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Comparing Healthcare Data Warehouse Approaches: A Deep-dive Evaluation of the Three Major Methodologies. February 2014. A Personal Experience with Healthcare. Dear mother… A trip to the doctor…. Healthcare Analytics Goal. Why have an EDW? It is a means to a greater end

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slide1

Comparing Healthcare Data Warehouse Approaches:A Deep-dive Evaluation of the Three Major Methodologies

February 2014

a personal experience with healthcare
A Personal Experience with Healthcare

Dear mother…

A trip to the doctor…

healthcare analytics goal
Healthcare Analytics Goal

Why have an EDW?

  • It is a means to a greater end
  • It exists to improve:
    • The effectiveness of care delivery (and safety)
    • The efficiency of care delivery (e.g. workflow)
    • Reduce Mean Time To Improvement (MTTI)
population health management
Population Health Management

Mean

1 box = 100 cases in a year

# of Cases

# of Cases

Poor Outcomes

Excellent Outcomes

Excellent Outcomes

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

polling question
Polling Question
  • What level would you to the healthcare analytic solutions with which you are most familiar?
  • (levels 1 – 8)
an analyst s time
An Analyst’s Time

Too much time spent hunting for and gathering data rather than understanding and interpreting data

Analyst’s or Clinician's Time

Understanding the need

Hunting for the data

Waste

Gathering or compiling

(including waiting for IT to run report or query)

Value-add

Interpreting data

Distribution of data

hr desired state

Authors

HR – Desired State
  • Authors or knowledge workers are scarce and in high demand – few users have bothclinical 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)

Drillers

Typical

User

Distribution

Viewers

Authors or Knowledge Workers

  • 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

Ideal User

Distribution for Continuous Improvement

Drillers

Viewers

enterprise data model
Enterprise Data Model

EDW

FINANCIAL SOURCES (e.g. EPSi, Lawson, PeopleSoft)

DEPARTMENTAL SOURCES (e.g. Apollo)

Patient

Bad Debt

Provider

Provider

Encounter

Survey

ADMINISTRATIVE SOURCES

(e.g. API Time Tracking)

Cost

ENTERPRISE DATA MODEL

Charge

Census

Facility

House Keeping

Diagnosis

Procedure

Employee

EMR SOURCE

(e.g. Cerner)

PATIENT SATISFACTION

SOURCES

(e.g. NRC Picker)

Catha Lab

Time Keeping

More Transformation

Less Transformation

Enforced Referential Integrity

11

enterprise data model still need subject area marts
Enterprise Data Model – Still need Subject Area Marts

EDW

FINANCIAL SOURCES (e.g. EPSi, Lawson, PeopleSoft)

DEPARTMENTAL SOURCES (e.g. Apollo)

Patient

Bad Debt

Provider

Provider

Encounter

Readmissions

Survey

ADMINISTRATIVE SOURCES

(e.g. API Time Tracking)

Cost

ENTERPRISE DATA MODEL

Diabetes

Charge

Census

Facility

Sepsis

House Keeping

Diagnosis

Procedure

Employee

EMR SOURCE

(e.g. Cerner)

PATIENT SATISFACTION

SOURCES

(e.g. NRC Picker)

Catha Lab

Time Keeping

More Transformation

Less Transformation

Enforced Referential Integrity

12

bill of materials conceptual model
Bill of Materials Conceptual Model
  • Typical Analyses
  • Counts
  • Simple aggregations
  • By various dimensions

Product

Supplier

Order

Customer

star schema conceptual model
Star Schema Conceptual Model

Dimension 1

(Product)

Dimension 4

(Location)

Fact

(Transaction)

  • Typical Analyses
  • Transaction counts
  • Simple aggregations
  • By various dimensions

Dimension 3

(Purchaser)

Dimension 2

(Date)

slide15

Vertical Summary Data Marts

Redundant

Data

Extracts

FINANCIAL SOURCES (e.g. EPSi, Lawson, PeopleSoft)

DEPARTMENTAL SOURCES (e.g. Apollo)

