Creative Commons Copyright February 2014 Comparing Healthcare Data Warehouse Approaches: A Deep-dive Evaluation of the Three Major Methodologies

© 2013 Health Catalyst | 2 A Personal Experience with Healthcare 2 Dear mother… A trip to the doctor…

Healthcare Analytics Goal Why have an EDW? It is a means to a greater end It exists to improve: 1.The effectiveness of care delivery (and safety) 2.The efficiency of care delivery (e.g. workflow) 3.Reduce Mean Time To Improvement (MTTI) 3

Creative Commons Copyright 4 Three Systems of Care Delivery

© 2013 Health Catalyst | Excellent OutcomesPoor Outcomes # of Cases Mean 1 box = 100 cases in a year Excellent Outcomes # of Cases Poor Outcomes Focus On Inliers (Tighten the Curve and Shift It to the Left) Strategy. Identify best practices through research and analytics and develop guidelines and protocols to reduce inlier variation Result. Shifting the cases which lie above the mean (47+%) toward the excellent end of the spectrum produces a much more significant impact than focusing on the adverse outlier tail (2.5%) 5 Population Health Management

Healthcare Analytics Adoption Model Level 8 Personalized Medicine & Prescriptive Analytics Tailoring patient care based on population outcomes and genetic 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 upon population metrics. Fee- for-quality includes bundled per case payment. Level 5 Waste & Care Variability Reduction Reducing variability in care processes. Focusing on 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 WarehouseCollecting and integrating the core data content. Level 0 Fragmented Point Solutions Inefficient, inconsistent versions of the truth. Cumbersome internal and external reporting.

Polling Question What level would you to the healthcare analytic solutions with which you are most familiar? (levels 1 – 8)

© 2013 Health Catalyst | 8 An Analysts Time Understanding the need Hunting for the data Gathering or compiling (including waiting for IT to run report or query) Interpreting data Distribution of data Waste Value-add Analysts or Clinician's Time Too much time spent hunting for and gathering data rather than understanding and interpreting data

© 2013 Health Catalyst | 9 HR – Desired State Authors Drillers Viewers Drillers Authors or Knowledge Workers Ideal User Distribution for Continuous Improvement Authors or knowledge workers are scarce and in high demand – few users have both clinical knowledge AND access to tools and data Large backlogs of analytic/report requests exist since underlying systems are too complex for the average user (users make analytic requests vs. self-service) Create more knowledge workers by doing the following: Expand data access (audit access vs. control access) Simplify data structures (relational vs. dimensional) Continue use of naming standards (intuitive vs. cryptic) Providing better tools (metadata, ad hoc, etc.) Promote shift in culture by rewarding process knowledge discovery rather than punishing outliers Typical User Distribution

© 2013 Health Catalyst | Comparison of prevailing approaches

© 2013 Health Catalyst | Less Transformation Provider Patient Bad Debt DiagnosisProcedure Facility Encounter Cost Charge Employee Survey House Keeping Catha Lab Provider Census Time Keeping More TransformationEnforced Referential Integrity ENTERPRISE DATA MODEL Enterprise Data Model 11 FINANCIAL SOURCES (e.g. EPSi, Lawson, PeopleSoft) ADMINISTRATIVE SOURCES (e.g. API Time Tracking) EMR SOURCE (e.g. Cerner) DEPARTMENTAL SOURCES (e.g. Apollo) PATIENT SATISFACTION SOURCES (e.g. NRC Picker) EDW

© 2013 Health Catalyst | Less Transformation Provider Patient Bad Debt DiagnosisProcedure Facility Encounter Cost Charge Employee Survey House Keeping Catha Lab Provider Census Time Keeping More TransformationEnforced Referential Integrity ENTERPRISE DATA MODEL Enterprise Data Model – Still need Subject Area Marts 12 FINANCIAL SOURCES (e.g. EPSi, Lawson, PeopleSoft) ADMINISTRATIVE SOURCES (e.g. API Time Tracking) EMR SOURCE (e.g. Cerner) DEPARTMENTAL SOURCES (e.g. Apollo) PATIENT SATISFACTION SOURCES (e.g. NRC Picker) EDW Diabetes Sepsis Readmissions

