Faculty of Computer Science © 2006 CMPUT 605February 11, 2008 A Data Warehouse Architecture for Clinical Data Warehousing Tony R. Sahama and Peter R. Croll Amit Satsangi

© 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):  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!