Biehl (2012) implementing a healthcare data warehouse
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Implementing a Healthcare Data Warehouse in One Year (or Less)
DISCLAIMER: The views and opinions expressed in this presentation are those of the author and do not necessarily represent official policy or position of HIMSS.
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Conflict of Interest Disclosure Richard E. Biehl, Ph.D.
• Ownership Interest: Richard is the sole proprietor of Data-Oriented Quality Solutions (DOQS), an IT/Quality consulting practice founded in 1988 and operating out of Orlando, Florida, USA.
• Consulting Fees: Richard earns approximately 15% of his income from consulting engagements that involve the heuristics included in this presentation.
• Other: The heuristics in this presentation can be immediately and directly implemented by attendees. Nothing has been held back that would necessitate engaging DOQS for implementation.
© 2012 HIMSS
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Learning Objectives 1. Assemble a small agile team for rapid and
focused warehouse implementation
2. Identify how to eliminate database and functional dependency project bottlenecks
3. Plan a multi-iteration project approach that delivers functionality quarterly
4. Diagnose organizational and resource issues that might stand in the way
5. Select the most powerful clinical data sources for the initial implementation
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Benefits Typically Sought
• Integrated data repository
• New and diverse access paths
• Enhanced data quality
• Early access to complicated data
• Scalable to include more sources
• Semantic pathways into data
• Longitudinal phenotypic data
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Teaming & Responsibilities
Data Governance
Group
Support Team
PROJECT TEAM
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Project Team (3-5 FTE)
• Data Warehouse Architect (.5 FTE)
• Business/Data Analysts (1-2 FTE)
• ETL Developers (1-2 FTE)
• Application Developer (.5 FTE)
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Support Team (1.5 FTE)
• Project Management (.25 FTE)
• Data Base Administrator (.25 FTE)
• Data Administrators (.25 FTE)
• HL7 Integration Specialist (.25 FTE)
• Business Intelligence Designer (.25 FTE)
• Organizational Change Agent (.25 FTE)
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Data Governance Group
• Promote the voice of the customer
• Ensure timely addressing of issues
• Advise owners, stewards, & users
• Enhance the quality of data
• Promote innovative uses for data
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Analysis
Database Design
Query Design
ETL Design & Build
Eliminate Bottlenecks
Data Queries Flow Controls People Goals
Maps Extraction Controls Duplicates Missing flows Validation Traceability
Modeling Storage Integration Keys Mapping Indexes Partitioning
Security Privacy Queries Usage Controls
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Analysis
Database Design
Query Design
ETL Design & Build
Eliminate Bottlenecks
Strong Functional Dependencies
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Standardized ETL Design
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Analysis
Database Design
Query Design
ETL Design & Build
Analysis Database
Design
Query Design
ETL Build Earlier Implementation
Eliminate Bottlenecks
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Development Iterations
• Alpha Version (end of January)
– Proof-of-Concept (1st week)
• Beta Version (mid-March)
• Gamma Version (early September)
• Release 1.0 (mid-December)
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ALPHA VERSION
• Implements just enough to illustrate core capabilities
• Loads small sampling of limited data
• Enables a way to visualize results by actually looking at a core subset
• Allow 3-4 weeks for development
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BETA VERSION
• Most of the required functionality
• A lot more data
• Facilitates planning & setup
• A show-and-tell tool for the team, not a self-service tool for users
• Ready about 2-3 months after the Alpha version is complete
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GAMMA VERSION
• A functionally complete system
– typically still lacking much of the data that would need to be loaded in order to consider it production-ready
Key issue: Finalizing HIPAA controls
• Typically ready 3-4 months after the Beta version is completed
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RELEASE 1.0
• Placed into production as a fully functional and secure data warehouse
– all first release scoped data loaded up to the implementation date, and
– an operating ETL environment that will keep that scoped data current
• Launch is about a year after the project started
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ALPHA BETA GAMMA Release 1.0
Analysis
Database Design
Query Design
ETL Build
Iterative Parallelism
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Diagnose Issues
• Knowledge of source data
• History vs. prospective data
• Access to source data structures
• Data capture & conversion tools
• System time vs. clinical time
• Physical operating environment
• Query performance (memory, indexing)
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Select Sources
• Admission-Discharge-Transfer (ADT)
• Diagnoses & Procedures
• Allergy & Problem Data
• Lab Orders & Results
• Medication Orders & Results
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Sources to Avoid
• Large text or report sources
– HIPAA issues
– Natural language issues
• Overly complex data
– Allow learning period
– Anticipatory discussions only
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Anticipate Pitfalls
• Difficulty in making the shortened timeframe real to stakeholders
• Extensive lead times on setup of operational environment
• Tendency to defer access and privacy discussions until too late in project
• Window of opportunity needs to be wide open across the organization
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Questions? You are welcome to contact me for additional information at any time:
Additional questions…..
Clinical & Business Intelligence Knowledge Center Today, 11:45a - 12:15p Booth 13247
Richard E. Biehl, Ph.D.
Data-Oriented Quality Solutions [email protected]
Booth 13657