Demystifying Healthcare Data Governance Dales Sanders May 7

Demystifying Healthcare Data Governance Dales Sanders – May 7, 2014 Creative Commons Copyright © 2014 Health Catalyst www. healthcatalyst. com

® 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 Creative Commons Copyright 2 © 2014 Health Catalyst www. healthcatalyst. com

® A Sampling of My Up & Down Journey TOO MUCH DATA GOVERNANCE (2005) Northwestern EDW (1995) IMDB & PIRS ● ● (1996) Intel Logistics EDW (1987) MMICS 2014 ● ● 1983 ● (1986) WWMCCS ● (1992) NSA Threat Reporting ● (1998) Intermountain Healthcare ● (2009) Cayman Islands HSA 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 TOO LITTLE DATA GOVERNANCE Creative Commons Copyright 3 © 2014 Health Catalyst www. healthcatalyst. com

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. ” Creative Commons Copyright 4 © 2014 Health Catalyst www. healthcatalyst. com

® Data Governance Cultures HIGHLY CENTRALIZED GOVERNMENT BALANCED GOVERNMENT HIGHLY DECENTRALIZED GOVERNMENT AUTHORITARIAN DEMOCRATIC TRIBAL Centralized EDW; monolithic early binding data model Centralized EDW; distributed late binding data model No EDW; multiple, distributed analytic systems Creative Commons Copyright 5 © 2014 Health Catalyst www. healthcatalyst. com

® 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 Creative Commons Copyright 6 © 2014 Health Catalyst www. healthcatalyst. com

® 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 Creative Commons Copyright 7 © 2014 Health Catalyst www. healthcatalyst. com

® 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 Creative Commons Copyright 8 © 2014 Health Catalyst www. healthcatalyst. com

® 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% Creative Commons Copyright 9 © 2014 Health Catalyst www. healthcatalyst. com

® 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% Creative Commons Copyright 10 © 2014 Health Catalyst www. healthcatalyst. com

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 Creative Commons Copyright 11 © 2014 Health Catalyst www. healthcatalyst. com

® Keys to Analytic Success The Data Governance Committee should be a driving force in all three… Mindset – Setting the tone of “data driven” for the culture Skillset – Actively building and recruiting for data literacy among employees Toolset – Choosing the right kind of tools to support analytics and data governance Creative Commons Copyright 12 © 2014 Health Catalyst www. healthcatalyst. com

® The Data Governance Layers Happy Data Analyst Creative Commons Copyright 13 © 2014 Health Catalyst www. healthcatalyst. com

® 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. ” Creative Commons Copyright 14 © 2014 Health Catalyst www. healthcatalyst. com

® 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. ” Creative Commons Copyright 15 © 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. ” Creative Commons Copyright 16 © 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. ” Creative Commons Copyright 17 © 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. ” Creative Commons Copyright 18 © 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. ” Creative Commons Copyright 19 © 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. ” Creative Commons Copyright 20 © 2014 Health Catalyst www. healthcatalyst. com

Who Is On The Data Governance Committee? Chief Analytics Officer CIO ® Representing the analytics customers The data technologist CMO & CNO The clinical data owners CFO The financial and supply chain data owner CRO Representing the researchers’ data needs Creative Commons Copyright 21 © 2014 Health Catalyst www. healthcatalyst. com

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 Creative Commons Copyright 22 © 2014 Health Catalyst www. healthcatalyst. com

® 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% Creative Commons Copyright 23 © 2014 Health Catalyst www. healthcatalyst. com

® 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 Creative Commons Copyright 24 © 2014 Health Catalyst www. healthcatalyst. com

® 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? Creative Commons Copyright 25 © 2014 Health Catalyst www. healthcatalyst. com

