Competitive Platforms: Enterprise Data Model

Report
Comparing Healthcare Data Warehouse Approaches:
A Deep-dive Evaluation of the Three Major
Methodologies
February 2014
Creative Commons Copyright
A Personal Experience with Healthcare
•
•
Dear mother…
A trip to the doctor…
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© 2013 Health Catalyst | www.healthcatalyst.com
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
Three Systems of Care Delivery
Analytic
System
Deployment
System
Content
System
4
Creative Commons Copyright
Population Health Management
Mean
# of
Cases
1 box = 100
cases in a year
Excellent Outcomes
# of
Cases
Poor 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%)
© 2013 Health Catalyst | www.healthcatalyst.com
5
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. Feefor-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 Warehouse
Collecting 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)
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
8
© 2013 Health Catalyst | www.healthcatalyst.com
HR – Desired State
Authors
Typical
User
Distribution
Drillers
• 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)
Viewers
• 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
9
Authors or
Knowledge
Workers
Drillers
Ideal User
Distribution for
Continuous
Improvement
Viewers
© 2013 Health Catalyst | www.healthcatalyst.com
Comparison of prevailing
approaches
© 2013 Health Catalyst | www.healthcatalyst.com
Enterprise Data Model
EDW
FINANCIAL SOURCES
(e.g. EPSi, Lawson,
PeopleSoft)
DEPARTMENTAL
SOURCES
(e.g. Apollo)
Patient
Provider
Bad Debt
Provider
Survey
Encounter
Cost
ADMINISTRATIVE
SOURCES
(e.g. API Time Tracking)
Charge
ENTERPRISE
DATA MODEL
Census
Facility
House
Keeping
Diagnosis
Procedure
Employee
Catha Lab
Time
Keeping
EMR SOURCE
(e.g. Cerner)
More Transformation
Less Transformation
PATIENT SATISFACTION
SOURCES
(e.g. NRC Picker)
Enforced Referential Integrity© 2013 Health Catalyst | www.healthcatalyst.com
11
Enterprise Data Model – Still need Subject Area Marts
EDW
FINANCIAL SOURCES
(e.g. EPSi, Lawson,
PeopleSoft)
DEPARTMENTAL
SOURCES
(e.g. Apollo)
Patient
Provider
Bad Debt
Provider
Readmissions
ADMINISTRATIVE
SOURCES
(e.g. API Time Tracking)
Survey
Encounter
Cost
Diabetes
Charge
ENTERPRISE
DATA MODEL
Census
Facility
Sepsis
House
Keeping
Diagnosis
Procedure
Employee
Catha Lab
Time
Keeping
EMR SOURCE
(e.g. Cerner)
More Transformation
Less Transformation
PATIENT SATISFACTION
SOURCES
(e.g. NRC Picker)
Enforced Referential Integrity© 2013 Health Catalyst | www.healthcatalyst.com
12
Bill of Materials Conceptual Model
Product
Supplier
Order
Customer
Typical Analyses
• Counts
• Simple aggregations
• By various dimensions
© 2013 Health Catalyst | www.healthcatalyst.com
13
Star Schema Conceptual Model
Dimension 4
Dimension 1
(Location)
(Product)
Typical Analyses
• Transaction counts
• Simple aggregations
• By various dimensions
Fact
(Transaction)
Dimension 3
Dimension 2
(Purchaser)
(Date)
© 2013 Health Catalyst | www.healthcatalyst.com
14
Vertical Summary Data Marts
FINANCIAL SOURCES
(e.g. EPSi, Lawson,
PeopleSoft)
DEPARTMENTAL
SOURCES
(e.g. Apollo)
Regulatory
Labor
Productivity
ADMINISTRATIVE
SOURCES
(e.g. API Time Tracking)
Pregnancy
Dimensional
Data
Model
Revenue Cycle
Oncology
Asthma
Redundant
Data
Extracts
Heart
Failure
Diabetes
Census
PATIENT SATISFACTION
SOURCES
(e.g. NRC Picker)
EMR SOURCE
(e.g. Cerner)
More Transformation
Less Transformation
© 2013 Health Catalyst | www.healthcatalyst.com
15
Adaptive Data Warehouse
Metadata: EDW Atlas Security and Auditing
Common, Linkable
Vocabulary
FINANCIAL SOURCES
(e.g. EPSi, Peoplesoft,
Lawson)
Financial
Source Marts
DEPARTMENTAL
SOURCES
(e.g. Apollo)
Departmental
Source Marts
Readmissions
Administrative
Source Marts
ADMINISTRATIVE
SOURCES
(e.g. API Time Tracking)
Diabetes
Patient
Source Marts
PATIENT SATISFACTION
SOURCES
(e.g. NRC Picker, Press
Ganey)
Sepsis
EMR
Source Marts
HR
Source Mart
EMR SOURCE
(e.g. Cerner)
Human Resources
(e.g. PeopleSoft)
More Transformation
Less Transformation
© 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
© 2013 Health Catalyst | www.healthcatalyst.com
Sample Diabetes Registry Data Model
Procedure
Code
Diagnosis
Code
Diagnosis
History
Procedure
History
Office Visit
Vital Signs
History
Diabetes
Patient
Exam
History
Current Lab
Result
Lab Result
History
Exam Type
Lab Type
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?
