edw 09112014 01a - medicine.utah.edu · • 2010-‐date epic for ... cognos business intelligence...

9/11/14

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UUHC Enterprise Data Warehouse Overview & 2014 Update

Vikrant G. Deshmukh, Ph.D. Cheri Y. Hunter

Michael B. Strong, M.D.

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Acknowledgements •  Dr. Jim Turnbull •  Jim Livingston •  Travis Gregory •  Nancy Brazelton •  Ming-‐Chieh Tu •  Reed Barney •  Cary MarVn •  Micky Daurelle •  Charlton Park •  Darryl Barfuss

•  Dr. Michael White •  Dr. Reid Holbrook •  Dr. Jeffrey Lin •  Kip Williams •  Joshua Spuhl •  Mingyuan Zhang •  Grant Lasson •  Dr. Bruce Bray •  Dr. Wendy Chapman

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Acknowledgements

•  Database Services •  Clinical InformaVon Systems (EMR, Other Ancillary ApplicaVons)

•  Enterprise IntegraVon (HL7)

•  Enterprise Server Management

•  Storage Management Services

•  Decision Support •  Quality and PaVent Safety

•  Strategic Planning •  University of Utah Medical Group

•  Utah PopulaVon Database

•  Biomedical InformaVcs

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Outline

•  IntroducVon •  Infrastructure •  Metadata •  Quality •  IntegraVon •  Access

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INTRODUCTION IntroducVon

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What is a Data Warehouse?

“A data warehouse is a subject-‐oriented, integrated, 6me-‐variant and non-‐vola6le collec6on of data used in an organiza6on”

What is a Data Warehouse? William H. Inmon, Prism, Volume 1, Number 1, 1995.

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CharacterisVcs Characteris2c Descrip2on Example Subject-‐oriented

Data available on an enVre subject area

Visits, Orders, Respiratory, etc.

Integrated Data from mulVple sources integrated in a single place

Orders data integrated across Cerner, Epic

Time-‐variant Data from a specific Vme-‐period

Nursing documentaVon data in the only available from 2007 onwards

Non-‐volaVle New data constantly added, but old data is not deleted

Financial records from two reVred systems (Allegra & IDX) and current (Epic)

What is a Data Warehouse? William H. Inmon, Prism, Volume 1, Number 1, 1995. 8

Enterprise Data Warehouse

An Enterprise Data Warehouse (EDW) contains all the relevant current and historical data for the enVre organizaVon.

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Data-‐marts

A data mart is a logical subunit of a data warehouse which contains data related to a single subject area (e.g. Orders), department/service line (e.g. Cardiovascular), business unit, etc.

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High Level Overview

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Brief History of Systems

Transac2onal Systems •  1993-‐1999 OACIS (inpaVent) •  1995-‐date Epic Care Ambulatory •  1995-‐2010 Allegra •  1995-‐2010 IDX •  1999-‐2003 e-‐Chart (inpaVent) •  2003-‐2014 Cerner (inpaVent)

–  2007 electronic nursing documentaVon

–  2009 computerized provider order entry

•  2010-‐date Epic for Business •  2014-‐date Epic OneChart

InpaVent

Analy2c Systems •  1990 HL7 integraVon engine •  1993 DisVnct data store

populated using HL7 •  1999 Cognos business intelligence

(BI) suite •  2000 Integrated financial data

(batch & HL7) •  2002 Subject-‐oriented data-‐marts •  2005 Corporate Radar web-‐based

tools for reporVng and dashboards

•  2011 SAP Business Objects Enterprise Business Intelligence

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How is an EDW different? Transac2onal Database (EMR) Analy2cal Database (EDW)

OpVmized for simple, single transacVon queries.

OpVmized for complex queries on paVent populaVons.

Data from a single system. Data from mulVple source systems.

Highly normalized data structures.

De-‐normalized or dimensional data structures.

Raw data on transacVons. Details + aggregate data. Used by clinical, financial and other applicaVon end-‐users.

Used by analyVcal users, researchers, etc.

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Types of Schemas Normalized

Dimensional

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Data Warehousing Approaches Inmon Kimball

Top-‐down: first common relaVonal model, then data-‐marts

Bomom-‐up: first several data-‐marts, then integrate

RelaVonal model – 3rd Normal Form (3NF).

Dimensional model – Star schema and Snowflake.

Time to fully build an EDW and then develop data-‐marts

Faster development cycle – data-‐marts built first

Higher consistency across the EDW PotenVal for silos in separate data-‐marts

Most granular data for reporVng as the need arises.

May require addiVonal development if a dimension was not planned iniVally

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The Data Integration Challenge (1)

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The Data Integration Challenge (2)

•  Three textual descriptions

•  Three numeric properties

•  Three date/time properties

•  Three common attributes of these objects

•  Three attributes that are unique to each object

•  Three uses of each object

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The Data Integration Challenge (3) •  Malus

domestica •  Fruit •  Sweet •  Things you eat •  Made in

California •  On sale at

Smiths (expires 09/13/2014)

•  Volkswagen Beetle

•  Year •  Trim •  License plate# •  VIN# ? •  Things you

drive •  Made in

Germany

•  Citrus sinensis •  Fruit •  Citrus fruit •  OJ! (no, not

Simpson!) •  Sweet •  Tangy •  Things you eat •  Made in Florida

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Data Integration Considerations (1)

•  Like Vs. Unlike attributes •  Old Vs. New sources/attributes •  Necessary Vs. Unnecessary attributes •  Feasible Vs. Unfeasible approaches to

integration

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Data Integration Considerations (2)

•  What is the intended use? •  How soon does it need to be deployed? •  How will the users access the data? •  How reusable are these data for users

across the organization?