© 2013 Health Catalyst | Bill of Materials Conceptual Model 13 ProductSupplier OrderCustomer Typical Analyses Counts Simple aggregations By various dimensions

© 2013 Health Catalyst | Star Schema Conceptual Model 14 Fact (Transaction) Dimension 1 (Product) Dimension 4 (Location) Dimension 2 (Date) Typical Analyses Transaction counts Simple aggregations By various dimensions Dimension 3 (Purchaser)

© 2013 Health Catalyst | EMR SOURCE (e.g. Cerner) Oncology Diabetes Heart Failure Regulatory PregnancyAsthma Labor Productivity Revenue Cycle Census PATIENT SATISFACTION SOURCES (e.g. NRC Picker) DEPARTMENTAL SOURCES (e.g. Apollo) FINANCIAL SOURCES (e.g. EPSi, Lawson, PeopleSoft) ADMINISTRATIVE SOURCES (e.g. API Time Tracking) Dimensional Data Model Dimensional Data Model Redundant Data Extracts Less TransformationMore Transformation 15 Vertical Summary Data Marts

© 2013 Health Catalyst | Metadata: EDW Atlas Security and Auditing Common, Linkable Vocabulary Financial Source Marts Administrative Source Marts Departmental Source Marts Patient Source Marts EMR Source Marts HR Source Mart Diabetes Sepsis Readmissions Less TransformationMore Transformation FINANCIAL SOURCES (e.g. EPSi, Peoplesoft, Lawson) ADMINISTRATIVE SOURCES (e.g. API Time Tracking) ADMINISTRATIVE SOURCES (e.g. API Time Tracking) EMR SOURCE (e.g. Cerner) EMR SOURCE (e.g. Cerner) DEPARTMENTAL SOURCES (e.g. Apollo) PATIENT SATISFACTION SOURCES (e.g. NRC Picker, Press Ganey) PATIENT SATISFACTION SOURCES (e.g. NRC Picker, Press Ganey) Human Resources (e.g. PeopleSoft) Human Resources (e.g. PeopleSoft) Adaptive Data Warehouse

© 2013 Health Catalyst | Classic Star Schema Deficiencies Resolution of many many-to-many relationships Not as much about counts of transactions More about: Events States of change over time Related states (e.g. co-morbidities, attribution) 17

© 2013 Health Catalyst | Sample Diabetes Registry Data Model 18 Diabetes Patient Typical Analyses How many diabetes patients do I have? When was there last HA1C, LDL, Foot Exam, Eye Exam? What was the value for each instance for the last 2 years? What are all the medications they are on? How long have they been taking each medication? What was done at each of their visits for the last 2 years? Which doctors have seen these patients and why? List of all admissions and reason for admission? What co-morbid conditions do these patient have? Which interventions (diet, exercise, medications) are having the biggest impact on LDL, HA1C scores? Procedure History Vital Signs History Current Lab Result Lab Result History Office Visit Exam Type Exam History Diagnosis History Diagnosis Code Procedure Code Lab Type

© 2013 Health Catalyst | Health Catalyst | 19 Measurement System Exercise Webinar

© 2013 Health Catalyst | The Enterprise Shopping Model Produce Meat Dairy Dry Goods __ Apples __ Pears __ Tomatoes __ Carrots __ Beef __ Ham __ Chicken __ Pork __ Milk __ Eggs __ Cheese __ Cream __ Pasta __ Flour __ Sugar __ Soup __ Celery __ Banana __ Melon __ Grapes __ Turkey __ Sausage __ Lamb __ Bacon __ 2% Milk __ Half & Half __ Yogurt __ Margarine __ Baking soda __ Rice __ Beans __ B. Sugar E n t e r p r i s e S h o p p i n g M o d e l Apples Tomato Soup Flour Milk Turkey Lettuce Sugar Beans Hot dogs Banana Noodles Yogurt Your Shopping List Eggs Flowers Tires Dry cleaning Additional purchases

