A Data Warehouse Architecture for Clinical Data Warehousing Tony R. Sahama and Peter R. Croll
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Transcript of A Data Warehouse Architecture for Clinical Data Warehousing Tony R. Sahama and Peter R. Croll
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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 [email protected]
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© 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
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© 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
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© 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
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© 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
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© 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
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© 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
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© 2006
Department of Computing Science
CMPUT 605
Different DW Architectures (Sen & Sinha 2005)
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© 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
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© 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
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© 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
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© 2006
Department of Computing Science
CMPUT 605
Oncology Patient Management
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© 2006
Department of Computing Science
CMPUT 605
Mental Health Patient Management
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© 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
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© 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
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© 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
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© 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
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© 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.
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© 2006
Department of Computing Science
CMPUT 605
Thank You For Your Attention!