A tranSMART journey back to the real world
at Deloitte
November 2013

tranSMART
Agenda Topics

•
•
•
•
•

2

About Recombinant By Deloitte
Hot topics from Deloitte client community
Real World Evidence +...
About
Recombinant By Deloitte
Recombinant + Deloitte - Organization Within Deloitte
Deloitte U.S. Firms

Audit &
Enterprise
Risk
Services

Tax

Financia...
Recombinant Vision For Capabilities for Translational Medicine

Payors

-5-

PPT Template_Recombinant by Deloitte.potx

Ph...
General Market Approach

Products / Tools

 Data Strategy
 Data Governance

Key
Capabilities

Target
Markets

 Data War...
-7PPT Template_Recombinant by Deloitte.potx

Client and Partner Ecosystem
Hot Topics
Real World Evidence Objectives

9

Copyright © 2012 Deloitte Consulting LLC. All rights reserved.
Convergence of translational informatics data mining approaches

Observational data mining

High volume assay multimodal d...
(Dan Housman’s)
Translational Research Enterprise Informatics
Infrastructure Maturity Model

Level 7
Level 6
Level 5

Cogn...
Translational Research Enterprise Informatics nfrastructure Maturity Model
Level 7

Cognitive computing: Advanced ‘many to...
Deloitte Health Miner Capabilities

Outcomes
Miner

Population
Miner

Precision
Miner

•

Visually explore populations •

...
Translational Research Platform
Research Portal

Precision Miner

Application Layer

(Miner)

Study
Design
Study
Recruitme...
Real world evidence?
Safety Solution Vision (Example)
Reporting & Analytics

Safety & RWE Platform

Safety Reports
Safety
Case
Reports

Query I...
Teaming to Enable Data Driven Healthcare Improvement

- President Obama to the AMA
June 15, 2009
Chicago, Illinois

Loweri...
Leukemia
• Cancer staging
• Chemo /
radiation
therapy
• Genetic
biomarkers
•
•
•
•

Breast Cancer
Tumor data
Cancer stagin...
Smart Data Approach to the Deloitte-Intermountain consortium
Deloitte is focused on providing life sciences companies with...
Subscription Portal
RWE Reports from RWE Data Sets

Safety DW Report
20

Report based on
purchased claims

Report summariz...
Demos
Population Miner
Outcomes Miner

21

Copyright © 2012 Deloitte Consulting LLC. All rights reserved.
Development of a learning loop that leverages RWE and
the experience of healthcare providers

Evaluating
Evidence
from Stu...
Vision: Leveraging tranSMART + workbench to identify
insights from existing study
 Using Deloitte‟s translational researc...
Vision: Pooled analysis of asthma studies to identify
impact of genomics on treatment outcomes
 Performing a pooled analy...
Vision: Overview of Asthma patients in real-world to
enable better characterization of disease
 Overview of all the asthm...
Vision: Evaluation of outcomes for two asthma
medications in real-world setting
 Comparison of patients on „Drug A‟ versu...
Vision: Evaluation of outcomes for two asthma
medications in real-world setting (contd …)
Evaluating
Evidence from
Studies...
Knowing more about i2b2
data?
Big picture type solution for ‘AMC’ genomics initiatives
Closed Loop

Data Warehouse /
Research Stores

Source Data
Clinic...
Representative View: Select Cohorts via i2b2 Query & Analysis interface

- 30 -
Representative View: i2b2 passport profiles available data

- 31 -
Representative View: i2b2 Passport, cont.

