Late Binding in Data Warehouses
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Transcript of Late Binding in Data Warehouses
© 2013 Health Catalystwww.healthcatalyst.com
© 2013 Health Catalystwww.healthcatalyst.com
Designing for Analytic Agility
Late Binding in Data Warehouses:
Dale Sanders, Oct 2013
© 2013 Health Catalystwww.healthcatalyst.com
Overview
• The concept of “binding” in software and data engineering
• Examples of data binding in healthcare
• The two tests for early binding• Comprehensive & persistent agreement
• The six points of binding in data warehouse design• Data Modeling vs. Late Binding
• The importance of binding in analytic progression• Eight levels of analytic adoption in healthcare
© 2013 Health Catalystwww.healthcatalyst.com
3
Late Binding in Software Engineering
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
● Major 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 Catalystwww.healthcatalyst.com
44
Atomic data must be “bound” to business rules about that data and to vocabularies related to that data in order to create information
Vocabulary binding in healthcare is pretty obvious● Unique patient and provider identifiers● Standard facility, department, and revenue center codes● Standard definitions for gender, race, ethnicity● ICD, CPT, SNOMED, LOINC, RxNorm, RADLEX, etc.
Examples of binding data to business rules● Length of stay● Patient relationship attribution to a provider● Revenue (or expense) allocation and projections to a department● Revenue (or expense) allocation and projections to a physician● Data definitions of general disease states and patient registries● Patient exclusion criteria from disease/population management● Patient admission/discharge/transfer rules
Late Binding in Data Engineering
© 2013 Health Catalystwww.healthcatalyst.com
Data Binding
What’s the rule for declaring and managing a “hypertensive patient”?
Vocabulary
“systolic &diastolicblood pressure”
Rules
“normal”
Pieces ofmeaningless
data
11260
Bindsdata to
Software Programming
© 2013 Health Catalystwww.healthcatalyst.com6
Why Is This Concept Important?
Two tests for tight, early binding
Knowing when to bind data, and howtightly, to vocabularies and rules is
THE KEY to analytic success and agility
Is the rule or vocabulary widely accepted as true and accurate in the organization or industry?
Comprehensive Agreement
Is the rule or vocabulary stable and rarely change?
PersistentAgreement
Acknowledgements to Mark Beyer of Gartner
© 2013 Health Catalystwww.healthcatalyst.com
ACADEMIC
STATE
SOURCEDATA CONTENT
SOURCE SYSTEMANALYTICS
CUSTOMIZED DATA MARTS
DATAANALYSIS
OTHERS
HR
FINANCIAL
CLINICAL
SUPPLIES
INT
ER
NA
LE
XT
ER
NA
L
ACADEMIC
STATE
OTHERS
HR
FINANCIAL
CLINICAL
SUPPLIES
RESEASRCH REGISTRIES
QlikView
Microsoft Access/ODBC
Web applications
Excel
SAS, SPSS
Et al
OPERATIONAL EVENTS
CLINICAL EVENTS
COMPLIANCE AND PAYER MEASURES
DISEASE REGISTRIES
MATERIALS MANAGEMENT
3
Data Rules and Vocabulary Binding Points
High Comprehension & Persistence of vocabulary & business rules? => Early binding
Low Comprehension and Persistence of vocabulary or business rules? => Late binding
Six Binding Points in a Data Warehouse
421 5 6
© 2013 Health Catalystwww.healthcatalyst.com
Data Modeling for AnalyticsFive Basic Methodologies
● Corporate Information Model‒ Popularized by Bill Inmon and Claudia Imhoff
● I2B2‒ Popularized by Academic Medicine
● Star Schema‒ Popularized by Ralph Kimball
● Data Bus Architecture‒ Popularized by Dale Sanders
● File Structure Association‒ Popularized by IBM mainframes in 1960s
‒ Reappearing in Hadoop & NoSQL
‒ No traditional relational data model
Early binding
Late binding
Binding to Analytic Relations
Core Data Elements
Charge code
CPT code
Date & Time
DRG code
Drug code
Employee ID
Employer ID
Encounter ID
Gender
ICD diagnosis code
ICD procedure code
Department ID
Facility ID
Lab code
Patient type
Patient/member ID
Payer/carrier ID
Postal code
Provider ID
In today’s environment, about 20 data elements represent 80-90% of analytic use cases. This will grow over time, but right now, it’s fairly simple.
Source data vocabulary Z (e.g., EMR)
Source data vocabulary Y (e.g., Claims)
Source data vocabulary X
(e.g., Rx)
In data warehousing, the key is to relate data, not model data
© 2013 Health Catalystwww.healthcatalyst.com
Vendor AppsClient
Developed Apps
Third Party Apps
Ad Hoc Query Tools
EMR CostRxClaims Etc.Patient Sat
Late Binding Bus Architecture
CP
T c
ode
Dat
e &
Tim
e
DR
G c
ode
Dru
g co
de
Em
ploy
ee I
D
Em
ploy
er I
D
Enc
ount
er I
D
Gen
der
ICD
dia
gnos
is c
ode
Dep
artm
ent
ID
Fac
ility
ID
Lab
code
Pat
ient
typ
e
Mem
ber
ID
Pay
er/c
arrie
r ID
Pro
vide
r ID
The Bus Architecture
© 2013 Health Catalystwww.healthcatalyst.com
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.
© 2013 Health Catalystwww.healthcatalyst.com
Progression in the ModelThe patterns at each level
• Data content expands
• Adding new sources of data to expand our understanding of care
delivery and the patient
• Data timeliness increases
• To support faster decision cycles and lower “Mean Time To
Improvement”
• Data governance expands
• Advocating greater data access, utilization, and quality
• The complexity of data binding and algorithms increases
• From descriptive to prescriptive analytics
• From “What happened?” to “What should we do?”
© 2013 Health Catalystwww.healthcatalyst.com
The Expanding Ecosystem of Data Content
• Real time 7x24 biometric monitoring data for all patients in the ACO
• Genomic data• Long term care facility data• Patient reported outcomes data*• Home monitoring data• Familial data• External pharmacy data• Bedside monitoring data• Detailed cost accounting data*• HIE data• Claims data• Outpatient EMR data• Inpatient EMR data• Imaging data• Lab data• Billing data
3-12 months
1-2 years
2-4 years
* - Not currently being addressed by vendor products
© 2013 Health Catalystwww.healthcatalyst.com
Six Phases of Data Governance
You need to move through these phases in no more than two years
14
3-12 months
1-2 years
2-4 years
• Phase 6: Acquisition of Data
• Phase 5: Utilization of Data
• Phase 4: Quality of Data
• Phase 3: Stewardship of Data
• Phase 2: Access to Data
• Phase 1: Cultural Tone of “Data Driven”
One Page Self Inspection Guide
© 2013 Health Catalystwww.healthcatalyst.com16
Principles to Remember1. 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 changes to vocabulary and rules
bindings in the data models of the data warehouse
● Bake the history of vocabulary and business rules bindings into the
data models so you can retrace your analytic steps if need be
© 2013 Health Catalystwww.healthcatalyst.com17
Closing Words of Caution
Healthcare suffers from a low degree of Comprehensive and Persistent agreement on many topics that impact analytics
The vast majority of vendors and home grown data warehouses bind to rules and vocabulary too early and too tightly, in comprehensive enterprise data models
Analytic agility and adaptability suffers greatly• “We’ve been building our EDW for two years.”
• “I asked for that report last month.”