Post on 29-Mar-2015
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Creative Commons Copyright
February 2014
Comparing Healthcare Data Warehouse Approaches:
A Deep-dive Evaluation of the Three Major Methodologies
© 2013 Health Catalyst | www.healthcatalyst.com2
A Personal Experience with Healthcare
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• 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)
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Creative Commons Copyright4
Three Systems of Care Delivery
Analytic System
Content System
Deployment System
© 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%)
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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 ReductionReducing variability in care processes. Focusing on internal optimization and waste reduction.
Level 4 Automated External ReportingEfficient, consistent production of reports & adaptability to changing requirements.
Level 3 Automated Internal ReportingEfficient, 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 SolutionsInefficient, 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)
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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
© 2013 Health Catalyst | www.healthcatalyst.com9
HR – Desired State
Authors
Drillers
Viewers
Viewers
Drillers
Authors or Knowledge
WorkersIdeal 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
TypicalUser
Distribution
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Comparison of prevailing approaches
© 2013 Health Catalyst | www.healthcatalyst.comLess Transformation
Provider
Patient
Bad Debt
Diagnosis Procedure
Facility
EncounterCost
Charge
Employee
Survey
House Keeping
Catha Lab
Provider
Census
Time Keeping
More Transformation Enforced Referential Integrity
ENTERPRISE DATA MODEL
Enterprise Data Model
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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 SATISFACTIONSOURCES
(e.g. NRC Picker)
EDW
© 2013 Health Catalyst | www.healthcatalyst.comLess Transformation
Provider
Patient
Bad Debt
Diagnosis Procedure
Facility
EncounterCost
Charge
Employee
Survey
House Keeping
Catha Lab
Provider
Census
Time Keeping
More Transformation Enforced Referential Integrity
ENTERPRISE DATA MODEL
Enterprise Data Model – Still need Subject Area Marts
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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 SATISFACTIONSOURCES
(e.g. NRC Picker)
EDW
Diabetes
Sepsis
Readmissions
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Bill of Materials Conceptual Model
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Product Supplier
Order Customer
Typical Analyses• Counts• Simple aggregations• By various dimensions
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Star Schema Conceptual Model
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Fact(Transaction)
Dimension 1(Product)
Dimension 4(Location)
Dimension 2(Date)
Typical Analyses• Transaction counts• Simple aggregations• By various dimensions
Dimension 3(Purchaser)
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EMR SOURCE (e.g. Cerner)
Oncology
DiabetesHeart
Failure
Regulatory
Pregnancy Asthma
Labor Productivity
Revenue Cycle
CensusPATIENT 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
Redundant Data
Extracts
Less TransformationMore Transformation15
Vertical Summary Data Marts
© 2013 Health Catalyst | www.healthcatalyst.com
Metadata: EDW Atlas Security and Auditing
Common, Linkable Vocabulary
FinancialSource Marts
AdministrativeSource Marts
DepartmentalSource Marts
PatientSource Marts
EMR Source Marts
HRSource Mart
Diabetes
Sepsis
Readmissions
Less TransformationMore Transformation
FINANCIAL SOURCES (e.g. EPSi, Peoplesoft,
Lawson)
ADMINISTRATIVE SOURCES
(e.g. API Time Tracking)
EMR SOURCE (e.g. Cerner)
DEPARTMENTAL SOURCES (e.g. Apollo)
PATIENT SATISFACTIONSOURCES
(e.g. NRC Picker, Press Ganey)
Human Resources(e.g. PeopleSoft)
Adaptive Data Warehouse
© 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)
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Sample Diabetes Registry Data Model
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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 | www.healthcatalyst.com© 2012 Health Catalyst | www.healthcatalyst.com19
Measurement System ExerciseWebinar
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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 lApples