- 32 -
Representative View: i2b2 Passport – summary of data over time

- 33 -
Automated research request data mart production system

- 34 -
Getting AMC registry data
into i2b2
(for tranSMART)
Harvard/CHiP
Jonathan Bickel M.D., M.S., FAAP
REDCap Study Representation

- 36 -
XML to i2b2

REDCap

Archive
(ODM XML)

i2b2
Staging

File system

Oracle Schema
• Ontology
• CRC

EDC
system
of
choice

-...
Choose your Stud(ies)

• Choose
studies to be
imported
• Supply token
to be used
for study
• Click to
initiate export

- 3...
Choose your Stud(ies)

• If a project that
has been
previously
exported is
selected, the
export process
begins by
cleaning...
CDISC ODM XML

<?xml version="1.0" encoding="UTF-8" standalone="yes" ?>
<ODM ODMVersion="1.3.1"
CreationDateTime="2012-02-...
REDCap Study Representation

- 41 -
Modifiers POC (Kimmel Cancer Center)

Informatics Core Director, KCC, TJU Director of
Research Informatics, and Research P...
Jefferson Kimmel Cancer Center - i2b2 Ontology

© 2013. For information, contact Deloitte Touche Tohmatsu Limited.

- 43 -
Jefferson Kimmel Cancer Center
i2b2 v1.6 Biospecimen Ontology

de-identified case
ID

Specimen class
(solid
tissue, fluid,...
Jefferson Kimmel Cancer Center
i2b2 v1.6 Tumor Registry Ontology

Tumor identifier modifier links
different facts about th...
Jefferson Kimmel Cancer Center
i2b2 v1.6 Genomic Profile Ontology

Results for a 58 gene assay panel.

chromosome
number

...
Jefferson Kimmel Cancer Center tranSMART (prototype 1)

© 2013. For information, contact Deloitte Touche Tohmatsu Limited....
The FDA needed to explore new approaches to data management and analysis for
effective evaluation of product safety and ef...
tranSMART 1.2 prep
Demo tranSMART Modifiers
Integrated faceted GWAS results
Cross trials
Query by ‘sequence’
Workspace ‘save’

© 2013. For in...
Switch data center co-locates multiple hosting options for Life Sciences

Deloitte – Internal Users

Deloitte – SaaS Solut...
Disclaimer: This publication contains general information only, and none of the member firms of Deloitte Touche Tohmatsu L...
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tranSMART Community Meeting 5-7 Nov 13 - Session 5: Recent tranSMART Lessons Learned

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tranSMART Community Meeting 5-7 Nov 13 - Session 5: Recent tranSMART Lessons Learned in Academic and Life Science Settings
Dan Housman, Recombinant by Deloitte
The Recombinant by Deloitte team has worked with organizations such as Kimmel Cancer Center as a model to adapt existing mature i2b2 implementations to meet business and scientific needs. Other organizations are increasingly focused on how to use cloud and high performance computing models to achieve different performance levels. Advanced initiatives are progressing to link commercial tools such as Qlikview to explore tranSMART data and to solve for key gaps in scientific pipelines. Dan will present recent lessons learned, new capabilities, and some of the impact on the path forwards for future tranSMART updates.

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  • Screenshots description: First, we are looking at insights into a single research study, specifically, a box plot of a gene signature list against all participants in the study who have genomic data loaded. This shows us large varients in two distinct subgroups (Type I and Type IV). We can then create a heatmap, limiting our selections to those subgroups showing the variance in genetic markers. It shows variations, but they are not statistically significant.
  • Screenshot Description: From our initial investigations into a single study, we can now perform a comparison of two different study groups to observe first the phenotypic differences (Age, Sex, etc.) and then compare the specific variances of two gene variants between the two treatment regimens. Now, we can observe that there is a statistically better outcome for one of the study groups who received a particular treatment.
  • ~150k patients with various boxes indicating types of treatments available. Also other characteristics like age, gender and so on …Identified the two most key treatment medications with the help of provider which are believed to be the cornerstone of treatment in patients with severe asthma, but do not have much of a difference in outcomes overall …
  • Token access to selected projects is required. A project’s token may be obtained from the administrator for the REDCap instance.The Last Processed timestamp is displayed for projects that have already been exported, along with the status of the export attempt.If a project that has already be exported is re-selected for export, the export process begins by cleaning out all references to the project from the i2b2 database. That is, a REDCap project that is imported into i2b2 more than once will contain only records from the most recent successful import; all traces of previous exports will be gone.
  • Token access to selected projects is required. A project’s token may be obtained from the administrator for the REDCap instance.The Last Processed timestamp is displayed for projects that have already been exported, along with the status of the export attempt.If a project that has already be exported is re-selected for export, the export process begins by cleaning out all references to the project from the i2b2 database. That is, a REDCap project that is imported into i2b2 more than once will contain only records from the most recent successful import; all traces of previous exports will be gone.
  • tranSMART Community Meeting 5-7 Nov 13 - Session 5: Recent tranSMART Lessons Learned

