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What is the best Healthcare Data Warehouse Model for Your Organization?

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Join Steve Barlow as he addresses the strengths and weaknesses of each of the following three primary Data Model approaches for data warehousing in healthcare:

1. Enterprise Data Model
2. Independent Data Marts
3. Late-binding Solutions

Published in: Health & Medicine

What is the best Healthcare Data Warehouse Model for Your Organization?

  1. 1. Creative Commons Copyright February 2014 Comparing Healthcare Data Warehouse Approaches: A Deep-dive Evaluation of the Three Major Methodologies
  2. 2. © 2013 Health Catalyst | www.healthcatalyst.com2 A Personal Experience with Healthcare 2 • Dear mother… • A trip to the doctor…
  3. 3. 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
  4. 4. Creative Commons Copyright 4 Three Systems of Care Delivery
  5. 5. © 2013 Health Catalyst | www.healthcatalyst.com Excellent Outcomes Poor 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
  6. 6. Healthcare Analytics Adoption Model
  7. 7. Polling Question What level would you to the healthcare analytic solutions with which you are most familiar? (levels 1 – 8)
  8. 8. © 2013 Health Catalyst | www.healthcatalyst.com8 An Analyst’s 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 Analyst’s or Clinician's Time Too much time spent hunting for and gathering data rather than understanding and interpreting data
  9. 9. © 2013 Health Catalyst | www.healthcatalyst.com9 HR – Desired State Authors Drillers Viewers 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
  10. 10. © 2013 Health Catalyst | www.healthcatalyst.com© 2013 Health Catalyst | www.healthcatalyst.com Comparison of prevailing approaches
  11. 11. © 2013 Health Catalyst | www.healthcatalyst.comLess Transformation ProviderProvider PatientPatient Bad DebtBad Debt DiagnosisDiagnosis ProcedureProcedure FacilityFacility EncounterEncounterCostCost ChargeCharge EmployeeEmployee SurveySurvey House Keeping House Keeping Catha LabCatha Lab ProviderProvider CensusCensus Time Keeping Time Keeping More Transformation Enforced Referential Integrity ENTERPRISE DATA MODEL Enterprise Data Model 11 FINANCIAL SOURCES (e.g. EPSi, Lawson, PeopleSoft) FINANCIAL SOURCES (e.g. EPSi, Lawson, PeopleSoft) 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) DEPARTMENTAL SOURCES (e.g. Apollo) PATIENT SATISFACTION SOURCES (e.g. NRC Picker) PATIENT SATISFACTION SOURCES (e.g. NRC Picker) EDW
  12. 12. © 2013 Health Catalyst | www.healthcatalyst.comLess Transformation ProviderProvider PatientPatient Bad DebtBad Debt DiagnosisDiagnosis ProcedureProcedure FacilityFacility EncounterEncounterCostCost ChargeCharge EmployeeEmployee SurveySurvey House Keeping House Keeping Catha LabCatha Lab ProviderProvider CensusCensus Time Keeping Time Keeping More Transformation Enforced Referential Integrity ENTERPRISE DATA MODEL Enterprise Data Model – Still need Subject Area Marts 12 FINANCIAL SOURCES (e.g. EPSi, Lawson, PeopleSoft) FINANCIAL SOURCES (e.g. EPSi, Lawson, PeopleSoft) 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) DEPARTMENTAL SOURCES (e.g. Apollo) PATIENT SATISFACTION SOURCES (e.g. NRC Picker) PATIENT SATISFACTION SOURCES (e.g. NRC Picker) EDW DiabetesDiabetes SepsisSepsis ReadmissionsReadmissions
  13. 13. © 2013 Health Catalyst | www.healthcatalyst.com Bill of Materials Conceptual Model 13 Product Supplier Order Customer Typical Analyses •Counts •Simple aggregations •By various dimensions
  14. 14. © 2013 Health Catalyst | www.healthcatalyst.com 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)
  15. 15. © 2013 Health Catalyst | www.healthcatalyst.com EMR SOURCE (e.g. Cerner) EMR SOURCE (e.g. Cerner) OncologyOncology DiabetesDiabetesHeart Failure Heart Failure RegulatoryRegulatory PregnancyPregnancy AsthmaAsthma Labor Productivity Labor Productivity Revenue CycleRevenue Cycle CensusCensus PATIENT SATISFACTION SOURCES (e.g. NRC Picker) PATIENT SATISFACTION SOURCES (e.g. NRC Picker) DEPARTMENTAL SOURCES (e.g. Apollo) DEPARTMENTAL SOURCES (e.g. Apollo) FINANCIAL SOURCES (e.g. EPSi, Lawson, PeopleSoft) FINANCIAL SOURCES (e.g. EPSi, Lawson, PeopleSoft) ADMINISTRATIVE SOURCES (e.g. API Time Tracking) ADMINISTRATIVE SOURCES (e.g. API Time Tracking) Dimensional Data Model Redundant Data Extracts Less TransformationMore Transformation 15 Vertical Summary Data Marts
  16. 16. © 2013 Health Catalyst | www.healthcatalyst.com Metadata: EDW Atlas Security and Auditing Common, Linkable Vocabulary Financial Source Marts Financial Source Marts Administrative Source Marts Administrative Source Marts Departmental Source Marts Departmental Source Marts Patient Source Marts Patient Source Marts EMR Source Marts EMR Source Marts HR Source Mart HR Source Mart DiabetesDiabetes SepsisSepsis ReadmissionsReadmissions Less TransformationMore Transformation FINANCIAL SOURCES (e.g. EPSi, Peoplesoft, Lawson) 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) 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