© 2013 Health Catalyst | Less Transformation Provider Patient Bad Debt DiagnosisProcedure Facility Encounter Cost Charge Employee Survey House Keeping Catha Lab Provider Census Time Keeping More TransformationEnforced Referential Integrity ENTERPRISE DATA MODEL Enterprise Data Model (Technology Vendors) 21 FINANCIAL SOURCES (e.g. EPSi, Lawson, PeopleSoft) ADMINISTRATIVE SOURCES (e.g. API Time Tracking) EMR SOURCE (e.g. Cerner) DEPARTMENTAL SOURCES (e.g. Apollo) PATIENT SATISFACTION SOURCES (e.g. NRC Picker) EDW

© 2013 Health Catalyst | Using a dimensional model in Healthcare is kind of like shopping for data like this … 22

© 2013 Health Catalyst | 23

© 2013 Health Catalyst | The Dimensional Shopping Model 24 DairyDry Goods __ ½ cup of butter __ ½ cup milk __ 2 eggs __ 1 cup white sugar __ 1 ½ cups all-purpose flour __ 2 teaspoons vanilla extract __ 1 ¾ teaspoon baking powder Dimensional Shopping Model - Cake Trip #2 to the Store How many recipes to do you need to make? Trip #1 to the Store Dairy Dry Goods __ 4 eggs __ 2 c shortening __ 1 c sugar __ 2 c brown sugar __ 2 t baking soda __ 2 t vanilla __ 1 t salt __ 4-5 c all-purpose flour __ 4 cups chocolate chips Dimensional Shopping Model - Cookies

© 2013 Health Catalyst | EMR SOURCE (e.g. Cerner) Oncology Diabetes Heart Failure Regulatory PregnancyAsthma Labor Productivity Revenue Cycle Census PATIENT SATISFACTION SOURCES (e.g. NRC Picker) DEPARTMENTAL SOURCES (e.g. Apollo) FINANCIAL SOURCES (e.g. EPSi, Lawson, PeopleSoft) ADMINISTRATIVE SOURCES (e.g. API Time Tracking) Dimensional Data Model Dimensional Data Model Redundant Data Extracts Less TransformationMore Transformation 25 Dimensional Data Model (Healthcare Point Solutions)

© 2013 Health Catalyst | The Adaptive Shopping Model 26 A d a p t i v e S h o p p i n g M o d e l __ ______________ Store: _____________________________ Additional Get eggs Buy flowers Get tires rotated Pick up dry cleaning Buy a Christmas tree Baking Powder Baking Soda Buy a new couch Get oil change Chocolate Chips Buy paint and painting supplies Buy yarn and knitting supplies Vanilla extract Buy a set of pots and pans And Even More Initial List Apples Tomato Soup Flour Milk Turkey Lettuce Sugar Beans Hot dogs Banana Noodles Yogurt

© 2013 Health Catalyst | Shopping List Revisited 27 Additional Get eggs Buy flowers Get tires rotated Pick up dry cleaning Once you are home can you make these recipes? Cake: 1 cup white sugar 1 ½ cups all-purpose flour 2 teaspoons vanilla extract 1 ¾ teaspoon baking powder ½ cup of butter ½ cup milk 2 eggs Cookies: 1 cup (2 sticks) butter, softened 2 large eggs 3/4 cup white sugar 2 1/4 cups all-purpose flour 1 teaspoon vanilla extract 1 teaspoon salt 1 teaspoon baking soda 2 cups chocolate chips Buy a Christmas tree Baking Powder Baking Soda Buy a new couch Get oil change Chocolate Chips Buy paint and painting supplies Buy yarn and knitting supplies Vanilla extract Buy a set of pots and pans And Even More Initial List Apples Tomato Soup Flour Milk Turkey Lettuce Sugar Beans Hot dogs Banana Noodles Yogurt

© 2013 Health Catalyst | Metadata: EDW Atlas Security and Auditing Common, Linkable Vocabulary Financial Source Marts Administrative Source Marts Departmental Source Marts Patient Source Marts EMR Source Marts HR Source Mart Diabetes Sepsis Readmissions Less TransformationMore Transformation FINANCIAL SOURCES (e.g. EPSi, Peoplesoft, Lawson) ADMINISTRATIVE SOURCES (e.g. API Time Tracking) ADMINISTRATIVE SOURCES (e.g. API Time Tracking) EMR SOURCE (e.g. Cerner) EMR SOURCE (e.g. Cerner) DEPARTMENTAL SOURCES (e.g. Apollo) PATIENT SATISFACTION SOURCES (e.g. NRC Picker, Press Ganey) PATIENT SATISFACTION SOURCES (e.g. NRC Picker, Press Ganey) Human Resources (e.g. PeopleSoft) Human Resources (e.g. PeopleSoft) Adaptive Data Warehouse

