Demystifying Healthcare Data Governance

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As the Age of Analytics emerges in healthcare, health system executives are increasingly challenged to define a data governance strategy that maximizes the value of data to the mission of their organizations.

Adding to that challenge, the competitive nature of the data warehouse and analytics market place has resulted in significant noise from vendors and consultants alike who promise to help health systems develop their data governance strategy. Having gone on his own turbulent data governance ride as a CIO in the US Air Force and healthcare, Dale Sanders, Senior Vice President at Health Catalyst will cut through the market noise to cover the following topics:

General concepts of data governance, regardless of industry
Unique aspects of data governance in healthcare
Data governance in a “Late Binding” data warehouse
The layers and roles in data governance
The four “Closed Loops” of healthcare analytics and data governance

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  • Clinical TeamEHR TeamAnalytics TeamPerformance Loop
  • Demystifying Healthcare Data Governance

    1. 1. © 2014 Health Catalyst www.healthcatalyst.com Creative Commons Copyright © 2014 Health Catalyst www.healthcatalyst.comCreative Commons Copyright Dales Sanders – May 7, 2014 Demystifying Healthcare Data Governance
    2. 2. © 2014 Health Catalyst www.healthcatalyst.com Creative Commons Copyright Today’s Agenda  General concepts in data governance  Unique aspects of data governance in healthcare  The layers and roles in data governance  Constant theme: Data governance as it relates to analytics and data warehousing 2
    3. 3. © 2014 Health Catalyst www.healthcatalyst.com Creative Commons Copyright A Sampling of My Up & Down Journey TOO LITTLE DATA GOVERNANCE TOO MUCH 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 (1987) MMICS (1992) NSA Threat Reporting (1995) IMDB & PIRS (1996) Intel Logistics EDW (1998) Intermountain Healthcare (2005) Northwestern EDW (2009) Cayman Islands HSA 1983 2014 3
    4. 4. © 2014 Health Catalyst www.healthcatalyst.com Creative Commons Copyright The Sanders Philosophy of Data Governance The best data governance governs to the least extent necessary to achieve the greatest common good.” Govern no data until its time.” 4
    5. 5. © 2014 Health Catalyst www.healthcatalyst.com Creative Commons Copyright Centralized EDW; monolithic early binding data model Data Governance Cultures HIGHLY CENTRALIZED GOVERNMENT BALANCED GOVERNMENT HIGHLY DECENTRALIZED GOVERNMENT Centralized EDW; distributed late binding data model No EDW; multiple, distributed analytic systems 5
    6. 6. © 2014 Health Catalyst www.healthcatalyst.com Creative Commons Copyright Characteristics of Democracy  Elements of centralized decision making ● Elected or appointed, centralized representatives ● Majority rules  Elements of decentralized action ● Direct voting and participation, locally ● Everyone is expected to participate in developing shared values, rules, and laws; then abide by them and act accordingly 6
    7. 7. © 2014 Health Catalyst www.healthcatalyst.com Creative Commons Copyright What’s It Look Like? Not enough data governance  Completely decentralized, uncoordinated data analysis resources-- human and technology  Inconsistent analytic results from different sources, attempting to answer the same question  Poor data quality, e.g., duplicate patient records rate is > 10% in the master patient index  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 requirements… like accountable care 7
    8. 8. © 2014 Health Catalyst www.healthcatalyst.com Creative Commons Copyright What’s It Look Like? Too much data governance  Unhappy data analysts… and their customers  Everything takes too long – Loading new data – Making changes to data models to support new analytic use cases – Getting access to data – Resolving data quality problems – Developing new reports and analyses 8
    9. 9. © 2014 Health Catalyst www.healthcatalyst.com Creative Commons Copyright Poll Question What best describes the current state of affairs for data governance in your organization? 193 Respondents Authoritarian – 19.7% Democratic – 24.3% Tribal – 56% 9
    10. 10. © 2014 Health Catalyst www.healthcatalyst.com Creative Commons Copyright Poll Question How would you rate data governance effectiveness in your organization? 179 Respondents 5 – Very effective – 1.6% 4 – 7.2% 3 – 22.3% 2 – 44.1% 1 – Ineffective – 24.8% 10
