic patient management 38

39. © 2014 Health Catalyst www.healthcatalyst.com Start Within Your Scope of Influence We are still learning how to manage outpatient populations Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. 39

40. © 2014 Health Catalyst www.healthcatalyst.com In Conclusion Practice democratic data governance – Find the balance between central and decentralized governance – Federal vs. States’ rights is a good metaphor The Triple Aim of Data Governance – Data Quality, Data Literacy, and Data Exploitation Analytics gives data governance something to govern – Start within your current scope of influence and data, then grow from there Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. 40

41. © 2014 Health Catalyst www.healthcatalyst.com Link to original article for a more in-depth discussion. Demystifying Healthcare Data Governance More about this topic Becoming the Change Agent Your Healthcare System Needs Dr. John Haughom, Senior Advisor 3 Phases of Healthcare Data Governance in Analytics Mike Doyle, Vice President of Sales Data Governance: 7 Essential Practices Dale Sanders, Senior Vice President of Strategy How Accountable Care Organizations Will Drive Demand for Data Analytics Dr. David Burton, Former CEO and Executive Chairman Discovering Patterns in the Data to Improve Patient Care Dr. John Haughom, Senior Advisor Proprietary. Feel free to share but we would appreciate a Health Catalyst citation.

42. – John Haughom, MD, Senior Advisor, Health Catalyst © 2014 Health Catalyst www.healthcatalyst.com For more information: Download Healthcare: A Better Way. The New Era of Opportunity “This is a knowledge source for clinical and operational leaders, as well as front-line caregivers, who are involved in improving processes, reducing harm, designing and implementing new care delivery models, and undertaking the difficult task of leading meaningful change on behalf of the patients they serve.” Proprietary. Feel free to share but we would appreciate a Health Catalyst citation.

43. Other Clinical Quality Improvement Resources Dale Sanders has been one of the most influential leaders in healthcare analytics and data warehousing since his earliest days in the industry, starting at Intermountain Healthcare from 1997-2005, where he was the chief architect for the enterprise data warehouse (EDW) and regional director of medical informatics at LDS Hospital. In 2001, he founded the Healthcare Data Warehousing Association. From 2005-2009, he was the CIO for Northwestern University’s physicians’ group and the chief architect of From 2009-2012, he served as the CIO for the national health system of the Cayman Islands where he helped lead the implementation of new care delivery processes that are now associated with accountable care in the US. Prior to his healthcare experience, Dale had a diverse 14-year career that included duties as a CIO on Looking Glass airborne command posts in the US Air Force; IT support for the Reagan/Gorbachev summits; nuclear threat assessment for the National Security Agency and START Treaty; chief architect for the Intel Corp’s Integrated Logistics Data Warehouse; and co-founder of Information Technology International. As a systems engineer at TRW, Dale and his team developed the largest Oracle data warehouse in the world at that time (1995), using an innovative design principle now known as a late binding architecture. He holds a BS degree in chemistry and minor in biology from Ft. Lewis College, Durango Colorado, and is a graduate of the US Air Force Information Systems Engineering program. © 2014 Health Catalyst www.healthcatalyst.com Click to read additional information at www.healthcatalyst.com the Northwestern Medical EDW. Proprietary. Feel free to share but we would appreciate a Health Catalyst citation.