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Types of Schemas Normalized

Dimensional

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Example of a Snowflake Model

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INFRASTRUCTURE

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EDW & UUHC Infrastructure

•  Tier-‐1 system •  MulVple copies – redundancy

– Downtown Data Center (primary) – Hot Site Data Center (failover) – Disaster Recovery Site Data Center (scheduled)

•  MulVple nodes – Oracle RAC – 2 Rack-‐mount Servers (768 GB RAM, 16 proc. EA) – 4 Blade Servers (256 GB RAM, 12 proc. EA)

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Resource VirtualizaVon

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Resource UVlizaVon – All

Courtesy: Brad Millem 26

EDW Resource UVlizaVon – Groups

Courtesy: Brad Millem

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METADATA

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What is Metadata?

“Metadata is all the informa6on in the data warehouse environment that is not the actual data itself.”

Kimball, Ralph; Ross, Margy (2008-‐04-‐21). The Data Warehouse Toolkit: The Complete Guide to Dimensional Modeling (Kindle LocaVons 944-‐945). Wiley. Kindle EdiVon.

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Metadata Contributors

•  EMR Developers •  Workflow Engineers •  EMR Implementers •  Clinical Users •  EDW Architects •  Analysts

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Metadata Example (1)

Courtesy: Dr. Reid Holbrook

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Metadata Example (2)

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QUALITY

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Defining Data Quality

•  “Fitness for use” – OperaVons – Decision-‐making – Planning, etc.

Juran's Quality Handbook: The Complete Guide to Performance Excellence; Joseph M. Juran and Joseph A. De Feo; 6th EdiVon (May 2010); McGraw-‐Hill.

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Assessing Quality Criteria Defini2on Example

Completeness Does the data provide a complete picture of events in a given domain?

Visits data-‐mart -‐ complete picture of all encounter/visit-‐level events.

Accuracy How well do the data reflect enVVes and relaVonships in the real world?

CPOE process in the EMR -‐ modeled in the Orders data-‐mart in terms of medicaVon and lab orders, other orders, lab results, medicaVon administraVons, etc.

Timeliness Are the data available at the Vme needed, without delay?

HL7 feed instead of a nightly extract for medicaVon orders

SyntacVc correctness

Are the data captured and stored in a well-‐conceived data-‐model?

Determining the most suitable data-‐model (See 3NF, dimensional, etc.)

SemanVc correctness

Are the data encoded using proper healthcare terms?

Determining if NDCs were used in documenVng medicaVon dispense, administraVon and billing

Adapted from: Clinical and Business Intelligence: Data Management – A Founda6on for Analy6cs – Data Governance, Health InformaVon Management Systems Society, April 2013

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Monitoring Completeness (1)

Courtesy: Joshua Spuhl, Reed Barney 36

Monitoring Completeness (2)

Courtesy: Mingyuan Zhang

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Monitoring Performance

Courtesy: Brad Millem 38

Quality and PaVent Safety

Courtesy: Darryl Barfuss

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INTEGRATION

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Epic EMR IntegraVon

Courtesy: Ryan Derrick

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EMR DownVme SoluVon (1)

Courtesy: Michael R. Donnelly 42

EMR DownVme SoluVon (2)

Courtesy: Michael R. Donnelly

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Novel ApplicaVons & IntegraVon

Courtesy: Michael Sherwood

FAZST

AC Lite – Epic BOE

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Cerner – Allegra & Cerner – Epic

Courtesy: Michael R. Donnelly

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EHR & ancillary

applicaVons

EDW

Provide data to other systems

Enterprise Data Warehouse

Support Research Infrastructures e.g. FURTHeR

Provide data to Research Groups.

e.g. PORC, SORG, UPDB

Quality & Regulatory Agencies

e.g. UHC, CDC, NSQIP

Enterprise ReporVng

Infrastructure

Provide data for reporVng

Quality & PaVent Safety, OperaVonal Excellence

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ACCESS

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Methods

•  Enterprise Business Intelligence – Business Objects

•  Warthog Targeted Chart Review Tool •  Direct access to the database – SQL Navigator, Toad, ODBC, etc.

•  Legacy tools – Corporate Radar •  Other custom tools and applicaVons

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The “Clinical Universe”

Courtesy: Reed Barney

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Drag Objects to Include

Courtesy: Reed Barney 50

Add Filter Objects


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Example Report


A Report With Drill-‐down


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Drill down to individual study


Warthog: IniVal Cohort


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Warthog: Counts by Code


Warthog: Text Search


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Warthog: Text Search Results


Warthog: Text Results Review


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Warthog: Create QuesVons


Warthog: Answer Grid


edw 09112014 01a - medicine.utah.edu · • 2010-‐date epic for ... cognos business intelligence...

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