The Four Levels of Closed Loop Analytics in Healthcare CDS: EDW: EHR: MTTI: Clinical Information Systems Decisions & Actions Supporting information Clinical Decision Support Enterprise Data Warehouse Electronic Health Record Mean Time To Improvement Executive & Clinical Leadership Set expectations for use of evidence & standards Enterprise Clinical Teams Act on performance information Clinical, EHR, EDW & Analytics Teams Update EHR protocols & EDW metrics Start here Internal Evidence Clinicians’ suggestions Optimal State Clinical Variations & Needs Quality Governance Monitor baselines & clinical processes Select a problem Set outcomes & metrics External Evidence Literature, reports, etc. Clinicians use standard protocols & orders in daily care Clinical, EHR, EDW & Analytics Teams Align metrics & data Update EHR & EDW with new data items if needed & possible EHR & CDS Electronic clinical data Sub-Optimal State Clinicians use diverse protocols & orders in daily care Performance Practice MTTI Protocols Processing EDW Analyzable data Clinical Analytics Analyze data quality & process/outcome variations Generate the internal evidence Other Data Sources Clinical, Financial, etc. Lo Hi Standards © 2014 Denis Protti, Dale Sanders & Corinne Eggert Best Evidence Information that clinicians trust Quality Governance Use comparative data to identify best outcomes Determine standard order sets, protocols & decision support rules External Evidence Literature, reports, etc. 26

® Healthcare Analytics Adoption Model © Sanders, Protti, Burton, 2013 Level 8 Personalized Medicine & Prescriptive Analytics Tailoring patient care based on population outcomes and generic data. Fee-for-quality rewards health maintenance. Level 7 Clinical Risk Intervention & Predictive Analytics Organizational processes for intervention are supported with predictive risk models. Fee-for-quality includes fixed per capita payment. Level 6 Population Health Management & Suggestive Analytics Tailoring patient care based on population metrics. Fee-for -quality includes bundled per case payment. Level 5 Waste & Care Variability Reduction internal optimization and waste reduction. Level 4 Automated External Reporting Efficient, consistent production of reports & adaptability to changing requirements. Level 3 Automated Internal Reporting Efficient, consistent production of reports & widespread availability in the organization. Level 2 Standardized Vocabulary & Patient Registries Relating and organizing the core data content. Level 1 Enterprise Data Warehouse Collecting and integrating the core data content. Level 0 Fragmented Point Solutions Inefficient, inconsistent versions of the truth. Cumbersome internal and external reporting. Reducing variability in care processes. Focusing on Creative Commons Copyright 27 © 2014 Health Catalyst www. healthcatalyst. com

® Progression in the Model The progressive patterns at each level 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 Creative Commons Copyright 28 © 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 Personalized Medicine & Prescriptive Analytics 2 -4 years Phase 6: Acquisition of Data 1 -2 years – 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 1 Enterprise Data Warehouse 3 -12 months Creative Commons Copyright 29 © 2014 Health Catalyst www. healthcatalyst. com

® What Data Are We Governing? Creative Commons Copyright 30 © 2014 Health Catalyst www. healthcatalyst. com

® Master Data Management 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 The data that is mastered includes: Reference data - the dimensions for analysis – Analytical rules – supports consistent data binding – Creative Commons Copyright 31 © 2014 Health Catalyst www. healthcatalyst. com

Data Binding & Data Governance ® Analytics Software Programming Pieces of meaningless data Vocabulary Binds data to 115 60 “systolic & diastolic blood pressure” Rules “normal” Creative Commons Copyright 32 © 2014 Health Catalyst www. healthcatalyst. com

Why Is This Binding Concept Important? ® Knowing when to bind data, and how tightly, to vocabularies and rules is CRITICAL to analytic success and agility Comprehensive Agreement Persistent Agreement 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? Data Governance needs to look for and facilitate both Creative Commons Copyright 33 © 2014 Health Catalyst www. healthcatalyst. com

® Vocabulary: Where Do We Start? Charge code CPT code Date & Time DRG code 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. Drug code Employee ID Employer ID Source data vocabulary Z (e. g. , EMR) Encounter ID Gender ICD diagnosis code ICD procedure code Department ID Facility ID Lab code Patient type Source data vocabulary Y (e. g. , Claims) Source data vocabulary X (e. g. , Rx) Patient/member ID Payer/carrier ID Postal code Provider ID Creative Commons Copyright 34 © 2014 Health Catalyst www. healthcatalyst. com

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 Creative Commons Copyright 35 © 2014 Health Catalyst www. healthcatalyst. com

Start Within Your Scope of Influence We are still learning how to manage outpatient populations 36

® 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 Creative Commons Copyright 37 © 2014 Health Catalyst www. healthcatalyst. com

OBJECTIVE 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. 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. Creative Commons Copyright © 2014 Health Catalyst 38 www. healthcatalyst. com

® Contact Info and Q&A dale. sanders@healthcatalyst. com @drsanders www. linkedin. com/in/dalersanders/ Creative Commons Copyright 39 © 2014 Health Catalyst www. healthcatalyst. com