    1. 1. A tranSMART journey back to the real world at Deloitte November 2013 tranSMART
    2. 2. Agenda Topics • • • • • 2 About Recombinant By Deloitte Hot topics from Deloitte client community Real World Evidence + In Memory Computing I2b2/AMC back translation „integrated tranSMART‟ demo (1.2 components) preview Copyright © 2012 Deloitte Consulting LLC. All rights reserved.
    3. 3. About Recombinant By Deloitte
    4. 4. Recombinant + Deloitte - Organization Within Deloitte Deloitte U.S. Firms Audit & Enterprise Risk Services Tax Financial Advisory Services Consulting Human Capital Technology Strategy & Ops Service Area Service Area Service Area Information Management & Life Sciences Health Care Consulting Services Innovation Recombinant By Dedicated US-India (USI) Resources 4 -4- PPT Template_Recombinant by Deloitte.potx Deloitte
    5. 5. Recombinant Vision For Capabilities for Translational Medicine Payors -5- PPT Template_Recombinant by Deloitte.potx Pharma
    6. 6. General Market Approach Products / Tools  Data Strategy  Data Governance Key Capabilities Target Markets  Data Warehousing/Bioinformatics Implementations  Professional Open Source Support Contracts      Data Trust  Selectrus Analytics  Miner Suite Clinical Performance Improvement Clinical Quality Operational Excellence Accountable Care Provider / Research ACO / Payer -6-      Data Integration Hub  Open source tools (I2B2, SHRINE, tranSMA RT) Translational Research Cost Effectiveness Comparative Effectiveness Pharmacovigilance Life Sciences Federal 6 PPT Template_Recombinant by Deloitte.potx Services
    7. 7. -7PPT Template_Recombinant by Deloitte.potx Client and Partner Ecosystem
    8. 8. Hot Topics
    9. 9. Real World Evidence Objectives 9 Copyright © 2012 Deloitte Consulting LLC. All rights reserved.
    10. 10. Convergence of translational informatics data mining approaches Observational data mining High volume assay multimodal data Comparative effectiveness Target identification Unmet health system needs Target validation Health economics Pharmacogenomic markers Safety signal sensitivity Value based medicine RWE Precision Medicine Indication expansion Cross-study analysis Competitive intelligence System biology models Patient stratification External Innovation 10 Copyright © 2012 Deloitte Consulting LLC. All rights reserved.
    11. 11. (Dan Housman’s) Translational Research Enterprise Informatics Infrastructure Maturity Model Level 7 Level 6 Level 5 Cognitive computing Real time decision support External innovation and validation/optimization Level 4 Level 3 Level 2 Level 1 Level 0 Business focused solutions Enterprise utilization and standardization Data integration – data warehouse Fragmented and siloed analyses Reliance on external vendors
    12. 12. Translational Research Enterprise Informatics nfrastructure Maturity Model Level 7 Cognitive computing: Advanced ‘many to many’ unsupervised discovery algorithms with sufficient supporting underlying semantic models . Use of very large compute to identify hard to find insights. Advanced imaging feature detection analysis. Significant use of NLP enrichment and ‘on demand’ access to external data on ad-hoc basis. Broad access and use of phenotype and genotype for large populations. Use of in silico models for systems biology translated from inputs and molecular innovation. Level 6 Real time decision support: Use of predictive analytics to drive decision support at multiple levels. Patient level decision support with use of molecular markers such as trial recruitment at point of care leveraging informatics services. Real time access to data from active studies. Rapid incorporation of a broad array of data from data platforms such as microbiome, PRO, home health devices. CFR 11 validation of translational analysis tools for use in active studies. Broad establishment of enterprise data driven culture within organization. Advanced rapid access to data visualizations. Level 5 External innovation and validation/optimization: Extensive automated data exchange, broad data access contracting. Collaboration cloud with pre-competitive partners including AMCs, patient advocacy, peers, and commercial data providers. Execution of complex pipelines across multiple modes of data e.g. mRNA and NGS and literature. Federated