tranSMART Community Meeting 5-7 Nov 13 - Session 3: Clinical Biomarker Discovery
tranSMART Community Meeting 5-7 Nov 13 - Session 5: Recent tranSMART Lessons Learned
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Transcript of tranSMART Community Meeting 5-7 Nov 13 - Session 5: Recent tranSMART Lessons Learned
A tranSMART journey back to the real world at Deloitte
November 2013
tranSMART
Copyright © 2012 Deloitte Consulting LLC. All rights reserved.2
Agenda Topics
• 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
AboutRecombinant By Deloitte
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Audit & Enterprise
Risk Services
Financial Advisory Services
Services
Deloitte U.S. Firms
ConsultingTax
Technology
Service Area
Recombinant
By
Deloitte
Human Capital
Service Area
Strategy & Ops
Service AreaInnovation
Dedicated US-India (USI) Resources
Information Management & Life Sciences Health Care
Consulting
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Recombinant + Deloitte - Organization Within Deloitte
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Pharma Payors
Recombinant Vision For Capabilities for Translational Medicine
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Life SciencesProvider / Research
ACO / Payer Target
Markets
Clinical Performance Improvement Clinical Quality Operational Excellence Accountable Care
Key Capabilities
Translational Research Cost Effectiveness Comparative Effectiveness Pharmacovigilance
Federal
Services
Data StrategyData Governance
Data Warehousing/Bioinformatics Implementations
Professional Open Source Support Contracts
Products / Tools
Data TrustSelectrus AnalyticsMiner Suite
Data Integration Hub Open source tools (I2B2,
SHRINE, tranSMART)
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General Market Approach
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Client and Partner Ecosystem
Hot Topics
Copyright © 2012 Deloitte Consulting LLC. All rights reserved.9
Real World Evidence Objectives
Copyright © 2012 Deloitte Consulting LLC. All rights reserved.10
Convergence of translational informatics data mining approaches
RWE Precision Medicine
Target identification
Target validation
Pharmacogenomic markers
Indication expansion
Cross-study analysis
System biology models
External Innovation
Comparative effectiveness
Unmet health system needs
Health economics
Safety signal sensitivity
Value based medicine
Competitive intelligence
Patient stratification
High volume assay multimodal data Observational data mining
(Dan Housman’s)Translational Research Enterprise Informatics
Infrastructure Maturity Model
Level 7 Cognitive computingLevel 6 Real time decision supportLevel 5 External innovation and validation/optimizationLevel 4 Business focused solutionsLevel 3 Enterprise utilization and standardizationLevel 2 Data integration – data warehouseLevel 1 Fragmented and siloed analysesLevel 0 Reliance on external vendors
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.
Copyright © 2012 Deloitte Consulting LLC. All rights reserved.13
Deloitte Health Miner Capabilities
Precision Miner
PopulationMiner
Outcomes Miner
Recombinant Platform
• Omics analysis
• Transmart++
• Analysis archive
• Data delivery pipelines
• Research data warehouse
• Visually explore populations
• View temporal relationships
• Select cohorts to analyze
• Identify basic correlations
• Large population data sets
• Propensity matched subsets
• Identify advanced correlations
• Compare treatment effectiveness
• Access curated data sets
• Subscription access to reports
• Data consortia & licensed data sets
• Data integration, cleansing, enrichment tools
• Data models and analytics frameworks
• Cloud and on premise deployment tools
• Commercial open source support
• Informatics and statistical models
Translational Research Platform
Research Portal
Sec
uri
ty a
nd
Id
enti
ty M
anag
emen
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Master PatientIndexing Metadata/
TerminologyServices
Data Trust (DT)Research Trust
Data Management, Storage and Processing Engine
‘Omic Data Management
Data Marts (ADM, Research Mart,
CFDM, OMOP, i2b2)
Data Processing Pipelines
Data Acquisition Custom ETLPackaged
Parsers/Adapters
Data IntegrationData Integration
Hub (DIH)Data De-ID/Re-ID
Services
OADM DT Extensions
Business and Analytical Services
Cohort Matching
Metrics Calculation
Statistical Model Execution
Knowledge Management
RIE Services
Application Layer
(Miner)
Precision Miner Cohort Identification
i2b2
‘Omic Explorer
tranSMART+
Study Design
Study Recruitment
Manager
Population Miner
In MemoryExploration
Outcomes Miner
Compare
Safety
Patient Journey
Primary Sources Research Datasets
EMR/Clinical Clinical Trials
Data/Messaging APIs
Clinical/Omics Terminology
Mapping
Real world evidence?