  17. 17. © 2013 Health Catalyst | www.healthcatalyst.com 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
  18. 18. © 2013 Health Catalyst | www.healthcatalyst.com 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
  19. 19. © 2013 Health Catalyst | www.healthcatalyst.com© 2012 Health Catalyst | www.healthcatalyst.com19 Measurement System Exercise Webinar
  20. 20. © 2013 Health Catalyst | www.healthcatalyst.com 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 Your Shopping List Eggs Flowers Tires Dry cleaning Additional purchases
  21. 21. © 2013 Health Catalyst | www.healthcatalyst.comLess Transformation ProviderProvider PatientPatient Bad DebtBad Debt DiagnosisDiagnosis ProcedureProcedure FacilityFacility EncounterEncounterCostCost ChargeCharge EmployeeEmployee SurveySurvey House Keeping House Keeping Catha LabCatha Lab ProviderProvider CensusCensus Time Keeping Time Keeping More Transformation Enforced Referential Integrity ENTERPRISE DATA MODEL Enterprise Data Model (Technology Vendors) 21 FINANCIAL SOURCES (e.g. EPSi, Lawson, PeopleSoft) FINANCIAL SOURCES (e.g. EPSi, Lawson, PeopleSoft) 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) DEPARTMENTAL SOURCES (e.g. Apollo) PATIENT SATISFACTION SOURCES (e.g. NRC Picker) PATIENT SATISFACTION SOURCES (e.g. NRC Picker) EDW
  22. 22. © 2013 Health Catalyst | www.healthcatalyst.com Using a dimensional model in Healthcare is kind of like shopping for data like this … 22
  23. 23. © 2013 Health Catalyst | www.healthcatalyst.com23
  24. 24. © 2013 Health Catalyst | www.healthcatalyst.com The Dimensional Shopping Model 24 Dairy Dry 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
  25. 25. © 2013 Health Catalyst | www.healthcatalyst.com EMR SOURCE (e.g. Cerner) EMR SOURCE (e.g. Cerner) OncologyOncology DiabetesDiabetesHeart Failure Heart Failure RegulatoryRegulatory PregnancyPregnancy AsthmaAsthma Labor Productivity Labor Productivity Revenue CycleRevenue Cycle CensusCensus PATIENT SATISFACTION SOURCES (e.g. NRC Picker) PATIENT SATISFACTION SOURCES (e.g. NRC Picker) DEPARTMENTAL SOURCES (e.g. Apollo) DEPARTMENTAL SOURCES (e.g. Apollo) FINANCIAL SOURCES (e.g. EPSi, Lawson, PeopleSoft) FINANCIAL SOURCES (e.g. EPSi, Lawson, PeopleSoft) ADMINISTRATIVE SOURCES (e.g. API Time Tracking) ADMINISTRATIVE SOURCES (e.g. API Time Tracking) Dimensional Data Model Redundant Data Extracts Less TransformationMore Transformation 25 Dimensional Data Model (Healthcare Point Solutions)
  26. 26. © 2013 Health Catalyst | www.healthcatalyst.com 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
  27. 27. © 2013 Health Catalyst | www.healthcatalyst.com 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
  28. 28. © 2013 Health Catalyst | www.healthcatalyst.com Metadata: EDW Atlas Security and Auditing Common, Linkable Vocabulary Financial Source Marts Financial Source Marts Administrative Source Marts Administrative Source Marts Departmental Source Marts Departmental Source Marts Patient Source Marts Patient Source Marts EMR Source Marts EMR Source Marts HR Source Mart HR Source Mart DiabetesDiabetes SepsisSepsis ReadmissionsReadmissions Less TransformationMore Transformation FINANCIAL SOURCES (e.g. EPSi, Peoplesoft, Lawson) 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) 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
  29. 29. © 2013 Health Catalyst | www.healthcatalyst.com© 2012 Health Catalyst | www.healthcatalyst.com29 Late-binding Deeper Dive
  30. 30. © 2013 Health Catalyst | www.healthcatalyst.com 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
  31. 31. © 2013 Health Catalyst | www.healthcatalyst.com 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
  32. 32. © 2013 Health Catalyst | www.healthcatalyst.com 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 Kay’s concepts ● Late binding – or as late as practical – becomes the norm ● Maybe Jobs’ largest contribution to computer science 32
  33. 33. © 2013 Health Catalyst | www.healthcatalyst.com 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
  34. 34. © 2013 Health Catalyst | www.healthcatalyst.com 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
  35. 35. © 2013 Health Catalyst | www.healthcatalyst.com 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. InternalExternal 1 2 3 4 5 6 Research Registries Operational Events Clinical Events Compliance Measures Materials Management Disease Registries Business Rule and Vocabulary Binding Points Low volatility = Early binding High volatility = Late binding
  36. 36. © 2013 Health Catalyst | www.healthcatalyst.com 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
  37. 37. © 2013 Health Catalyst | www.healthcatalyst.com© 2012 Health Catalyst | www.healthcatalyst.com37 Thank you!
  38. 38. © 2013 Health Catalyst | www.healthcatalyst.com Questions 38 • To dive in deeper, click on links below: EDW Platform Population Health Healthcare Analytics Adoption Model Understanding Binding • Contact us to learn more about our solutions www.healthcatalyst.com/company/contact-us

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