queries across multiple institutions and modes to answer key questions. Collaborative environments with shared users and identity management and social networking. Automated tiered storage and compute to manage very large data sets and reanalysis pipelines. Use of semantic web tools to expose resources. Common internal and external tools and approaches such as OMOP. Level 4 Business focused solutions: Differentiated solutions by business area such as health economics, safety, research, operations, marker discovery, lab/sample availability, competitive analysis, pre-clinical, etc. Demonstrated and published results driving key business decisions achieved from enterprise informatics frameworks. Integration of translational research informatics with multiple enterprise systems such as portfolio management. Significant curated library by use containing clinical studies and associated open/public data. Secure web service API access to data. Standard and shared algorithms and methods across disparate internal teams. Access to broad array of real world evidence sources e.g. Twitter, adverse events, surveillance partnerships. Level 3 Enterprise utilization and standardization: Focus on use of data for decision making in major R&D cycle decisions. Documented governance of use of data, quality processes for data, and internal/external sharing. Semantic translation of studies into common formats. Cross study and multiple platform analysis enablement through integration of analytic pipelines and advanced standardization. Central informatics framework for interfacing to multiple commercial, open, and internally developed research platforms. Policy based self service access to data. Factory model and self-service curation. Acquired data sets from subscriptions converted into standard formats or repository system. Level 2 Data integration – data warehouse: Centralization of translational research data sets in single DBMS repository. Data includes clinical studies, molecular assays, observational studies, 3rd party data. Linkage at patient level across data and between data and analyses. Access controlled by ad-hoc governance model with honest broker or service delivery focus on analyses on an as needed basis to share data across groups. Self service access via web for browsing and exploring data including basic analyses. Focused pilots engage early adopter users. Level 1 Fragmented and siloed analyses: Silo approach to clinical data controlled through experts such as biostatistics groups. Data stored in primary forms such as SAS data sets and files in organized directories. Analyses produced are ad-hoc with specific tools. Internal development of systems to offer intranet or file server access to data files beyond. Recognition of governance needs. Subscription services manage reference data or to search external data. Basic catalog available through files or experts. Desktop analysis tools primary interface to data. Level 0 Reliance on external vendors: Historical focus on clinical only data sets with no ‘Omics and data integration internally. External vendors exclusively generate analyses for combined clinical and molecular data. Infrastructure for storage is file servers with limited governance and generally report focus. Limited to no institutional knowledge of available data sets from historical work.
    13. 13. Deloitte Health Miner Capabilities Outcomes Miner Population Miner Precision Miner • Visually explore populations • Propensity matched subsets • Omics analysis • View temporal relationships • Identify advanced correlations • Transmart++ • Select cohorts to analyze • Compare treatment effectiveness • • Identify basic correlations • Access curated data sets • Data delivery pipelines • Large population data sets • Subscription access to reports • Research data warehouse Analysis archive Recombinant Platform • Data consortia & licensed data sets • Cloud and on premise deployment tools • Data integration, cleansing, enrichment tools • Commercial open source support • Data models and analytics frameworks • Informatics and statistical models 13 Copyright © 2012 Deloitte Consulting LLC. All rights reserved.
