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A Bit About Architecture

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“Information Architecture is a high level or general view of something that conveys an overall understanding of its various components and how those components interrelate.”

John Hobbs

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Why is Architecture Important

• Achieve intended goals• Control weaknesses and threats• Specify and manage policies and

mechanics for delivering strategic goals• Defines infrastructure requirements • Minimize vendor dependence and cost• Drives effective governance

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Architecture vs Infrastructure

“Infrastructure are the technologies required to support all the information systems activities taking place across the organization. The infrastructure will serve the users within the business much the same way a road and rail networks serve transport users.”

Source: Gunton T. “Building a Framework for Corporate Information Handling”, Prentice Hall, 1989.

Building a Healthcare Analytics Architecture

orWhat Would Dr. Snow Do?

a healthcare analytics thought experiment

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What is a Thought Experiment?

A thought experiment or Gedankenexperiment(from German) considers some hypothesis, theory,[1

or principle for the purpose of thinking through itsconsequences. Given the structure of the experiment,it may or may not be possible to actually perform it,and if it can be performed, there need be no intentionof any kind to actually perform the experiment inquestion. The common goal of a thought experimentis to explore the potential consequences of the principle in question.

- Widipedia -

Our ThoughtExperiment Today

The Setting:Cholera Outbreak in London, 1854

Dr. Snow’s Study of the Epidemic and his Intervention

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Can we conceive an analytic architecture capable of reproducing Dr. Snow’s results?

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C

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Dr. Snow andthe London Cholera Outbreak of 1854

Cholera – a disease of urban populationdensity (First Cholera in London – 1832)

Sudden outbreak in London’s Soho District, August 1854

Can kill within hours of onset

Extreme fluid loss

Blue skin tint in later stages

No germ theory

Miasma prevailing theory of cause

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Our Protagonists

Noted anesthesiologistPrevious study of choleraSoho residentPublished soiled water theoryTheories shunned by community

Dr. John Snow Henry Whitehead

Assistant curate at St. Luke’sVery familiar with local custom and cultureOriginally believed ‘miasma’ theory

Our Antagonists

The real cause of Cholera

V. Cholereaa bacterium

William Farr

‘Miasma’ theory of disease predominatesSupported medically and politically“All Smell is Disease”Many ancillary miasma theoriesChloride of lime on streets

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What Can We SayAbout Dr. Snow’s Data?

The London Census(The General Registry)

NameBirthDeath Record Name Gender Address Cause of DeathMarriagesProfessionAddress

Dr. Snow’s Data

NameDate of Fatal Cholera Attack(added from his interviews)Date of death(from the General Registry)Age (estimate)AddressAnecdotal information about ‘consumed|water source’. Did not carry out comprehensive or thorough survey

Whitehead’s Data

NameAgeAddress (assumed not explicitly stated) Position of the rooms occupiedSanitary arrangements, Consumed water with respect tothe Broad Street pump, and the hour of onset of the fatal attack.

Disparate SystemsNo initial integrationNo data integrity checkNo identifying index numberManually CollectedText based

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Whitehead’s Corroboration

Located ‘Index’ patient(Infant)

Isolated probable cause of contamination(Soiled nappies thrown in nearby cesspit)

Caused cesspit inspection(Brick deterioration causing leak into Broad Street Well)

Abandoned disease theory of Miasma

Critical cultural and social knowledge keyleading to intervention

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Dr. Snow and the Broad Street Pump

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How does Dr. Snow take his data andchallenge a medical theory long entrenched in the medical, social, and politicalinstitutions of his world???

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Dr. Snow Sees Edmund Cooper’s Map

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Snow’s Ghost Map Version 1:Not Good Enough for the Miasmists

‘Stacked’ deaths for emphasis

Broad Street pumpcommon water source

‘Look for life where there should be death. Look for death where there should be life.

The aunt and her niece

The workhouse

The brewery

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Snow’s Ghost Map Version 2:The Voronoi Diagram Points inside Snow’s

diagram are closer tothe Broad Street pumpthan any other pump.

