a data warehouse architecture for clinical data warehousing tony r. sahama and peter r. croll

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Amit Satsangi amit@cs.ualberta.ca. A Data Warehouse Architecture for Clinical Data Warehousing Tony R. Sahama and Peter R. Croll. Focus. Why are Clinical Data Warehouses (CDW) needed? Issues in their construction Design & design-choices in the construction of a CDW. - PowerPoint PPT Presentation

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Faculty of Computer Science

CMPUT 605 December 06, 2007February 11,

2008© 2006

A Data Warehouse Architecture for Clinical Data Warehousing

Tony R. Sahama and Peter R. Croll

Amit Satsangiamit@cs.ualberta.ca

© 2006

Department of Computing Science

CMPUT 605

Focus

Why are Clinical Data Warehouses (CDW) needed?

Issues in their construction

Design & design-choices in the construction of a

CDW

© 2006

Department of Computing Science

CMPUT 605

Why Clinical Data Warehouse?

Efficient Storage

Uniformity in storage and querying of data

Timely analysis

Quality of decision making and analytics

—Decision based on larger sized datasets

—More accurate information

—Better strategies and research methods

© 2006

Department of Computing Science

CMPUT 605

Why Clinical Data Warehouse?

Measurement of the effectiveness of treatment

Relationships between causality and treatment protocols

Safety

Management

—Breakdown of cost, and charge information

—Forecasting demand

—Better strategies and research methods

© 2006

Department of Computing Science

CMPUT 605

Some Facts…

Large volume of data distributed in a number of

small repositories—”islands” of information

Data has great scientific and medical insight

Great potential for people practicing clinical

medicine

© 2006

Department of Computing Science

CMPUT 605

Issues

Heterogeneity—different clinical practices e.g. public vs. private hospitals

Data Location

Technical platforms & data formats

Organizational behaviors on processing the data

Varying cultures amongst data management population

© 2006

Department of Computing Science

CMPUT 605

Past efforts

Szirbik et al. – Medical data Warehouse for elderly patients

—Six methodological steps to build medical data warehouses for research. International Journal of Medical Informatics 75 (9): 683-691

Used Rational Unified process (RUP) framework

Identification of current trends (critical requirements of future)

Data Modelling

Ontology Building

Quality Management and exception handling

© 2006

Department of Computing Science

CMPUT 605

Different DW Architectures (Sen & Sinha 2005)

© 2006

Department of Computing Science

CMPUT 605

Design and Planning

Business Analytics Approach—understand the key

processes of the business

DW architect + Business Analyst + Expected Users

Understand Key business processes + the

questions that would be asked of those processes

Analysis might be conducted on demographic,

diagnosis, severity of illness, length of stay

© 2006

Department of Computing Science

CMPUT 605

Approach

Integration of data from two Biomedical Knowledge Repositories (BKR’s)—Oncology & Mental care

Used SAS Data Warehouse Administrator (SAS 2002)

—Flexibility to integrate external data repositories

—Hassle-free ETL

—Analytics with Data Miner

—Reporting using SAS Enterprise Guide (EG)

Operational Data Store Architecture & Distributed Data Warehouse Architecture

© 2006

Department of Computing Science

CMPUT 605

Several data marts to include different administration and management operations

—Summary reports

—Monitoring of clinical outcomes by management

© 2006

Department of Computing Science

CMPUT 605

Oncology Patient Management

© 2006

Department of Computing Science

CMPUT 605

Mental Health Patient Management

© 2006

Department of Computing Science

CMPUT 605

Data Transformation

Source systems CDW (ETL— Extraction-

Transformation-Load)

Data preparation & Integration takes 90% of the

effort in a given CDW project

Excel, SAS External File Interface (EFI) & SAS

Enterprise Guide (EG) used to clean the data

© 2006

Department of Computing Science

CMPUT 605

Steps in creation of CDW

Step 1: Data imported in SAS

—Standardization into SAS table format

—Opportunity for data manipulation—create/delete columns

Step 2: Creation of metadata using Operational Data definition

Step 3: Creation and loading of Data Tables

—Different tables for predictive and Database analysis

—Creation of multi-dimensional cubes

© 2006

Department of Computing Science

CMPUT 605

Discussion

Data acquisition step took very long—very little

time left for cleaning, transformation

Not enough time left to refine the shared

environment (no modifications to their interface

implementation etc.)

Security issues of federated Data Warehouses—

anonymization of records

© 2006

Department of Computing Science

CMPUT 605

Discussion

SAS EM used to interpret relationships between

seemingly unconnected data

Newer CDW models coming from Case-based, Role-

based & evidence-based data structures need to be

incorporated

© 2006

Department of Computing Science

CMPUT 605

Steps in creation of CDW

Step 4: Data Mining

—Tools integrable with or within SAS used EM, EG etc.

© 2006

Department of Computing Science

CMPUT 605

Thank You For Your Attention!

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