    14. 14. Translational Research Platform Research Portal Precision Miner Application Layer (Miner) Study Design Study Recruitment Manager Cohort Identification Population Miner Outcomes Miner i2b2 In Memory Exploration Compare tranSMART+ Patient Journey Security and Identity Management Cohort Matching Business and Analytical Services Data Management, Storage and Processing Engine Safety ‘Omic Explorer Metrics Calculation RIE Services Data/Messaging APIs Clinical/Omics Terminology Mapping Statistical Model Execution Knowledge Management Data Trust (DT) Research Trust Data Marts (ADM, Research Mart, CFDM, OMOP, i2b2) Master Patient Indexing ‘Omic Data Management OADM DT Extensions Data Processing Pipelines Data Integration Data Integration Hub (DIH) Data De-ID/Re-ID Services Data Acquisition Custom ETL Packaged Parsers/Adapters Primary Sources Research Datasets EMR/Clinical Clinical Trials Metadata/ Terminology Services
    15. 15. Real world evidence?
    16. 16. Safety Solution Vision (Example) Reporting & Analytics Safety & RWE Platform Safety Reports Safety Case Reports Query Interface Argus DB High Quality Real World Data/Analytics from Collaborators Safety DW (internal) Analytics Export to SAS, Excel Reports RWE Population Miner OMOP Purchased Real World Data and Federation RWE Data Trust i2b2 Others Randomized Clinical Trial (RCT) and L4 Data Research Trust tranSMART Precision Miner Reports OMOP Analytics Internal Analytics Population Stratification Inventory of Data Assets RWE Portal Outcomes Miner Cross Study „Omics Analysis Social Media DW Signal Detection Sentiment Complaints 16 Copyright © 2013 Deloitte Development LLC. All rights reserved.
    17. 17. Teaming to Enable Data Driven Healthcare Improvement - President Obama to the AMA June 15, 2009 Chicago, Illinois Lowering cost through quality improvement Quality “We need to build on the examples of outstanding medicine at places like ... Intermountain Health in Salt Lake City, where high-quality care is being provided at a cost well below average. These are islands of excellence that we need to make the standard in our healthcare system.” Cost • Decrease variation in clinical processes • Measure the processes through analytics • Measure, adjust, measure, adjust… “We selected Deloitte as the best partner to translate Intermountain’s pioneering work to other systems. The use of our technologies will allow clinicians and researchers to more quickly discover practices to help usher in a new wave of innovation throughout the nation’s health systems.” - Marc Probst, CIO Intermountain Healthcare Intermountain is the initial member of what will be a Consortium of preeminent health systems across therapeutic areas and from around the world 17 Copyright © 2012 Deloitte Consulting LLC. All rights reserved.
    18. 18. Leukemia • Cancer staging • Chemo / radiation therapy • Genetic biomarkers • • • • Breast Cancer Tumor data Cancer staging Chemo / radiation therapy Genetic biomarkers Chronic Respiratory Diseases • Pulmonary Function Test • Respiratory Rate Medication (prescription and adherence) Vitals Renal Diseases • Glomerular Filtration Rate • Creatinine levels Infectious Diseases • Lab results • Microbiology results 18 Patient Encounters Treatment procedures (medical and surgical) Diabetes Mellitus • HbA1c • Blood glucose levels Patient Demographic (e.g., Age, Gen der, Ethnicity) • Approximately 2.1 million patients • 137,881,670 diagnoses • > 10 years longitudinal data set • At least 2 years visibility for all patients Clinical Diagnosis and Symptoms Alzheimer’s Disease • Cognitive scores • EEG results • Genetic data • • • • Heart Failure Echo data Staging of CHF EKG Stress test Lifestyle Parameters (e.g., Smoking, Body Mass Index) Lab Results (numerical values and text information) Mortality data (with primary / secondary causes) Rheumatoid Arthritis • Bone Mineral Density • Fracture Risk • Biologics use Hypertension • Blood pressure • EKG • Cardiac status Ischemic Heart Disease • Pulse oximetry • Cath lab data • Inpatient activity Osteoporosis • Bone Mineral Density • Fracture Risk • Menstrual and HRT status Available Intermountain Data Copyright © 2013 Deloitte Development LLC. All rights reserved.
