late binding in data warehouses: desiging for analytic agility

16
© 2013 Health Catalyst Designing for Analytic Agility ww © w. 2 h 0 e 1 a 3 lth H c e a a ta lt l h ys C t. a co ta m ly Late Binding in Data Warehouses: Dale Sanders, Oct 2013

Upload: health-catalyst

Post on 04-Dec-2014

249 views

Category:

Health & Medicine


2 download

DESCRIPTION

Listen to Part 2 of the Late-Binding (TM) Data Warehouse webinar, a separate webinar focused on answering detailed follow-up questions generated from the first Late-Binding (TM) Data Warehouse webinar.

TRANSCRIPT

Page 2: Late Binding in Data Warehouses: Desiging for Analytic Agility

Overview

© 2013 Health Catalyst www.healthcatalyst.com

• The concept of “binding” in software and dataengineering

• 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

Page 3: Late Binding in Data Warehouses: Desiging for Analytic Agility

Late Binding in Software Engineering

3© 2013 Health Catalyst

www.healthcatalyst.com

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

Page 4: Late Binding in Data Warehouses: Desiging for Analytic Agility

4© 2013 Health Catalyst

www.healthcatalyst.com

Atomic data must be “bound” to business rules about that data andto 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

Page 6: Late Binding in Data Warehouses: Desiging for Analytic Agility

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?

ComprehensiveAgreement

Is the rule or vocabulary stableand rarely change?

PersistentAgreement

Acknowledgements to Mark Beyer of Gartner

© 2013 Health Catalyst www.healthcatalyst.com

6

Page 7: Late Binding in Data Warehouses: Desiging for Analytic Agility

© 2013 Health Catalyst www.healthcatalyst.com

ACADEMIC

STATE

SOURCE DATA CONTENT

SOURCE SYSTEM ANALYTICS

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 PAYERMEASURES

DISEASE REGISTRIES

MATERIALS MANAGEMENT

3 4 5

Data Rules and Vocabulary Binding Points

High Comprehension &Persistence of vocabulary &business rules? => Early binding

Low Comprehension andPersistence of vocabulary orbusiness rules? => Late binding

Six Binding Points in a Data Warehouse

21 6

Page 8: Late Binding in Data Warehouses: Desiging for Analytic Agility

© 2013 Health Catalyst www.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

Page 9: Late Binding in Data Warehouses: Desiging for Analytic Agility

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

Page 10: Late Binding in Data Warehouses: Desiging for Analytic Agility

CatalystApps

Client Developed

Apps

Third PartyApps

Ad HocQuery Tools

EMR CostRxClaims Etc.Patient Sat

Catalyst’s Late Binding Bus ArchitectureTM

CP

T c

od

e

Da

te &

Tim

e

DR

G c

od

e

Dru

g co

de

Em

plo

yee

ID

Em

plo

yer

ID

En

cou

nte

r ID

Ge

nd

er

ICD

dia

gn

osi

s

cod

e

De

pa

rtm

en

t I

D

Fa

cilit

y ID

La

b co

de

Pa

tien

t ty

pe

Me

mb

er

ID

Pa

yer/

carr

ier

ID

Pro

vid

er

ID

The Bus Architecture

© 2013 Health Catalyst www.healthcatalyst.com

Page 11: Late Binding in Data Warehouses: Desiging for Analytic Agility

Healthcare Analytics Adoption Model

© 2013 Health Catalyst www.healthcatalyst.com

Level 8 Personalized Medicine& Prescriptive Analytics

Tailoring patient care based on population outcomes andgenetic 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 Reduction Reducing variability in care processes. Focusing oninternal 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. Cumbersomeinternal and external reporting.

Page 12: Late Binding in Data Warehouses: Desiging for Analytic Agility

Progression in the Model

© 2013 Health Catalyst www.healthcatalyst.com

The patterns at each level• Data content expands

• Adding new sources of data to expand our understanding of caredelivery and the patient

• Data timeliness increases

• To support faster decision cycles and lower “Mean Time ToImprovement”

• Data governance and literacy 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?”•

Page 13: Late Binding in Data Warehouses: Desiging for Analytic Agility

The Expanding Data EcosystemBilling data1

Lab data2

Imaging data3

Inpatient EMR data4

Outpatient EMR data5

Claims Data6

HIE Data7

Detailed cost accounting8

Bedside monitoring data9

External pharmacy data10

Familial data11

Home monitoring data12

Patient reported outcomes data13

Long term care facility data14

Genomic data15

Real-time 7x24 biometric monitoring for all patients in the ACO

16

NO

W1-

2 Y

EA

RS

2-4

YE

AR

S

Not currently being addressed by vendors

© 2013 Health Catalyst www.healthcatalyst.com

13

Page 14: Late Binding in Data Warehouses: Desiging for Analytic Agility

Principles to Remember

© 2013 Health Catalyst www.healthcatalyst.com

14

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 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

Page 15: Late Binding in Data Warehouses: Desiging for Analytic Agility

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.”

© 2013 Health Catalyst www.healthcatalyst.com

15

Page 16: Late Binding in Data Warehouses: Desiging for Analytic Agility

16

Questions

• Learn about the technical overview of Late-Binding

http://www.healthcatalyst.com/late-binding-data-warehouse-explained

• Contact us to learn more about our solutions and communication tools

www.healthcatalyst.com/company/contact-us