© 2013 Health Catalyst | Health Catalyst | 29 Late-binding Deeper Dive

© 2013 Health Catalyst | Data Modeling Approaches 30 Early Binding Late Binding Corporate Information Model Popularized by Bill Inmon and Claudia Imhoff Corporate Information Model Popularized by Bill Inmon and Claudia Imhoff I2B2 Popularized by Academic Medicine I2B2 Popularized by Academic Medicine Star Schema Popularized by Ralph Kimball Star Schema Popularized by Ralph Kimball Data Bus Popularized by Dale Sanders Data Bus Popularized by Dale Sanders File Structure Association Popularized by IBM mainframes in 1960s Reappearing in Hadoop & NoSQL File Structure Association Popularized by IBM mainframes in 1960s Reappearing in Hadoop & NoSQL

© 2013 Health Catalyst | Origins of Early vs Late Binding Early days of software engineering Tightly coupled code, early binding of software at compile time Hundreds of thousands of lines of code in one module, thousands of function points Single compile, all functions linked at compile time If one thing breaks, all things break Little or no flexibility and agility of the software to accommodate new use cases 31

© 2013 Health Catalyst | Origins of Early vs Late Binding 1980s: Object Oriented Programming Alan Kay, Universities of Colorado & Utah, Xerox/PARC Small objects of code, reflecting the real world Compiled individually, linked at runtime, only as needed Agility and adaptability to address new use cases Steve Jobs: NeXT Computing Commercial, large-scale adoption of Kays concepts Late binding – or as late as practical – becomes the norm Maybe Jobs largest contribution to computer science 32

© 2013 Health Catalyst | Data Binding in Analytics Atomic data can be bound to business rules about that data and to vocabularies related to that data Vocabulary binding in healthcare –Unique patient and provider identifiers –Standard facility, department, and revenue center codes –Standard definitions for sex, race, ethnicity –ICD, CPT, SNOMED, LOINC, RxNorm, RADLEX, etc. Binding data to business rules –Length of stay –Patient attribution to a provider –Revenue and expense allocation and projections to a department –Data definitions of general disease states and patient registries –Patient exclusion criteria from population management –Patient admission/discharge/transfer rules 33

© 2013 Health Catalyst | Analytic Relations The key is to relate data, not model data 34 High Value Attributes About 20 data attributes account for 90% of healthcare analytic use cases Core Data Elements Charge Code CPT Code Date & Time DRG code Drug code Employee ID Employer ID Encounter ID Sex Diagnosis Code Procedure Code Department ID Facility ID Lab code Patient type Patient / member ID Payer / carrier ID Postal code Provider ID Vocab in Source System 1 Vocab in Source System 2 Vocab in Source System 3 Highest value area for standardizing vocabulary

© 2013 Health Catalyst | Data Analysis Six Points to Bind Data 35 Source Data Content Source System Analytics Customized Data Marts Visualization Others HR Supplies Financial Clinical Academic State Academic State Others HR Supplies Financial Clinical QlikView, Tableau Microsoft Access Web Applications Excel SAS, SPSS et al. Internal External Research Registries Operational Events Clinical Events Compliance Measures Materials Management Disease Registries Business Rule and Vocabulary Binding Points Low volatility = Early bindingHigh volatility = Late binding

© 2013 Health Catalyst | Binding Principles & Strategy 1.Delay Binding as long as possible…until a clear analytic use case requires it 2.Earlier binding is appropriate for business rules or vocabularies that change infrequently or that the organization wants to lock down for consistent analytics 3.Late binding in the visualization layer is appropriate for what if scenario analysis 4.Retain a record of the bindings from the source system in the data warehouse 5.Retain a record of the changes to vocabulary and rules bindings in the data models of the data warehouse 36

© 2013 Health Catalyst | Health Catalyst | 37 Thank you!