    19. 19. Smart Data Approach to the Deloitte-Intermountain consortium Deloitte is focused on providing life sciences companies with the deepest insights from “near real time” medical record data in the world through:  Analytics based upon Intermountain (in the future other consortium health data) systems; ~200 systems covering the entire patient clinical experience  Insights from leading health systems who are mastering the care processes  Innovative business model to facilitate rapid learning with collaborative research with systems including Intermountain health care Analytics Platform Deloitte has jointly developed an analytic platform with Intermountain Healthcare’s Homer Warner Center Analytic Platform 2 Analytic Platform 3 Analytics Provisioning DHI Provides Analytic Results to Customers Illustrative Visualizations & Tools Customer Deloitte is implementing the analytics platform at provider organizations participating in a consortium and providing their EHR / EDW data DHI Portal Data Providers EHR / EDW Analytic Platform EHR / EDW Analytic Platform Access Via Subscription 1 Study of patient outcomes for selected therapeutic area (TA) EHR / EDW Business Model: Provider Partners  Produce analytic results  Help generate analytics algorithms for new products  Conduct follow-on studies 19 Data/Technology Custom Studies Deloitte as a Service Provider  Aggregate analytic results  Deliver content to customers  Broker follow-on studies Subscription & Custom Study Fees Customers Custom Informatics & Insights On-Demand  Access the results through subscription portal Copyright © 2013 Deloitte Development LLC. All rights reserved.
    20. 20. Subscription Portal RWE Reports from RWE Data Sets Safety DW Report 20 Report based on purchased claims Report summarizing social media analytics Copyright © 2013 Deloitte Development LLC. All rights reserved.
    21. 21. Demos Population Miner Outcomes Miner 21 Copyright © 2012 Deloitte Consulting LLC. All rights reserved.
    22. 22. Development of a learning loop that leverages RWE and the experience of healthcare providers Evaluating Evidence from Studies Implement Learning Focused Studies to Generate New Evidence 22 Validating Evidence in Real World Collaborate to Develop New Insights Copyright © 2013 Deloitte Development LLC. All rights reserved.
    23. 23. Vision: Leveraging tranSMART + workbench to identify insights from existing study  Using Deloitte‟s translational research tools suite of tools for evaluating current studies Evaluating Evidence from Studies Implementatio n of Learnings Focused Studies to Generate New Evidence Validating Evidence in Real World Collaborate to Develop New Insights  Studying phenotypic and genotypic profile of patients participating in a recent Asthma study  Variants of PDE4 gene and CYP 450 gene indicate variation in outcomes (however not statistically significant) Step 1: Viewing at insights into a single research study, specifically, a box plot of a gene signature list against all participants in an asthma study who have genomic data loaded. This shows us large variants in two distinct subgroups (Type I and Type IV) 23 Step 2: Heatmap view limiting our selections to those subgroups showing the variance in genetic markers. It shows variations, but they are not as significant to generalize insights Copyright © 2013 Deloitte Development LLC. All rights reserved.
    24. 24. Vision: Pooled analysis of asthma studies to identify impact of genomics on treatment outcomes  Performing a pooled analysis of “multiple studies” across various asthma studies Evaluating Evidence from Studies Implementatio n of Learnings Focused Studies to Generate New Evidence Validating Evidence in Real World Collaborate to Develop New Insights  Larger sample size enables studying phenotypic and genotypic profile of patients with greater confidence  Analysis indicates variants of PDE4 gene and CYP 450 gene showing significant variation in outcomes for certain treatments Step 3: Now we perform a comparison of multiple different study groups to observe first the phenotypic differences (Age, Sex, etc.) and then compare the specific variances of two gene variants between the study groups 24 Step 4: Heatmap view now indicates significant difference in terms of how the genetic variations are impacting the outcomes of treatments Copyright © 2013 Deloitte Development LLC. All rights reserved.