    11. 11. © 2014 Health Catalyst www.healthcatalyst.com Creative Commons Copyright The Triple Aim of Data Governance 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 • Pushing the data-driven agenda for cost reduction, quality improvement, and risk reduction 11
    12. 12. © 2014 Health Catalyst www.healthcatalyst.com Creative Commons Copyright Keys to Analytic Success The Data Governance Committee should be a driving force in all three… – Setting the tone of “data driven” for the culture – Actively building and recruiting for data literacy among employees – Choosing the right kind of tools to support analytics and data governance Mindset Skillset Toolset 12
    13. 13. © 2014 Health Catalyst www.healthcatalyst.com Creative Commons Copyright The Data Governance Layers Happy Data Analyst 13
    14. 14. © 2014 Health Catalyst www.healthcatalyst.com Creative Commons Copyright The Different Roles in Each Layer Executive & Board Leadership 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.” 14
    15. 15. © 2014 Health Catalyst www.healthcatalyst.com Creative Commons Copyright The Different Roles in Each Layer Data Governance Committee 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.” 15
    16. 16. © 2014 Health Catalyst www.healthcatalyst.com Creative Commons Copyright 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.” 16
    17. 17. © 2014 Health Catalyst www.healthcatalyst.com Creative Commons Copyright 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.” 17
    18. 18. © 2014 Health Catalyst www.healthcatalyst.com Creative Commons Copyright 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.” 18
    19. 19. © 2014 Health Catalyst www.healthcatalyst.com Creative Commons Copyright 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.” 19
    20. 20. © 2014 Health Catalyst www.healthcatalyst.com Creative Commons Copyright 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.” 20
    21. 21. © 2014 Health Catalyst www.healthcatalyst.com Creative Commons Copyright Who Is On The Data Governance Committee? Representing the analytics customers The data technologist The clinical data owners The financial and supply chain data owner Representing the researchers’ data needs Chief Analytics Officer CIO CMO & CNO CFO CRO 21
    22. 22. © 2014 Health Catalyst www.healthcatalyst.com Creative Commons Copyright Data Governance Committee Failure Modes Wandering: Lacking direction and experience ● “We know we need data governance, but we don’t know how to go about it.” Technical Overkill: An overly passionate and inexperienced IT person leads the data governance committee ● Can’t see the forest for the trees ● For example, Executives on the Data Governance Committee (DGC) are asked to define naming conventions and data types for a database column Politics: Members of the DGC are passive aggressive, narrowly motivated, data poseurs ● They pretend to be data driven and selfless, but they aren’t ● Territorial and defensive about “their” data ● “That person isn’t smart enough to use my data properly.” Red Tape: Committee members are not governors of the data, they are bureaucrats ● Red tape processes for accessing data ● Confuse data governance with data security 22
    23. 23. © 2014 Health Catalyst www.healthcatalyst.com Creative Commons Copyright Poll Question Your organization’s biggest risks to the success of the Data Governance Committee 182 Respondents – Multiple Choice Wandering – 52% Politics – 61% Technical Overkill – 20% Red Tape – 36% Other – 16% 23
    24. 24. © 2014 Health Catalyst www.healthcatalyst.com Creative Commons Copyright Data Governance & Data Security  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 24
    25. 25. © 2014 Health Catalyst www.healthcatalyst.com Creative Commons Copyright Tools for Data Governance Data quality reports – Data Quality = Validity x Completeness CRM tools for the data warehouse – Who’s using what data? When? Why? “White Space” data management tools – For capturing and filling-in computable data that’s missing in the source systems Metadata repository – What’s in the data warehouse? – Are there any data quality problems? – Who’s the data steward? – How much data is available and over what period of time? – What’s the source of the data? 25