Regulatory

Labor Productivity

Revenue Cycle

Dimensional Data

Model

ADMINISTRATIVE SOURCES

(e.g. API Time Tracking)

Pregnancy

Oncology

Asthma

Heart Failure

Diabetes

Census

EMR SOURCE

(e.g. Cerner)

PATIENT SATISFACTION

SOURCES

(e.g. NRC Picker)

More Transformation

Less Transformation

15

slide16

Adaptive Data Warehouse

Metadata: EDW AtlasSecurity and Auditing

FINANCIAL SOURCES (e.g. EPSi, Peoplesoft, Lawson)

DEPARTMENTAL SOURCES (e.g. Apollo)

Common, Linkable Vocabulary

FinancialSource Marts

DepartmentalSource Marts

Readmissions

AdministrativeSource Marts

PatientSource Marts

PATIENT SATISFACTION

SOURCES

(e.g. NRC Picker, Press Ganey)

ADMINISTRATIVE SOURCES

(e.g. API Time Tracking)

Diabetes

Sepsis

EMR Source Marts

HRSource Mart

EMR SOURCE

(e.g. Cerner)

Human Resources

(e.g. PeopleSoft)

More Transformation

Less Transformation

classic star schema deficiencies
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)
sample diabetes registry data model
Sample Diabetes Registry Data Model

Procedure Code

Diagnosis Code

  • 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?

Diagnosis History

Procedure History

Office Visit

Diabetes Patient

Vital Signs History

Exam History

Current Lab Result

Lab Result History

Exam Type

Lab Type

the enterprise shopping model
The Enterprise Shopping Model

Your Shopping List

Apples

Tomato Soup

Flour

Milk

Turkey

Lettuce

Sugar

Beans

Hot dogs

Banana

Noodles

Yogurt

Additional purchases

E n t e r p r i s e S h o p p i n g M o d e l

Produce

Dairy

Eggs

Flowers

Tires

Dry cleaning

__ Milk

__ Eggs

__ Cheese

__ Cream

__ 2% Milk

__ Half & Half

__ Yogurt

__ Margarine

__ Apples

__ Pears

__ Tomatoes

__ Carrots

__ Celery

__ Banana

__ Melon

__ Grapes

Meat

DryGoods

__ Turkey

__ Sausage

__ Lamb

__ Bacon

__ Beef

__ Ham

__ Chicken

__ Pork

__ Bakingsoda

__ Rice

__ Beans

__ B. Sugar

__ Pasta

__ Flour

__ Sugar

__ Soup

enterprise data model technology vendors
Enterprise Data Model (Technology Vendors)

EDW

FINANCIAL SOURCES (e.g. EPSi, Lawson, PeopleSoft)

DEPARTMENTAL SOURCES (e.g. Apollo)

Patient

Bad Debt

Provider

Provider

Encounter

Survey

ADMINISTRATIVE SOURCES

(e.g. API Time Tracking)

Cost

ENTERPRISE DATA MODEL

Charge

Census

Facility

House Keeping

Diagnosis

Procedure

Employee

EMR SOURCE

(e.g. Cerner)

PATIENT SATISFACTION

SOURCES

(e.g. NRC Picker)

Catha Lab

Time Keeping

More Transformation

Less Transformation

Enforced Referential Integrity

21

the dimensional shopping model
The Dimensional Shopping Model

Dairy

Dry Goods

Dry Goods

Dairy

__ 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

__ 1 c sugar

__ 2 c brown sugar

__ 2 t baking soda

__ 2t vanilla

__ 1 t salt

__ 4-5 c all-purpose flour

__ 4 cups chocolate chips

__ 4 eggs

__ 2 c shortening

Trip #1 to the Store

Trip #2 to the Store

Dimensional Shopping Model - Cookies

Dimensional Shopping Model - Cake

How many recipes to do you need to make?

slide25

Dimensional Data Model (Healthcare Point Solutions)

Redundant

Data

Extracts

FINANCIAL SOURCES (e.g. EPSi, Lawson, PeopleSoft)

DEPARTMENTAL SOURCES (e.g. Apollo)