    25. 25. Vision: Overview of Asthma patients in real-world to enable better characterization of disease  Overview of all the asthma patients treated in the real-world setting in the past decade Evaluating Evidence from Studies Implementatio n of Learnings Focused Studies to Generate New Evidence Validating Evidence in Real World Collaborate to Develop New Insights  Evaluation of current treatment paradigms in the real-world and correlation with outcomes  Identification of two key treatments medications that are the cornerstone of treatment for further evaluation Step 5: Evaluating all the patients having ‘Asthma’ at Intermountain Healthcare to identify age, gender, disease frequency distribution. Identification of treatment, lifestyle, ethnicity and comorbidity patterns for the patients 25 Step 6: Ability to identify two most common medications used in patients with severe asthma condition for further evaluation using Outcomes Miner Copyright © 2013 Deloitte Development LLC. All rights reserved.
    26. 26. Vision: Evaluation of outcomes for two asthma medications in real-world setting  Comparison of patients on „Drug A‟ versus „Drug B‟ to identify difference in outcomes Evaluating Evidence from Studies Implementatio n of Learnings Focused Studies to Generate New Evidence Validating Evidence in Real World Collaborate to Develop New Insights  Evaluating „Emergency Visits‟ as an outcomes and then filtering them by „Emergency Visits specific to Asthma‟  Patients with CHF as comorbidity and treatment with Betablocker treatment indicate higher Emergency Visits Step 7: Evaluating ‘Emergency Room Visits’ as the outcome on the dashboard, overall more Emergency Room Visits for Asthma patients with CHF disease as comorbidity 26 Step 8: Evaluating ‘Emergency Room Visits for Asthma’ as the outcome, high Emergency Room Visits for patients with beta blocker treatment for CHF Copyright © 2013 Deloitte Development LLC. All rights reserved.
    27. 27. Vision: Evaluation of outcomes for two asthma medications in real-world setting (contd …) Evaluating Evidence from Studies Implementatio n of Learnings Focused Studies to Generate New Evidence Validating Evidence in Real World  Comparison indicates patients on „Drug A‟ depression, neurological conditions and psychosis have high degree of correlation with Emergency Visits  CHF and Beta Blockers believed to have an association with CYP 450 gene variants Collaborate to Develop New Insights  Indicates the need to further study impact of CYP 450 genes in drug outcomes Step 9: Evaluating ‘Emergency Room Visits for Asthma’ as the outcome for patients on Drug A shows same degree of correlation with depression, neurological conditions and psychosis 27 Step 10: Evaluating ‘Emergency Room Visits for Asthma’ as the outcome for patients on Drug B shows limited correlation with depression, neurological conditions and psychosis Copyright © 2013 Deloitte Development LLC. All rights reserved.
    28. 28. Knowing more about i2b2 data?
    29. 29. Big picture type solution for ‘AMC’ genomics initiatives Closed Loop Data Warehouse / Research Stores Source Data Clinical EMRs & Claims Data Workflow/ Enhancement RI Analytics & Care Delivery Translational Research Applications Data Trust Extended Systems Clinical Partner Clinical data Research Trust Labs Clinical Trials, Registries, Internal/External Results Data Curation Honest Broker Data Pipeline Omics/Cohort Explorer Research Open Source Research i2b2 Data DeIdentification tranSMART/ Sample Explorer Omics File Store e.g. genomics (BAM, VCF, CEL) Publications, PDF, Pathology Biobanks LIMS Statistical Analysis ‘Omics Platforms (CLC Bio) Master Data Management MPI/Provider Common Services Research Data Marts Research Information Exchange Research Portal ETL Study Recruitment Manager Security MPI Scientific Reference R SPSS SAS Terminology Reference Ref Data Mgmt Hub Collaboration *Note: Representative diagram – not all integrations are shown - 29 - HPC Portal Storage
    30. 30. Representative View: Select Cohorts via i2b2 Query & Analysis interface - 30 -
    31. 31. Representative View: i2b2 passport profiles available data - 31 -
    32. 32. Representative View: i2b2 Passport, cont. - 32 -
    33. 33. Representative View: i2b2 Passport – summary of data over time - 33 -
    34. 34. Automated research request data mart production system - 34 -
