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A Cancer Center Vision:  The development and state-wide adoption of a centralized scientific and clinical Data Warehouse  The Future of Personalized Medicine Edward Martinez (Former VP & CIO of Moffitt Cancer Center)  Case Study
What is Personalized Medicine? ,[object Object],[object Object],[object Object],[object Object]
The Problem with Contemporary Cancer Care ,[object Object],[object Object],[object Object],[object Object],[object Object]
Current Approach to Phase II Trials is Hit or Miss  ,[object Object],[object Object],[object Object],[object Object],[object Object]
The Solution:  Total Cancer Care  Total Cancer Care  Personalized Cancer Care  2010  Study large populations…   Identify cancer sub-populations…   Develop therapies for sub-populations of individuals…
Moffitt’s Total Cancer Care?  ,[object Object],[object Object],[object Object],[object Object],[object Object]
Hypothesis/Plan ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
New  Standard  Of Care  Personalized  Molecular  Medicine  Signatures  Central Data Warehouse  50,000 Patients  (Primaries + Metastatic Biopsies)  QOL, Clinical  Tissue  Survivorship  Surveys  Banking  Pipeline  Evidence  IT  Quality  Therapeutic  Based  Improvement  Solutions  Trials  Medicine  TCC  Affiliate  MCC  Consortium  Network  (Resections + Trials)  (Metastatic Biopsies)  The Building Blocks of Total Cancer Care
TCC Mission & Value  ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Potential to capture 50% of cancer cases In second highest cancer burden State  State-wide affiliations allow for sharing of clinical and genomic information
Strategic Approach ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Data Warehouse Strategy ,[object Object],[object Object]
Data  Warehouse  Design Objectives ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Operational Data Flow Challenges Tissue Tracker Cerner Surginet OnCore HPV Colon 400 Capstone Galvanon MDDB Cancer  Registry Capstone Micro Array LabVantage SurgiNet PathNet PharmNet Staging Area Data is extracted from source systems and delivered here  ETL ETL ETL DR ETL DM ETL Data Warehouse Data is transformed into Standard Structures Tissue Analysis Person Tissue Events EMR Micro Array Data Mining Genetic Analysis EMR Analysis Event Analysis
Person-Centric Model ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Person Linkage of Person to other data through a unique identifier Independent of MRN, SS#, or any other known identifier
Data Elements – Sample Person Biospecimen EMR Trials Age Gender Race Ethnicity Source Category Gross Diagnosis Preparation Type Assay Results Diagnosis Lab Results Medications Surgeries Scheduling Physician Protocols Studies Phases Participation Consent Therapy Event Type Hierarchy  Attribute Result Date Association Chip Type Array Type Probe set Probe Signal Level Genes Present QC Metrics Form Observation Answer Language Skip Pattern MRN SSN Name Birth Date Address Date of Death Phone Events Questionnaire MicroArray PHI
Pre- Warehouse ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Queries are limited to a single system.  Getting results that span more than one system requires extensive time and effort, and in some cases may not even be possible.
Post Warehouse ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Queries span multiple systems and provide a holistic view of data
Same Data – User Categories Nurse Bio-Informatics Scientist Data Manager Clinician Researcher
Classes of Data Users Executive User Functional User Power User Highly Aggregated More Detail Complete Raw Data ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Vision: Aligned Data Sources Cerner OPTX Tissue Bank Lawson Cancer Registry CT Admin Affiliate Systems Sunquest Research Systems Other Databases Legacy Database
Vision: Disparate Sources    CUI Extract, Transform, Load Common Data Semantics, Standards Interactive Presentation Lab or Medical Instruments IVR Hand-Held Databases Other Systems DB
Logical View: Multi-Dimensional Data • Data on each Clinical Event • by Program • by Trial • by Site • by Investigator • by Patient Clinical Data Warehouse Facts Dimensions Integrated Clinical Data Mart Facts Dimensions Detailed Clinical Data Facts Treatment Descriptors Trial Descriptors Site Descriptors Patient Descriptors Physician / Investigator Descriptors ,[object Object],[object Object],[object Object],• Tx Group • Con Meds • Prior Diagnoses • Outcomes • Phase • Protocol • Type • Location • Contract Status • IRB • Location  • Contract Status • Type • Fee Structure ,[object Object],• Demographics
Logical View: Shared Meta-Data Clinical Outcomes Facts Patient Descriptors Location Descriptors Protocol Descriptors Investigator Descriptors Date / Time Descriptors Data Collection Facts Patient Satisfaction Facts Trial Enrollment Facts Data Warehouse Facts Dimensions Data Warehouse Facts Dimensions Data Warehouse Facts Dimensions Data Warehouse Facts Dimensions
Logical View: Process-Driven View Per Protocol Per Study Per Site Per Patient Patient Screened Patient Randomized Data Set Received CRFs Received Data Set Coded Data Set Queries CRFs Certified Per Visit Patient Dropped Data Set Certified Safety Review Patient on Listing Patient Certified Patient Withdrawn
Data Pipeline Extraction / Capture Mapping  Transformation OpTx Trial Metrics Affiliate Systems Cancer Registry Cerner (lists, code maps,  dictionaries) Subject Area Data Marts Master Reference Data Analytics / Dashboards Standard Reports Live (ad hoc) reports Alerts Source Data Data Preparation Data Provisioning Applications Data Cubes 01 02 M F IF(sum(a,b) . . .
