2. Introduction
Interim Executive
Director
Networked Data
Warehousing
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• Expertise
• Leading large-scale data integration & analytic programs
• Understanding domain area needs
• Engineer practical, technology solutions using health
technology standards
• Key Accomplishments
• Nationwide syndromic surveillance system with 500+
hospitals
• Developing community-based population health solutions
• Professional Qualifications
• Engineering background with a bachelor of arts in
business administration in information systems.
• Certified Six Sigma and Project Management Professional
• Member HIMSS Clinical and Business Intelligence
Committee
• Co-Chair of the HIMSS Data and Technology Task Force
3. Topic Flow
• Enterprise DW and Analytics Team
• UCSF’s EDW Strategy
• Epic Cogito DW
• Questions
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4. Enterprise DW and Analytics Team
Objectives
• Create a team with a passion for understanding and
managing enterprise data
• Partner with domain areas to understand their data and
analytic needs
• Implement highly professional data management
practices
– Well managed data architecture
– Comprehensive and high quality metadata management
– Strong data security and controls
• Provide domain areas:
– Easy and secure access to enterprise data
– Expertise in developing analytic work products
– Expertise on BI and analytic technologies
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5. Increase Analytics Maturity
Optimize
What is my best
alternative?
Precision medicine
Forecast &
Predict
What happens if
trends continue?
Population management and
value-based reimbursements
Decision
Support
What should I do?
Applying evidence-based
guidelines at the POC
Statistical
Analysis
Why is this
happening?
Determining evidence-based
guidelines
Metrics and
Dashboards
Where is the
problem?
Quality and safety KPIs and
benchmarks
Reporting What happened? Standard retrospective reports
INCREASINGVALUE
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6
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6. Domains
Research
Patient Care Finance/Admin
Education HR/Payroll
• IDR / UCReX
• RDB
• Oncore / REDCap
• Clinical data marts
• APeX, Clarity,
Cogito EDW
• Axiom Dental
• ACO data
• UCALL / OmniView
• DART
• Campus Fin DW
• Registration System
• Course Evaluation
• Grades Mgmt
• Peoplesoft HRMS
• Peoplesoft Payroll
• OLPPS
/ Integration
Data Governance
Metadata
Management
Master Data
Management
/ Management
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8. Epic Cogito (ko-GEE-toe) DW
• An analytical database combining Epic and Non-Epic
Data
– Pre-defined healthcare data model
– Seamless flow of Epic data from APeX Clarity database
– Extensible to include non-APeX data
• Common data model across Epic Customers
– Facilitates collaboration with other Epic customers (e.g.,
Other UCs, Children’s of Oakland, etc.)
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9. Uses for Cogito EDW
• Research
– Sophisticated cohort selection (RDB)
– Quality and clinical research
• Population Health
– Combining APeX clinical data with external clinical, claims
and patient satisfaction data
• Performance Improvement
– Monitoring clinical and operational metrics for APeX and
non-Apex data
• Streamlined reporting for APeX data
– Highly simplified version of Clarity
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10. Information Flow
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Chronicles
Cache
Chronicles
Cache
95,000+
Data Elements
Reporting Workbench
Real-time operational reporting
Clarity
SQL Server
Clarity
SQL Server
12,000+ Tables
125,000+ Columns
Clarity Reporting
Enterprise reporting
Cogito DW
SQL Server
Cogito DW
SQL Server
19 Fact Tables
76 Dimensions
Data Warehouse Reporting
BI and Analytical Reporting
14. Data Not Currently in Cogito*
• Ambulatory
– Provider metrics
– Order sets
• Anesthesia
• ED – Chief Complaint
• Inpatient
– Clinical Notes
– Medication Administration
• Operating room administration
• Obstetrics and Labor & Delivery
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* Representative list of key data items14
15. Cogito DW Dimensions*
Admission Profile Department Lab Component
Appointment Diagnosis Lab Result
Billing Area Diagnosis Hierarchy Medication
Billing Account Discharge Profile Patient Attributes
Billing Service DRG Patient
Billing Status Duration Procedure
Billing Procedure Employee Provider Attributes
Cost Center Encounter Provider
Coverage Encounter Profile Reaction Profile
Date Guarantor Visit Attributes
Time of Date Immunization Visit Profile
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*Partial, representative list15
17. Cogito Timeline
• Version 8
– Additional Epic Data
• ED
• Surgery
• Coded Procedures
– Non-Epic Data
• CMS Medicate Shared Savings Plan Claims
• Press Ganey
– Additional Universes – Received Claims, Patient
Satisfaction17
18. Summary
• Creating an Enterprise DW and Analytics Team
– Coordinate UCSF data architecture, metadata definitions
and serve as a resource for available data sources
• Cogito Data Warehouse is being implemented
– Research Data Browser 1st
use case
– Understandable set of data structures
– Extensible data model
– Facilitates sharing of data with other Epic sites
– Epic continues to refine and enhance
• More information
– Doug Berman - Academic Research Systems
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5 Major Domains:
Research
Patient Care
Finance/Admin
Education
GL/Payroll
Domain foci will include UCSF internally generated data as well as external data such as data from research collaborators, ACO partners or external benchmarks.
Enterprise Data Warehouse and Analytics team will have staff who work with domain areas to understand and document their source systems, data staging areas, data warehouse, data marts and key analytics. Doug Berman and the Academic Research Systems are responsible for interacting with UCSF’s Research community and performing this function.
The Enterprise Data Warehouse and Analytics team will also be responsible for documenting data flow between and among both internal systems and with external systems.
To help tie together and manage all these data sources and repositories the Enterprise DW and Analytics team will be responsible for building a fabric of processes and technologies.
It starts with implementing a Data Governance process. This process …..
The data governance process will generate a tremendous amount of data about UCSF’s data, or metadata. The Enterprise DW and Analytics team will put in place a meta data management system to store and manage these data. This will allow UCSF researchers and staff to get up to date information on key data resources and be able to benefit from the understanding of those data created by others before them.
Finally, the Enterprise DW and Analytics team will implement Master Data Management capabilities. This provides the ability to match data across systems and domain areas.
Master Data includes:
Identifiers: patient, provider, payer, facility
Codes: GL account number, lab test, department, result status
Hierarchies: reporting relationship, service line rollup
Mappings: ICD-9 to ICD-10 code, prior GL code to current GL code
As you can imagine, this is a monumental task. The approach that the EDW and Analytics team will take is to incrementally build these capabilities while meeting pressing research, clinical, financial, educational and human resource needs.