    26. 26. Practice Protocols Processing EDW Analyzable data Clinicians use diverse protocols & orders in daily care Sub-Optimal State The Four Levels of Closed Loop Analytics in Healthcare © 2014 Denis Protti, Dale Sanders & Corinne Eggert CDS: EDW: EHR: MTTI: Clinical Decision Support Enterprise Data Warehouse Electronic Health Record Mean Time To Improvement Clinical Information Systems Decisions & Actions Supporting information Clinical, EHR, EDW & Analytics Teams Align metrics & data Update EHR & EDW with new data items if needed & possible Start here Monitor baselines & clinical processes Select a problem Set outcomes & metrics Quality Governance Clinical Variations & Needs Internal Evidence Clinicians’ suggestions External Evidence Literature, reports, etc. Quality Governance Use comparative data to identify best outcomes Determine standard order sets, protocols & decision support rules External Evidence Literature, reports, etc. Analyze data quality & process/outcome variations Generate the internal evidence Clinical Analytics Other Data Sources Clinical, Financial, etc. MTTI EHR & CDS Electronic clinical data Clinicians use standard protocols & orders in daily care Optimal State Clinical, EHR, EDW & Analytics Teams Update EHR protocols & EDW metrics Enterprise Clinical Teams Act on performance information Executive & Clinical Leadership Set expectations for use of evidence & standards Best Evidence Information that clinicians trust Standards  Performance 26
    27. 27. © 2014 Health Catalyst www.healthcatalyst.com Creative Commons Copyright Healthcare Analytics Adoption Model 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 27
    28. 28. © 2014 Health Catalyst www.healthcatalyst.com Creative Commons Copyright Progression in the Model Data content expands – 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” The complexity of data binding and algorithms increases – 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 The progressive patterns at each level 28
    29. 29. © 2014 Health Catalyst www.healthcatalyst.com Creative Commons Copyright Six Phases of Data Governance You need to move through these phases in no more than two years 29 3-12 months 1-2 years 2-4 years – 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” Level 8 Level 1 Personalized Medicine & Prescriptive Analytics Enterprise Data Warehouse
    30. 30. © 2014 Health Catalyst www.healthcatalyst.com Creative Commons Copyright What Data Are We Governing? 30
    31. 31. © 2014 Health Catalyst www.healthcatalyst.com Creative Commons Copyright Master Data Management The data that is mastered includes: – Reference data - the dimensions for analysis – Analytical rules – supports consistent data binding Comprises the processes, governance, policies, standards and tools that consistently define and manage the critical data of an organization to provide a single point of reference.” - Wikipedia 31
    32. 32. © 2014 Health Catalyst www.healthcatalyst.com Creative Commons Copyright Data Binding & Data Governance “systolic & diastolic blood pressure” Pieces of meaningless data 115 60 Binds data to Analytics Software Programming Vocabulary “normal” Rules 32
    33. 33. © 2014 Health Catalyst www.healthcatalyst.com Creative Commons Copyright Why Is This Binding Concept Important? Data Governance needs to look for and facilitate both 33 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? Comprehensive Agreement Is the rule or vocabulary stable and rarely change? Persistent Agreement
    34. 34. © 2014 Health Catalyst www.healthcatalyst.com Creative Commons Copyright Vocabulary: Where Do We Start?  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) 34
    35. 35. © 2014 Health Catalyst www.healthcatalyst.com Creative Commons Copyright Where Do We Start, Clinically? We see consistent opportunities, across the industry, in the following areas: • 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 35
    36. 36. Start Within Your Scope of Influence We are still learning how to manage outpatient populations 36
    37. 37. © 2014 Health Catalyst www.healthcatalyst.com Creative Commons Copyright 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 37
    38. 38. © 2014 Health Catalyst www.healthcatalyst.com Creative Commons Copyright 38 Obtain unbiased, practical, educational advice on proven analytics solutions that really work in healthcare. The future of healthcare requires transformative thinking by committed leadership willing to forge and adopt new data-driven processes. If you count yourself among this group, then HAS ’14 is for you. OBJECTIVE MOBILE APP Access to a mobile app that can be used for audience response and participation in real time. Group-wide and individual analytic insights will be shared throughout the summit, resulting in a more substantive, engaging experience while demonstrating the power of analytics.
    39. 39. © 2014 Health Catalyst www.healthcatalyst.com Creative Commons Copyright Contact Info and Q&A dale.sanders@healthcatalyst.com @drsanders www.linkedin.com/in/dalersanders/ 39

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