NOTE: Voronoi diagrams are named after Ukrainian mathematician Georgy Fedosievych Voronyi (or Voronoy) who defined and studied the general n-dimensional case in 1908

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The Intervention

Whitehead discovers index patient’s father contracted cholera at the time of pump handle removal.

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Could We Help Dr. Snow Today• Source data captured

all deaths logged by date• Define business rules

select only Cholera victimsreconcile patient identity and address

• Combine data from disparate data sourcesmashup – London City Map and Logged Cholera Deaths

• Cleanse Data. Explain data anomalies/outliersconversations. visitation

• Develop effective communication of resultsgraphic (not text) presentation

• Develop interventionremove pump handle

• Track post intervention resultslog of daily Cholera deaths

Infrastructure or Architecture ????

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Our Analytics ArchitectureGuiding Principles

(you wouldn’t build a house without ‘em)

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Source Systems

Load Original Data

The 1850 Census

Dr. Snow’s Death Record

Whitehead’sInterviews

1 Create Relational Database Warehouse Clean up, De-Dup etc Local Data Sources No Transformation – Preserve Original Data

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Source Systems

DW – Clean, Reconcile, Combine, De-dup, standardize, transform

The 1850 Census

Dr. Snow’s Death Record

Whitehead’sInterviews

Create Data Staging Layer Add Ancillary Data from Trusted Sources

DW Data Staging Area

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Source Systems

DW – Clean, Reconcile, Combine, De-dup, standardize, transform

The 1850 Census

Dr. Snow’s Death Record

Whitehead’sInterviews

Create Transforms and Business Rules Standardized Data Definitions Standardize Transformation Algorithms Group Like Entities (e.g. Master Person Index, Locations, Families, etc.

DW Data Staging Area

Business Rules

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Source Systems

DW – Clean, Reconcile, Combine, De-dup, standardize, transform

The 1850 Census

Dr. Snow’s Death Record

Whitehead’sInterviews

Create a Conformed Data Model Data Standards Applied to Original Data Transform Algorithms Applied Entities (people, families, locations) grouped correctly.

DW Data Staging Area

Business Rules

Create Conformed Data Model

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Source Systems

DW – Clean, Reconcile, Combine, De-dup, standardize, transform

The 1850 Census

Dr. Snow’s Death Record

Whitehead’sInterviews

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DW Data Staging Area

Business Rules

Create Conformed Data Model

Analytic tools

Logical GroupingsLogical

GroupingsLogical GroupingsLogical

Groupings

Create Analytics Layer Analysis Tools Data Groupings (e.g. Cubes)

OutputReportsDashboardsScreensAlertsMaps

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Source Systems

DW – Clean, Reconcile, Combine, De-dup, standardize, transform

The 1850 Census

Dr. Snow’s Death Record

Whitehead’sInterviews

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DW Data Staging Area

Business Rules

Create Conformed Data Model

Analytic tools

Logical GroupingsLogical

GroupingsLogical GroupingsLogical

Groupings

Create Analytics Layer Analysis Tools Data Groupings (e.g. Cubes)

OutputReportsDashboardsScreensAlertsMaps

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Source Systems

DW – Clean, Reconcile, Combine, De-dup, standardize, transform

The 1850 Census

Dr. Snow’s Death Record

Whitehead’sInterviews

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DW Data Staging Area

Business Rules

Create Conformed Data Model

Analytic tools

Logical GroupingsLogical

GroupingsLogical GroupingsLogical

Groupings

Create Data Governance Define Workflow Maintain Data Dictionary Insure Calculation Integrity

OutputReportsDashboardsScreensAlertsMaps

G O

V E

R N

A N

C E

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Let’s Review Our Architecture

• Achieve intended goals• Control weaknesses and threats• Specify and manage policies and

mechanics for delivering strategic goals• Defines infrastructure requirements • Minimize vendor dependence and cost• Drives effective governance

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What Problem Will You Solve Today?

When will you make something cool?

When will you make something

useful?

Young Geek Old Geek

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UTD/UTSouthwestern Analytics Collaboration

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UTSW MyChart Patient Portal

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Next Questions???Possible Interventions???