Copyright © 2013 Deloitte Development LLC. All rights reserved.16
Safety Solution Vision (Example)
Safety Case
ReportsArgus DB
Safety DW (internal)
OMOP
i2b2
RWE
RWE Data Trust
Others
High Quality Real World Data/Analytics from Collaborators
Purchased Real World Data and Federation
Safety & RWE PlatformReporting & Analytics
Safety Reports
Query Interface
Analytics
Export to SAS, Excel
RW
E P
orta
l
Reports
Population Miner
Outcomes Miner
OMOP Analytics
Internal Analytics
Population Stratification
Inventory of Data Assets
Reports
Research Trust
Randomized Clinical Trial (RCT) and L4
Data
tranSMART
Precision Miner
Social Media DW
Complaints
Sentiment
Cross Study
‘Omics Analysis
Signal Detection
Copyright © 2012 Deloitte Consulting LLC. All rights reserved.17
Teaming to Enable Data Driven Healthcare Improvement
• Decrease variation in clinical processes
• Measure the processes through analytics
• Measure, adjust, measure, adjust…
“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.”
- President Obama to the AMAJune 15, 2009Chicago, Illinois
“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
Qua
lity
Cost
Lowering cost through quality improvement
Intermountain is the initial member of what will be a Consortium of preeminent health systems across therapeutic areas and from around the world
Copyright © 2013 Deloitte Development LLC. All rights reserved.18
• Approximately 2.1 million patients
• 137,881,670 diagnoses
• > 10 years longitudinal data set
• At least 2 years visibility for all patients
Diabetes Mellitus
• HbA1c• Blood glucose
levels Heart Failure
• Echo data• Staging of CHF• EKG• Stress test
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 statusRheumatoid
Arthritis• Bone Mineral
Density• Fracture Risk• Biologics use
Alzheimer’s Disease
• Cognitive scores
• EEG results• Genetic data
Infectious Diseases
• Lab results• Microbiology
results
Renal Diseases• Glomerular
Filtration Rate• Creatinine
levels
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)
Patient Demographic
(e.g., Age, Gender,
Ethnicity)
Mortality data (with primary /
secondary causes)
Clinical Diagnosis
and Symptoms
Patient Encounters
Lab Results (numerical values and
text information)
Treatment procedures
(medical and
surgical) Lifestyle Parameters (e.g., Smoking, Body
Mass Index)
Vitals
Available
Intermountain Data
Copyright © 2013 Deloitte Development LLC. All rights reserved.19
Smart Data Approach to the Deloitte-Intermountain consortium
Customer Visualizations
& Tools
DH
I Po
rtal
Deloitte as a Service Provider
Analytic Platform
Analytic Platform
Analytic Platform
Provider Partners
Analytics Platform
Deloitte has jointly developed an analytic platform with Intermountain Healthcare’s Homer Warner
Center
Data Providers
Deloitte is implementing the analytics platform at provider organizations participating in a consortium and providing their EHR / EDW data
Produce analytic resultsHelp generate analytics algorithms for new products
Conduct follow-on studies
Aggregate analytic resultsDeliver content to customersBroker follow-on studies
Subscription & Custom Study Fees
Data/Technology
Analytic Platform
Analytics Provisioning
DHI Provides Analytic Results to Customers
EHR / EDW
EHR / EDW
EHR / EDW
Access the results through subscription portal
Acc
ess
Via
Su
bsc
rip
tio
n
Customers
1
2
Business Model:
Custom StudiesCustom
Informatics & Insights On-Demand
3
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
Illustrative
Study of patient outcomes for selected therapeutic area (TA)