Regulatory

Labor Productivity

Revenue Cycle

Dimensional Data

Model

ADMINISTRATIVE SOURCES

(e.g. API Time Tracking)

Pregnancy

Oncology

Asthma

Heart Failure

Diabetes

Census

EMR SOURCE

(e.g. Cerner)

PATIENT SATISFACTION

SOURCES

(e.g. NRC Picker)

More Transformation

Less Transformation

25

the adaptive shopping model
The Adaptive Shopping Model

Initial List

Additional

  • Apples
  • Tomato Soup
  • Flour
  • Milk
  • Turkey
  • Lettuce
  • Sugar
  • Beans
  • Hot dogs
  • Banana
  • Noodles
  • Yogurt

Get eggs

Buy flowers

Get tires rotated

Pick up dry cleaning

A d a p t i v e S h o p p i n g M o d e l

Store: _____________________________

And Even More

__ ______________

__ ______________

__ ______________

__ ______________

__ ______________

__ ______________

__ ______________

__ ______________

__ ______________

__ ______________

__ ______________

__ ______________

__ ______________

__ ______________

__ ______________

__ ______________

  • 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
shopping list revisited
Shopping List Revisited

Initial List

Additional

Once you are home can you make these recipes?

  • Apples
  • Tomato Soup
  • Flour
  • Milk
  • Turkey
  • Lettuce
  • Sugar
  • Beans
  • Hot dogs
  • Banana
  • Noodles
  • Yogurt

Get eggs

Buy flowers

Get tires rotated

Pick up dry cleaning

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

And Even More

  • 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

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

slide28

Adaptive Data Warehouse

Metadata: EDW AtlasSecurity and Auditing

FINANCIAL SOURCES (e.g. EPSi, Peoplesoft, Lawson)

DEPARTMENTAL SOURCES (e.g. Apollo)

Common, Linkable Vocabulary

FinancialSource Marts

DepartmentalSource Marts

Readmissions

AdministrativeSource Marts

PatientSource Marts

PATIENT SATISFACTION

SOURCES

(e.g. NRC Picker, Press Ganey)

ADMINISTRATIVE SOURCES

(e.g. API Time Tracking)

Diabetes

Sepsis

EMR Source Marts

HRSource Mart

EMR SOURCE

(e.g. Cerner)

Human Resources

(e.g. PeopleSoft)

More Transformation

Less Transformation

data modeling approaches
Data Modeling Approaches

Corporate Information Model

Popularized by Bill Inmon and Claudia Imhoff

Early Binding

I2B2

Popularized by Academic Medicine

Star Schema

Popularized by Ralph Kimball

Data Bus

Popularized by Dale Sanders

File Structure Association

Popularized by IBM mainframes in 1960s

Reappearing in Hadoop & NoSQL

Late Binding

origins of early vs late binding
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
origins of early vs late binding1
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
data binding in analytics
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
analytic relations
Analytic Relations

The key is to relate data, not model data

High Value Attributes

Core Data Elements

About 20 data attributes account for

90% of healthcare analytic use cases

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

six points to bind data
Six Points to Bind Data

Data Analysis

Source Data Content

Source System Analytics

Customized Data Marts

Visualization

Clinical

Clinical

Disease Registries

QlikView, Tableau

Microsoft Access

Web Applications

Excel

SAS, SPSS

et al.

Financial

Financial

Materials Management

Supplies

Supplies

Internal

Compliance Measures

HR

HR

Others

Others

Clinical Events

State

Operational Events

Academic

State

Research Registries

External

Academic

5

6

4

3

2

1

Business Rule and Vocabulary Binding Points

Low volatility = Early binding

High volatility = Late binding

binding principles strategy
Binding Principles & Strategy
  • Delay Binding as long as possible…until a clear analytic use case requires it
  • Earlier binding is appropriate for business rules or vocabularies that change infrequently or that the organization wants to “lock down” for consistent analytics
  • Late binding in the visualization layer is appropriate for “what if” scenario analysis
  • Retain a record of the bindings from the source system in the data warehouse
  • Retain a record of the changes to vocabulary and rules bindings in the data models of the data warehouse