© 2013 Health Catalyst | www.healthcatalyst.com
18
Measurement System Exercise
Webinar
19
© 2012
2013 Health Catalyst | www.healthcatalyst.com
The Enterprise Shopping Model
Your Shopping List
Enterprise Shopping Model
Produce
__ Apples
__ Pears
__ Tomatoes
__ Carrots
Dairy
__ Celery
__ Banana
__ Melon
__ Grapes
Meat
__ Beef
__ Ham
__ Chicken
__ Pork
__ Milk
__ Eggs
__ Cheese
__ Cream
__ 2% Milk
__ Half & Half
__ Yogurt
__ Margarine
Dry Goods
__ Turkey
__ Sausage
__ Lamb
__ Bacon
__ Pasta
__ Flour
__ Sugar
__ Soup
__ Baking soda
__ Rice
__ Beans
__ B. Sugar
Apples
Sugar
Tomato Soup Beans
Flour
Hot dogs
Milk
Banana
Turkey
Noodles
Lettuce
Yogurt
Additional purchases
Eggs
Flowers
Tires
Dry cleaning
© 2013 Health Catalyst | www.healthcatalyst.com
Enterprise Data Model (Technology Vendors)
EDW
FINANCIAL SOURCES
(e.g. EPSi, Lawson,
PeopleSoft)
DEPARTMENTAL
SOURCES
(e.g. Apollo)
Patient
Provider
Bad Debt
Provider
Survey
Encounter
Cost
ADMINISTRATIVE
SOURCES
(e.g. API Time Tracking)
ENTERPRISE
DATA MODEL
Charge
Census
Facility
House
Keeping
Diagnosis
Procedure
Employee
Catha Lab
Time
Keeping
EMR SOURCE
(e.g. Cerner)
More Transformation
Less Transformation
PATIENT SATISFACTION
SOURCES
(e.g. NRC Picker)
Enforced Referential Integrity© 2013 Health Catalyst | www.healthcatalyst.com
21
Using a dimensional model in Healthcare
is kind of like shopping for data like this …
22
© 2013 Health Catalyst | www.healthcatalyst.com
23
© 2013 Health Catalyst | www.healthcatalyst.com
The Dimensional Shopping Model
Trip #1 to the Store
Trip #2 to the Store
Dimensional Shopping Model - Cookies
Dairy
__ 4 eggs
__ 2 c shortening
Dimensional Shopping Model - Cake
Dry Goods
Dairy
Dry Goods
__ ½ cup of butter
__ ½ cup milk
__ 2 eggs
__ 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
__ 1 cup white sugar
__ 1 ½ cups all-purpose flour
__ 2 teaspoons vanilla extract
__ 1 ¾ teaspoon baking powder
How many recipes to do you need to make?