    35. 35. Getting AMC registry data into i2b2 (for tranSMART) Harvard/CHiP Jonathan Bickel M.D., M.S., FAAP
    36. 36. REDCap Study Representation - 36 -
    37. 37. XML to i2b2 REDCap Archive (ODM XML) i2b2 Staging File system Oracle Schema • Ontology • CRC EDC system of choice - 37 - i2b2 PRD
    38. 38. Choose your Stud(ies) • Choose studies to be imported • Supply token to be used for study • Click to initiate export - 38 -
    39. 39. Choose your Stud(ies) • If a project that has been previously exported is selected, the export process begins by cleaning out all references to the project from the i2b2 staging database. - 39 -
    40. 40. CDISC ODM XML <?xml version="1.0" encoding="UTF-8" standalone="yes" ?> <ODM ODMVersion="1.3.1" CreationDateTime="2012-02-03T10:59:14.175-05:00" FileOID="000-000-000" FileType="Transactional" xmlns:ns2=http://www.w3.org/2000/09/xmldsig# xmlns="http://www.cdisc.org/ns/odm/v1.3"> <Study OID="10"> <GlobalVariables> </GlobalVariables> <BasicDefinitions /> <MetaDataVersion Name="Version 1.3.1" OID="v1.3.1"> {YOUR METADATA HERE} </MetaDataVersion> </Study> <ClinicalData MetaDataVersionOID="v1.3.1" StudyOID="10"> {YOUR STUDY DATA HERE} </ClinicalData> </ODM> - 40 -
    41. 41. REDCap Study Representation - 41 -
    42. 42. Modifiers POC (Kimmel Cancer Center) Informatics Core Director, KCC, TJU Director of Research Informatics, and Research Professor of Cancer Biology Dr Jack London Informaticist, KCC Informatics Shared Resource Devjani Chatterjee, PhD Kimmel Cancer Center Deputy Director for Basic Science and Professor of Cancer Biology Karen Knudsen, PhD Assistant Professor Medical Oncology Hushan Yang, PhD Professor, Cancer Biology Hallgeir Rui, MD, PhD Vice President and CIO Stephen Tranquillo
    43. 43. Jefferson Kimmel Cancer Center - i2b2 Ontology © 2013. For information, contact Deloitte Touche Tohmatsu Limited. - 43 -
    44. 44. Jefferson Kimmel Cancer Center i2b2 v1.6 Biospecimen Ontology de-identified case ID Specimen class (solid tissue, fluid, serum , etc.) Specimen type (frozen or paraffin) de-identified specimen ID Pathologic status (normal or malignant) © 2013. For information, contact Deloitte Touche Tohmatsu Limited. - 44 -
    45. 45. Jefferson Kimmel Cancer Center i2b2 v1.6 Tumor Registry Ontology Tumor identifier modifier links different facts about the same tumor. Changing these facts from concepts to modifiers allows multiple occurrences of the same fact for the same individual to be associated with the correct corresponding tumor. © 2013. For information, contact Deloitte Touche Tohmatsu Limited. - 45 -
    46. 46. Jefferson Kimmel Cancer Center i2b2 v1.6 Genomic Profile Ontology Results for a 58 gene assay panel. chromosome number mutation classification © 2013. For information, contact Deloitte Touche Tohmatsu Limited. - 46 -
    47. 47. Jefferson Kimmel Cancer Center tranSMART (prototype 1) © 2013. For information, contact Deloitte Touche Tohmatsu Limited. - 47 -
    48. 48. The FDA needed to explore new approaches to data management and analysis for effective evaluation of product safety and efficacy Business Problem Ideal Effort Current Effort The FDA has committed to improving their overall submission review process • Resources were spending too much time on basic tasks to aggregate data across clinical trials • As a result, fewer resources were available for high-value data and regulatory analysis Effort • Review Activities Data Curation & Loading Data Selection Data Management Data Analysis Innovation Learning Sharing Regulatory Science Strategic Goals • • • Implement improved data management systems across the following multiple FDA Centers Enable the ability to: • Automate the process of loading clinical trial data from multiple source formats • Correlate data across clinical trials through a simple and intuitive user interface • Conduct advanced analytics across multiple data sets to better inform regulatory decisions Shift the utilization of resources from basic data management to high-value regulatory science - 48 -
    49. 49. tranSMART 1.2 prep
    50. 50. Demo tranSMART Modifiers Integrated faceted GWAS results Cross trials Query by ‘sequence’ Workspace ‘save’ © 2013. For information, contact Deloitte Touche Tohmatsu Limited. - 50 -
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