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

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)


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


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


Provider

Patient

Bad Debt

Diagnosis Procedure

Facility

EncounterCost

Charge

Employee

Survey

House Keeping

Catha Lab

Provider

Census

Time Keeping



Enterprise Data Model – Still need Subject Area Marts

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PeopleSoft)






(e.g. NRC Picker)

EDW

Diabetes

Sepsis

Readmissions


Bill of Materials Conceptual Model

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Product Supplier

Order Customer

Typical Analyses• Counts• Simple aggregations• By various dimensions


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)



Oncology

DiabetesHeart

Failure

Regulatory

Pregnancy Asthma

Labor Productivity

Revenue Cycle

CensusPATIENT SATISFACTION

SOURCES(e.g. NRC Picker)



PeopleSoft)



Dimensional Data Model

Redundant Data

Extracts

Less TransformationMore Transformation15

Vertical Summary Data Marts


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)






(e.g. NRC Picker, Press Ganey)

Human Resources(e.g. PeopleSoft)

Adaptive Data Warehouse


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


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


Provider

Patient

Bad Debt

Diagnosis Procedure

Facility

EncounterCost

Charge

Employee

Survey

House Keeping

Catha Lab

Provider

Census

Time Keeping



Enterprise Data Model (Technology Vendors)

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PeopleSoft)






(e.g. NRC Picker)

EDW


Using a dimensional model in Healthcareis kind of like shopping for data like this …



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



Oncology

DiabetesHeart

Failure

Regulatory

Pregnancy Asthma

Labor Productivity

Revenue Cycle

CensusPATIENT SATISFACTION

SOURCES(e.g. NRC Picker)



PeopleSoft)



Dimensional Data Model

Redundant Data

Extracts

Less TransformationMore Transformation25

Dimensional Data Model (Healthcare Point Solutions)


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


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


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)




(e.g. NRC Picker, Press Ganey)

Human Resources(e.g. PeopleSoft)

Adaptive Data Warehouse


Late-binding Deeper Dive


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


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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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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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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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



Highest value area for standardizing vocabulary


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


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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Thank you!

Documents

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