Tomato Soup
Flour
Milk
Turkey
Lettuce
Sugar
Beans
Hot dogs
Banana
Noodles
Yogurt
Your Shopping List
EggsFlowersTiresDry cleaning
Additional purchases
© 2013 Health Catalyst | www.healthcatalyst.comLess Transformation
Provider
Patient
Bad Debt
Diagnosis Procedure
Facility
EncounterCost
Charge
Employee
Survey
House Keeping
Catha Lab
Provider
Census
Time Keeping
More Transformation Enforced Referential Integrity
ENTERPRISE DATA MODEL
Enterprise Data Model (Technology Vendors)
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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 SATISFACTIONSOURCES
(e.g. NRC Picker)
EDW
© 2013 Health Catalyst | www.healthcatalyst.com22
Using a dimensional model in Healthcareis kind of like shopping for data like this …
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The Dimensional Shopping Model
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
© 2013 Health Catalyst | www.healthcatalyst.com
EMR SOURCE (e.g. Cerner)
Oncology
DiabetesHeart
Failure
Regulatory
Pregnancy Asthma
Labor Productivity
Revenue Cycle
CensusPATIENT 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
Redundant Data
Extracts
Less TransformationMore Transformation25
Dimensional Data Model (Healthcare Point Solutions)
© 2013 Health Catalyst | www.healthcatalyst.com26
The Adaptive Shopping Model
A d a p t i v e S h o p p i n g M o d e l
__ ________________ ________________ ________________ ________________ ________________ ________________ ________________ ______________
__ ________________ ________________ ________________ ________________ ________________ ________________ ________________ ______________
Store: _____________________________
Additional Get eggsBuy flowersGet tires rotatedPick 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 | www.healthcatalyst.com27
Shopping List Revisited
Additional Get eggsBuy flowersGet tires rotatedPick 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 | www.healthcatalyst.com
Metadata: EDW Atlas Security and Auditing
Common, Linkable Vocabulary
FinancialSource Marts
AdministrativeSource Marts
DepartmentalSource Marts
PatientSource Marts
EMR Source Marts
HRSource Mart
Diabetes
Sepsis
Readmissions
Less TransformationMore Transformation
FINANCIAL SOURCES (e.g. EPSi, Peoplesoft, Lawson)
ADMINISTRATIVE SOURCES(e.g. API Time Tracking)
EMR SOURCE (e.g. Cerner)
DEPARTMENTAL SOURCES (e.g. Apollo)
PATIENT SATISFACTIONSOURCES
(e.g. NRC Picker, Press Ganey)
Human Resources(e.g. PeopleSoft)
Adaptive Data Warehouse
© 2013 Health Catalyst | www.healthcatalyst.com© 2012 Health Catalyst | www.healthcatalyst.com29
Late-binding Deeper Dive
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Data Modeling Approaches
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Early Binding
Late Binding
Corporate Information ModelPopularized by Bill Inmon and Claudia Imhoff
I2B2Popularized by Academic Medicine
Star SchemaPopularized by Ralph Kimball
Data BusPopularized by Dale Sanders
File Structure AssociationPopularized by IBM mainframes in 1960sReappearing in Hadoop & NoSQL
© 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
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© 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
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© 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
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© 2013 Health Catalyst | www.healthcatalyst.com
Analytic RelationsThe key is to relate data, not model data
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High Value AttributesAbout 20 data attributes account for
90% of healthcare analytic use cases
Core Data Elements
Charge CodeCPT CodeDate & TimeDRG codeDrug codeEmployee IDEmployer IDEncounter IDSexDiagnosis CodeProcedure CodeDepartment IDFacility IDLab codePatient typePatient / member IDPayer / carrier IDPostal codeProvider ID
Vocab inSourceSystem 1
Vocab inSourceSystem 2
Vocab inSourceSystem 3
Highest value area for standardizing vocabulary
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Data Analysis
Six Points to Bind Data
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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.
Inte
rnal
Ext
erna
l
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
© 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
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© 2013 Health Catalyst | www.healthcatalyst.com© 2012 Health Catalyst | www.healthcatalyst.com37
Thank you!