Enterprise Data Strategy Requirements: Scientific, Business, Functional, Technical, Data Implementation Plan Technology Architecture Vision
Data Exchange Interfaces
Data Warehouse Architecture
Challenges ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Touch Screen Technology
Multi-Dimensional Clinical Data
Multi-Dimensional Clinical Data
Integrating Genomic and Clinical Data
Data Governance Data Stewards Data Owners Data Governance and Monitoring Committee (DGMC) Data Users IT Executive Sponsors
Data Governance Strategy ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Phased Implementation OCT 2005 FEB 2006 100 days 6 months Ongoing Study 1 Study 3 Study 2 Release 1 Data Warehouse V0.1 Data Access AUG 2006 Release 2 Surgery Billing Intake Survey Study 1 Study 3 Study 2 Release 1 Data Warehouse V0.1 Data Access Microarray Data Warehouse  V1.0 Specimen Bank Clinical Trials EMR Data Access AUG 2006 Release 3 Clinical Trials Data Access LIMS Partners Affiliates Release 2 Surgery Billing Intake Survey Study 1 Study 3 Study 2 Release 1 Data Warehouse V0.1 Data Access Microarray Data Warehouse  V1.0 Specimen Bank Clinical Trials EMR Data Access Data Warehouse  V1.1 ,[object Object],[object Object],[object Object],[object Object],[object Object],Cancer Registry AUG 2006
Three Portals to Data Patient View  Clinician View  Researcher View  TCC Multi-Dimensional Data  Warehouse  View my lab  Clinical Data  Gene Expression  Results online  • Data guides Rx  Clinical Data Signatures
Building on the Foundation  Genomic Data Biospecimen Data Quality P4P Costs Risk Factors Integrated Data System
How Pharmacogenomics Can Streamline Clinical Trials & Build Pharma Partnerships Traditional Clinical  TCC Trial  Trials  Responders Only  Broad Patient Population  10-12 Years  3-5 Years
Trial Enrichment 300 patients profiled  in database  ,[object Object],[object Object],[object Object]
PLASMA  EXPRESSION  URINE  PROTEOMICS  PROFILNG  PROTEOMICS  BIOINFORMATICS  ( PREOP/postop)  CONSULTING  COMMERCIAL OPPORTUNITIES  CPG  CGH/LOH  METHYLATION  AGILENT/  (AGILENT  AFFY SNP  CHIP BASED)  M 2 GEN  TCC DATA WAREHOUSE  TISSUE  SINGLE CELL  PHOSPHO-  SEQUENCING  PROTEOMICS  (SOMATIC  (SHOTGUN)  MUTATIONS)  PARAFFIN  Molecular  RTPCR  Imaging  TMA  HIGH THROUGHPUT TECHNOLOGIES  IN HOUSE VS. OUTSOURCE
Lessons Learned to Date  ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Quotes from: ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Edward Martinez [email_address] 813-420-4703

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The Future of Personalized Medicine

  • 1. A Cancer Center Vision: The development and state-wide adoption of a centralized scientific and clinical Data Warehouse The Future of Personalized Medicine Edward Martinez (Former VP & CIO of Moffitt Cancer Center) Case Study
  • 2.
  • 3.
  • 4.
  • 5. The Solution: Total Cancer Care Total Cancer Care Personalized Cancer Care 2010 Study large populations… Identify cancer sub-populations… Develop therapies for sub-populations of individuals…
  • 6.
  • 7.
  • 8. New Standard Of Care Personalized Molecular Medicine Signatures Central Data Warehouse 50,000 Patients (Primaries + Metastatic Biopsies) QOL, Clinical Tissue Survivorship Surveys Banking Pipeline Evidence IT Quality Therapeutic Based Improvement Solutions Trials Medicine TCC Affiliate MCC Consortium Network (Resections + Trials) (Metastatic Biopsies) The Building Blocks of Total Cancer Care
  • 9.
