1. A Bit About Architecture 1

2. “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 2

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7. 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 7

8. 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. 8

9. Building a Healthcare Analytics Architecture or What Would Dr. Snow Do? a healthcare analytics thought experiment

10. 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 its consequences. 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 intention of any kind to actually perform the experiment in question. The common goal of a thought experiment is to explore the potential consequences of the principle in question.Germanhypothesistheoryprinciple - Widipedia - 10

11. Our Thought Experiment Today The Setting: Cholera Outbreak in London, 1854 Dr. Snow’s Study of the Epidemic and his Intervention C Can we conceive an analytic architecture capable of reproducing Dr. Snow’s results? C C

12. Dr. Snow and the London Cholera Outbreak of 1854 Cholera – a disease of urban population density (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 12

13. Our Protagonists Noted anesthesiologist Previous study of cholera Soho resident Published soiled water theory Theories shunned by community Dr. John Snow Henry Whitehead Assistant curate at St. Luke’s Very familiar with local custom and culture Originally believed ‘miasma’ theory Our Antagonists The real cause of Cholera V. Cholerea a bacterium William Farr ‘Miasma’ theory of disease predominates Supported medically and politically “All Smell is Disease” Many ancillary miasma theories Chloride of lime on streets 13

14. What Can We Say About Dr. Snow’s Data? The London Census (The General Registry) Name Birth Death Record Name Gender Address Cause of Death Marriages Profession Address Dr. Snow’s Data Name Date of Fatal Cholera Attack (added from his interviews) Date of death(from the General Registry) Age (estimate) Address Anecdotal information about ‘consumed| water source’. Did not carry out comprehensive or thorough survey Whitehead’s Data Name Age Address (assumed not explicitly stated) Position of the rooms occupied Sanitary arrangements, Consumed water with respect to the Broad Street pump, and the hour of onset of the fatal attack. Disparate Systems No initial integration No data integrity check No identifying index number Manually Collected Text based 14

15. 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 key leading to intervention 15

16. Dr. Snow and the Broad Street Pump 16

17. How does Dr. Snow take his data and challenge a medical theory long entrenched in the medical, social, and political institutions of his world??? 17

18. Dr. Snow Sees Edmund Cooper’s Map 18

19. Snow’s Ghost Map Version 1: Not Good Enough for the Miasmists ‘Stacked’ deaths for emphasis Broad Street pump common 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 19

20. Snow’s Ghost Map Version 2: The Voronoi Diagram Points inside Snow’s diagram are closer to the Broad Street pump than 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 1908Georgy Fedosievych Voronyi 20

21. The Intervention Whitehead discovers index patient’s father contracted cholera at the time of pump handle removal. 21

22. Could We Help Dr. Snow Today Source data captured all deaths logged by date Define business rules select only Cholera victims reconcile patient identity and address Combine data from disparate data sources mashup – London City Map and Logged Cholera Deaths Cleanse Data. Explain data anomalies/outliers conversations. visitation Develop effective communication of results graphic (not text) presentation Develop intervention remove pump handle Track post intervention results log of daily Cholera deaths Infrastructure or Architecture ???? 22

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24. Our Analytics Architecture Guiding Principles (you wouldn’t build a house without ‘em) 24

25. Source Systems Load Original Data The 1850 Census Dr. Snow’s Death Record Whitehead’s Interviews 1 Create Relational Database Warehouse Clean up, De-Dup etc Local Data Sources No Transformation – Preserve Original Data 25

26. Source Systems DW – Clean, Reconcile, Combine, De-dup, standardize, transform The 1850 Census Dr. Snow’s Death Record Whitehead’s Interviews Create Data Staging Layer Add Ancillary Data from Trusted Sources DW Data Staging Area 26

27. Source Systems DW – Clean, Reconcile, Combine, De-dup, standardize, transform The 1850 Census Dr. Snow’s Death Record Whitehead’s Interviews 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 27

28. Source Systems DW – Clean, Reconcile, Combine, De-dup, standardize, transform The 1850 Census Dr. Snow’s Death Record Whitehead’s Interviews 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 28

29. Source Systems DW – Clean, Reconcile, Combine, De-dup, standardize, transform The 1850 Census Dr. Snow’s Death Record Whitehead’s Interviews. DW Data Staging Area Business Rules Create Conformed Data Model Analytic tools Logical Groupings Create Analytics Layer Analysis Tools Data Groupings (e.g. Cubes) Output Reports Dashboards Screens Alerts Maps 29

30. Source Systems DW – Clean, Reconcile, Combine, De-dup, standardize, transform The 1850 Census Dr. Snow’s Death Record Whitehead’s Interviews. DW Data Staging Area Business Rules Create Conformed Data Model Analytic tools Logical Groupings Create Analytics Layer Analysis Tools Data Groupings (e.g. Cubes) Output Reports Dashboards Screens Alerts Maps 30

31. Source Systems DW – Clean, Reconcile, Combine, De-dup, standardize, transform The 1850 Census Dr. Snow’s Death Record Whitehead’s Interviews. DW Data Staging Area Business Rules Create Conformed Data Model Analytic tools Logical Groupings Create Data Governance Define Workflow Maintain Data Dictionary Insure Calculation Integrity Output Reports Dashboards Screens Alerts Maps G O V E R N A N C E 31

32. 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 32

33. What Problem Will You Solve Today? When will you make something cool? When will you make something useful? Young Geek Old Geek 33

34. UTD/UTSouthwestern Analytics Collaboration 34

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38. UTSW MyChart Patient Portal 38

39. Next Questions??? Possible Interventions??? 39