You can watch the replay for this webcast in the IDERA Resource Center: http://ow.ly/GcUs50A5cvl This session will describe a large data warehouse that we built with the medical data for a large hospital. The warehouse has a Kimball architecture with 200+ dimensions and 100+ facts. The primary input source is from Epic Clarity. We'll describe the project and give a flavor of the data model. We'll discuss some of what went right and wrong about the project and take questions from the audience. About Michael: Michael Blaha is a consultant and trainer who specializes in conceiving, architecting, modeling, designing and tuning databases. He has worked with dozens of organizations around the world. Blaha has authored seven U.S. patents, seven books, many articles, and two video courses. His most recent publication is the Agile Data Warehouse Design video course from O’Reilly. He received his doctorate from Washington University in St. Louis, and is an alumnus of GE Global Research in Schenectady, New York.

1. © 2017 IDERA, Inc. All rights reserved.
Proprietary and confidential.
A MEDICAL DATA
WAREHOUSE MODEL
Michael R Blaha, DSc.
blaha@computer.org
www.superdataguy.com

2. 2© 2017 IDERA, Inc. All rights reserved. Proprietary and confidential.
WAREHOUSE DATA FLOW

3. 3© 2017 IDERA, Inc. All rights reserved. Proprietary and confidential.
SOURCE DATA DETAILS
 Epic Clarity is 99+% of source data
• All the data of a major hospital
• Small amount of data from other apps and
external sources
• No scrubbing of source data
• 10,000+ Clarity tables
• Some tables have 200+ columns
• Much redundant data
• Clarity also has both transaction data and rollup data
• No referential integrity
• Odd PKs of many-to-many tables
• Table1 PK + line_number

4. 4© 2017 IDERA, Inc. All rights reserved. Proprietary and confidential.
OTHER DATA FLOW DETAILS
 Data warehouse platform is Netezza
 The staging tables maintain a history of operational data
• One staging table for each major source
• Staging schema = operational schema + surrogate key
+ effective date + expiration date
 Informatica for staging data + a new commercial tool
 Informatica for ETL + agile SQL queries

5. 5© 2017 IDERA, Inc. All rights reserved. Proprietary and confidential.
BUS ARCHITECTURE
 Definition: A data warehouse with dimensions that are
consistently defined across facts
 200+ dimensions; 100+ facts; Kimball approach
 Fully commented data model with naming standards
 We created our own DW schema
• This project preceded Epic’s DW schema
 DW has strong correspondence to Clarity
• Little abstraction

6. 6© 2017 IDERA, Inc. All rights reserved. Proprietary and confidential.
PARTIAL BUS ARCHITECTURE
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Account_Coverage X X X X X
Account_Diagnosis X X X X X
Account_Procedure X X X X X X
Account_Summary X X X X X X X X X X X X
Billing_Tx_Detail X X X X X X X X X X
Encounter X X X X X X

7. 7© 2017 IDERA, Inc. All rights reserved. Proprietary and confidential.
PARTIAL BUS ARCHITECTURE (CONT.)
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Order_Medication X X X X X X
Order_Procedure X X X X X X X
Patient_Appt X X X X X X
Provider_Avail X X X X X X
Readmission X X X X X X X X X X
Referral X X X X X X X X X

8. 8© 2017 IDERA, Inc. All rights reserved. Proprietary and confidential.
SAMPLE DIMENSIONS
 Account
 Appointment
 Benefit_Plan
 Carrier
 Cost_Center
 Coverage
 Date
 Department
 Diagnosis
 DRG
 Encounter

9. 9© 2017 IDERA, Inc. All rights reserved. Proprietary and confidential.
SAMPLE DIMENSIONS (CONT.)
 Medication
 Patient
 Payor
 Pharmacy
 Problem_List
 Procedure
 Provider
 Revenue_Code
 Unavailable_Reason
 Vendor

10. 10© 2017 IDERA, Inc. All rights reserved. Proprietary and confidential.
SAMPLE FACTS
 Account_Coverage – patient account and coverage data
 Account_Diagnosis – data about diagnoses
 Account_Procedure – procedures for an account
 Account_Summary – an accumulating snapshot fact
 Billing_Transaction_Detail – hospital billing transactions
 Encounter – the current state of a patient encounter
 Order_Medication – data for medications
 Order_Procedure – data for procedures and lab orders
 Patient_Appointment – data for appointments
 Provider_Availability – time slots for a provider’s schedule
 Readmission – data for hospital readmission per ACA
 Referral – a patient handoff from one provider to another

11. 11© 2017 IDERA, Inc. All rights reserved. Proprietary and confidential.
SAMPLE SUBJECT AREA

12. 12© 2017 IDERA, Inc. All rights reserved. Proprietary and confidential.
SAMPLE SUBJECT AREA

13. 13© 2017 IDERA, Inc. All rights reserved. Proprietary and confidential.
SAMPLE SUBJECT AREA

14. 14© 2017 IDERA, Inc. All rights reserved. Proprietary and confidential.
STAFFING BREAKDOWN
 4 managers
 2 data modelers + advanced tools/techniques
 6 ETL
 5 business analysts
 1 DBA
 1 metadata
 2 secretary
 2 legacy software

15. 15© 2017 IDERA, Inc. All rights reserved. Proprietary and confidential.
RETROSPECTIVE
 Too much of “build it and they will come”
 There were errors in staging data!
 There was code review for ETL scripts
• I’m not confident that the ETL scripts were correct
• We should have used SQL to validate ETL scripts
 Agile analytics
• We had complex SQL for calculating readmissions
• The SQL was validated as correct
• The legacy software was wrong!
 Issue of Clarity rollup vs DW rollup

16. 16© 2017 IDERA, Inc. All rights reserved. Proprietary and confidential.
THANKS!
Any questions?
You can find me at:
@michaelrblaha
blaha@computer.org
www.superdataguy.com