  • 10. Potential to capture 50% of cancer cases In second highest cancer burden State State-wide affiliations allow for sharing of clinical and genomic information
  • 11.
  • 12.
  • 13.
  • 14. Operational Data Flow Challenges Tissue Tracker Cerner Surginet OnCore HPV Colon 400 Capstone Galvanon MDDB Cancer Registry Capstone Micro Array LabVantage SurgiNet PathNet PharmNet Staging Area Data is extracted from source systems and delivered here ETL ETL ETL DR ETL DM ETL Data Warehouse Data is transformed into Standard Structures Tissue Analysis Person Tissue Events EMR Micro Array Data Mining Genetic Analysis EMR Analysis Event Analysis
  • 15.
  • 16. Data Elements – Sample Person Biospecimen EMR Trials Age Gender Race Ethnicity Source Category Gross Diagnosis Preparation Type Assay Results Diagnosis Lab Results Medications Surgeries Scheduling Physician Protocols Studies Phases Participation Consent Therapy Event Type Hierarchy Attribute Result Date Association Chip Type Array Type Probe set Probe Signal Level Genes Present QC Metrics Form Observation Answer Language Skip Pattern MRN SSN Name Birth Date Address Date of Death Phone Events Questionnaire MicroArray PHI
  • 17.
  • 18.
  • 19. Same Data – User Categories Nurse Bio-Informatics Scientist Data Manager Clinician Researcher
  • 20.
  • 21. Vision: Aligned Data Sources Cerner OPTX Tissue Bank Lawson Cancer Registry CT Admin Affiliate Systems Sunquest Research Systems Other Databases Legacy Database
  • 22. Vision: Disparate Sources  CUI Extract, Transform, Load Common Data Semantics, Standards Interactive Presentation Lab or Medical Instruments IVR Hand-Held Databases Other Systems DB
  • 23.
  • 24. Logical View: Shared Meta-Data Clinical Outcomes Facts Patient Descriptors Location Descriptors Protocol Descriptors Investigator Descriptors Date / Time Descriptors Data Collection Facts Patient Satisfaction Facts Trial Enrollment Facts Data Warehouse Facts Dimensions Data Warehouse Facts Dimensions Data Warehouse Facts Dimensions Data Warehouse Facts Dimensions
  • 25. Logical View: Process-Driven View Per Protocol Per Study Per Site Per Patient Patient Screened Patient Randomized Data Set Received CRFs Received Data Set Coded Data Set Queries CRFs Certified Per Visit Patient Dropped Data Set Certified Safety Review Patient on Listing Patient Certified Patient Withdrawn
  • 26. Data Pipeline Extraction / Capture Mapping Transformation OpTx Trial Metrics Affiliate Systems Cancer Registry Cerner (lists, code maps, dictionaries) Subject Area Data Marts Master Reference Data Analytics / Dashboards Standard Reports Live (ad hoc) reports Alerts Source Data Data Preparation Data Provisioning Applications Data Cubes 01 02 M F IF(sum(a,b) . . .
  • 27. Enterprise Data Strategy Requirements: Scientific, Business, Functional, Technical, Data Implementation Plan Technology Architecture Vision
  • 30.
  • 34. Integrating Genomic and Clinical Data
  • 35. Data Governance Data Stewards Data Owners Data Governance and Monitoring Committee (DGMC) Data Users IT Executive Sponsors
  • 36.
  • 37.
  • 38. Three Portals to Data Patient View Clinician View Researcher View TCC Multi-Dimensional Data Warehouse View my lab Clinical Data Gene Expression Results online • Data guides Rx Clinical Data Signatures
  • 39. Building on the Foundation Genomic Data Biospecimen Data Quality P4P Costs Risk Factors Integrated Data System
  • 40. How Pharmacogenomics Can Streamline Clinical Trials & Build Pharma Partnerships Traditional Clinical TCC Trial Trials Responders Only Broad Patient Population 10-12 Years 3-5 Years
  • 41.
  • 42. PLASMA EXPRESSION URINE PROTEOMICS PROFILNG PROTEOMICS BIOINFORMATICS ( PREOP/postop) CONSULTING COMMERCIAL OPPORTUNITIES CPG CGH/LOH METHYLATION AGILENT/ (AGILENT AFFY SNP CHIP BASED) M 2 GEN TCC DATA WAREHOUSE TISSUE SINGLE CELL PHOSPHO- SEQUENCING PROTEOMICS (SOMATIC (SHOTGUN) MUTATIONS) PARAFFIN Molecular RTPCR Imaging TMA HIGH THROUGHPUT TECHNOLOGIES IN HOUSE VS. OUTSOURCE
  • 43.
  • 44.