Copyright © 2013 Deloitte Development LLC. All rights reserved.20
Subscription PortalRWE Reports from RWE Data Sets
Safety DW Report Report based on purchased claims
Report summarizing social media analytics
Copyright © 2012 Deloitte Consulting LLC. All rights reserved.21
Demos
Population Miner
Outcomes Miner
22 Copyright © 2013 Deloitte Development LLC. All rights reserved.
Development of a learning loop that leverages RWE and the experience of healthcare providers
Evaluating Evidence
from Studies
Validating Evidence in Real World
Collaborate to Develop
New Insights
Focused Studies to Generate
New Evidence
Implement
Learning
23 Copyright © 2013 Deloitte Development LLC. All rights reserved.
Vision: Leveraging tranSMART + workbench to identify insights from existing study
Evaluating Evidence from
Studies
Validating Evidence in Real World
Collaborate to Develop New
Insights
Focused Studies to
Generate New Evidence
Implementation of Learnings
Using Deloitte’s translational research tools suite of tools for evaluating current studies
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)
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
24 Copyright © 2013 Deloitte Development LLC. All rights reserved.
Vision: Pooled analysis of asthma studies to identify impact of genomics on treatment outcomes
Evaluating Evidence from
Studies
Validating Evidence in Real World
Collaborate to Develop New
Insights
Focused Studies to
Generate New Evidence
Implementation of Learnings
Performing a pooled analysis of “multiple studies” across various asthma studies
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
Step 4: Heatmap view now indicates significant difference in terms of how the genetic variations are impacting the outcomes of treatments
25 Copyright © 2013 Deloitte Development LLC. All rights reserved.
Vision: Overview of Asthma patients in real-world to enable better characterization of disease
Evaluating Evidence from
Studies
Validating Evidence in Real World
Collaborate to Develop New
Insights
Focused Studies to
Generate New Evidence
Implementation of Learnings
Overview of all the asthma patients treated in the real-world setting in the past decade
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
Step 6: Ability to identify two most common medications used in patients with severe asthma condition for further evaluation using Outcomes Miner
26 Copyright © 2013 Deloitte Development LLC. All rights reserved.
Vision: Evaluation of outcomes for two asthma medications in real-world setting
Evaluating Evidence from
Studies
Validating Evidence in Real World
Collaborate to Develop New
Insights
Focused Studies to
Generate New Evidence
Implementation of Learnings
Comparison of patients on ‘Drug A’ versus ‘Drug B’ to identify difference in outcomes
Evaluating ‘Emergency Visits’ as an outcomes and then filtering them by ‘Emergency Visits specific to Asthma’
Patients with CHF as comorbidity and treatment with Beta-blocker 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
Step 8: Evaluating ‘Emergency Room Visits for Asthma’ as the outcome, high Emergency Room Visits for patients with beta blocker treatment for CHF
27 Copyright © 2013 Deloitte Development LLC. All rights reserved.
Vision: Evaluation of outcomes for two asthma medications in real-world setting (contd …)
Evaluating Evidence from
Studies
Validating Evidence in Real World
Collaborate to Develop New
Insights
Focused Studies to
Generate New Evidence
Implementation of Learnings
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
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
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
Knowing more about i2b2 data?
- 29 -*Note: Representative diagram – not all integrations are shown
Big picture type solution for ‘AMC’ genomics initiatives
RI Analytics & Care DeliverySource Data
Clinical Trials,Registries,
Internal/ExternalResults
BiobanksLIMS
‘Omics Platforms(CLC Bio)
Clinical EMRs& Claims
Labs
Partner Clinical data
Master Data Management
MPI/ProviderScientific
ReferenceTerminologyReference
CommonServices MPI HPCRef Data Mgmt Hub Security Collaboration Portal Storage
Data Trust
Research Trust
Data Warehouse /Research Stores
Clinical
Research
Omics
ETL
DataCuration
Data De-Identification
Data Workflow/ Enhancement
Closed Loop
Translational Research Applications
Statistical Analysis
R SPSS SAS
Re
se
arc
h P
orta
l Research Open Source
i2b2tranSMART/
Sample Explorer
Extended SystemsStudy
Recruitment Manager
Omics/Cohort Explorer
Honest BrokerData Pipeline
Research Information Exchange
File Storee.g. genomics (BAM, VCF, CEL)
Publications, PDF, Pathology
Research Data Marts
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Representative View: Select Cohorts via i2b2 Query & Analysis interface
- 31 -
Representative View: i2b2 passport profiles available data
- 32 -
Representative View: i2b2 Passport, cont.