24
© 2013 Health Catalyst | www.healthcatalyst.com
Dimensional Data Model (Healthcare Point Solutions)
FINANCIAL SOURCES
(e.g. EPSi, Lawson,
PeopleSoft)
DEPARTMENTAL
SOURCES
(e.g. Apollo)
Regulatory
Labor
Productivity
ADMINISTRATIVE
SOURCES
(e.g. API Time Tracking)
Pregnancy
Revenue Cycle
Oncology
Heart
Failure
Asthma
Redundant
Data
Extracts
Diabetes
Census
PATIENT SATISFACTION
SOURCES
(e.g. NRC Picker)
EMR SOURCE
(e.g. Cerner)
More Transformation
Dimensional
Data
Model
Less Transformation
© 2013 Health Catalyst | www.healthcatalyst.com
25
The Adaptive Shopping Model
Adaptive Shopping Model
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
•
•
Store: _____________________________
__ ______________
__ ______________
__ ______________
__ ______________
__ ______________
__ ______________
__ ______________
__ ______________
Initial List
•
•
__ ______________
__ ______________
__ ______________
__ ______________
__ ______________
__ ______________
__ ______________
__ ______________
•
•
•
•
•
•
•
•
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
•
•
•
•
•
•
•
•
•
•
26
© 2013 Health Catalyst | www.healthcatalyst.com
Shopping List Revisited
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
•
•
•
•
Once you are home can
you make these recipes?
•
•
•
•
•
•
•
•
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
•
•
•
•
•
•
•
•
•
•
27
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
Cookies:
2 eggs
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
© 2013 Health Catalyst | www.healthcatalyst.com
Adaptive Data Warehouse
Metadata: EDW Atlas Security and Auditing
Common, Linkable
Vocabulary
FINANCIAL SOURCES
(e.g. EPSi, Peoplesoft, Lawson)
Financial
Source Marts
DEPARTMENTAL SOURCES
(e.g. Apollo)
Departmental
Source Marts
Readmissions
Administrative
Source Marts
Diabetes
Patient
Source Marts
ADMINISTRATIVE SOURCES
(e.g. API Time Tracking)
PATIENT SATISFACTION
SOURCES
(e.g. NRC Picker, Press Ganey)
Sepsis
EMR
Source Marts
HR
Source Mart
EMR SOURCE
(e.g. Cerner)
Human Resources
(e.g. PeopleSoft)
More Transformation
Less Transformation
© 2013 Health Catalyst
| www.healthcatalyst.com
Late-binding Deeper Dive
29
© 2012
2013 Health Catalyst | www.healthcatalyst.com
Data Modeling Approaches
Corporate Information Model
Early Binding
Popularized by Bill Inmon and Claudia Imhoff
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
© 2013 Health Catalyst | www.healthcatalyst.com
30
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
© 2013 Health Catalyst | www.healthcatalyst.com
31
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
© 2013 Health Catalyst | www.healthcatalyst.com
32
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
© 2013 Health Catalyst | www.healthcatalyst.com
33
Analytic Relations
The key is to relate data, not model data
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
High Value Attributes
About 20 data attributes account for
90% of healthcare analytic use cases
Vocab in
Source
System 1
Vocab in
Source
System 2
Vocab in
Source
System 3
Highest value area
for standardizing
vocabulary
© 2013 Health Catalyst | www.healthcatalyst.com
34
Six Points to Bind Data
Data Analysis
Internal
Source Data
Content
Source System
Analytics
Clinical
Clinical
Financial
Financial
Supplies
Supplies
HR
HR
Others
Others
Customized Data
Marts
Visualization
Disease Registries
QlikView, Tableau
Materials Management
Microsoft Access
Compliance Measures
Web Applications
Clinical Events
Excel
Operational Events
SAS, SPSS
Research Registries
et al.
5
6
External
State
Academic
State
Academic
1
2
3
4
Business Rule and Vocabulary Binding Points
Low volatility = Early binding
High volatility = Late binding
© 2013 Health Catalyst | www.healthcatalyst.com
35
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
© 2013 Health Catalyst | www.healthcatalyst.com
36
Thank you!
37
© 2012
2013 Health Catalyst | www.healthcatalyst.com

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