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A Data Warehouse Architecture for Clinical Data Warehousing Tony R. Sahama and Peter R. Croll

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.

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A Data Warehouse Architecture for Clinical Data Warehousing Tony R. Sahama and Peter R. Croll

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  1. Amit Satsangi amit@cs.ualberta.ca A Data Warehouse Architecture for Clinical Data WarehousingTony R. Sahama and Peter R. Croll CMPUT 605

  2. CMPUT 605 Focus • Why are Clinical Data Warehouses (CDW) needed? • Issues in their construction • Design & design-choices in the construction of a CDW

  3. 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

  4. 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

  5. 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

  6. 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

  7. 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

  8. CMPUT 605 Different DW Architectures (Sen & Sinha 2005)

  9. 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

  10. 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

  11. CMPUT 605 • Several data marts to include different administration and management operations —Summary reports —Monitoring of clinical outcomes by management

  12. CMPUT 605 Oncology Patient Management

  13. CMPUT 605 Mental Health Patient Management

  14. 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

  15. 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

  16. 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

  17. 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

  18. CMPUT 605 Steps in creation of CDW • Step 4: Data Mining —Tools integrable with or within SAS used EM, EG etc.

  19. Thank You For Your Attention! CMPUT 605

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