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Representative View: i2b2 Passport – summary of data over time
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Automated research request data mart production system
Getting AMC registry data into i2b2(for tranSMART)
Harvard/CHiPJonathan Bickel M.D., M.S., FAAP
- 36 -
REDCap Study Representation
- 37 -
XML to i2b2
REDCapArchive
(ODM XML)i2b2
Stagingi2b2PRD
File system Oracle Schema• Ontology• CRC
EDC system
of choice
- 38 -
Choose your Stud(ies)
• Choose studies to be imported
• Supply token to be used for study
• Click to initiate export
- 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.
- 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>
- 41 -
REDCap Study Representation
Modifiers POC (Kimmel Cancer Center)
Informatics Core Director, KCC, TJU Director of Research Informatics, and Research Professor of Cancer BiologyDr Jack London
Kimmel Cancer Center Deputy Director for Basic Science and Professor of Cancer BiologyKaren Knudsen, PhD
Professor, Cancer BiologyHallgeir Rui, MD, PhD
Informaticist, KCC Informatics Shared ResourceDevjani Chatterjee, PhD
Assistant Professor Medical OncologyHushan Yang, PhD
Vice President and CIOStephen Tranquillo
- 43 -© 2013. For information, contact Deloitte Touche Tohmatsu Limited.
Jefferson Kimmel Cancer Center - i2b2 Ontology
- 44 -© 2013. For information, contact Deloitte Touche Tohmatsu Limited.
Jefferson Kimmel Cancer Centeri2b2 v1.6 Biospecimen Ontology
Specimen type (frozen or paraffin)
Pathologic status (normal or malignant)
Specimen class (solid tissue, fluid, serum, etc.)
de-identified case ID de-identified specimen
ID
- 45 -© 2013. For information, contact Deloitte Touche Tohmatsu Limited.
Jefferson Kimmel Cancer Centeri2b2 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.
- 46 -© 2013. For information, contact Deloitte Touche Tohmatsu Limited.
Jefferson Kimmel Cancer Centeri2b2 v1.6 Genomic Profile Ontology
chromosome number
Results for a 58 gene assay panel.
mutation classification
- 47 -© 2013. For information, contact Deloitte Touche Tohmatsu Limited.
Jefferson Kimmel Cancer Center tranSMART (prototype 1)
- 48 -
The FDA needed to explore new approaches to data management and analysis for effective evaluation of product safety and efficacy
Business Problem
• 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
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
Current Effort Ideal Effort
Regulatory ScienceData Management
Data Curation & Loading
DataSelection
DataAnalysis
InnovationLearningSharing
Eff
ort
Review Activities
tranSMART 1.2 prep
- 50 -© 2013. For information, contact Deloitte Touche Tohmatsu Limited.
Demo tranSMART ModifiersIntegrated faceted GWAS results Cross trialsQuery by ‘sequence’Workspace ‘save’
- 51 -
Switch data center co-locates multiple hosting options for Life Sciences
Cloud and Hosting Eco-System
Deloitte – Internal UsersDeloitte – SaaS Solutions Deloitte – Client Hosting
Amazon
• Open burstable compute
• Client managed cloud purchases
• Connectivity• Low
cost/commodity on demand
Bluelock
• Approved for PII, PHA, HIPPA
• High Performance• Metered
applications• Subscription data• Cloud provisioning
of Deloitte managed burstable resources
Client Hosting
• Traditional Hosting• Local sensitive
data
Switch
Copyright © 2013 Deloitte Development LLC. All rights reserved.
Disclaimer: This publication contains general information only, and none of the member firms of Deloitte Touche Tohmatsu Limited, its member firms, or their related entities (collective, the “Deloitte Network”) is, by means of this publication, rendering professional advice or services. Before making any decision or taking any action that may affect your business, you should consult a qualified professional adviser. No entity in the Deloitte Network shall be responsible for any loss whatsoever sustained by any person who relies on this publication.
Definition footnote: As used in this document, "Deloitte" means Deloitte Consulting LLP, a subsidiary of Deloitte LLP. Please see www.deloitte.com/us/about for a detailed description of the legal structure of Deloitte LLP and its subsidiaries. Certain services may not be available to attest clients under the rules and regulations of public accounting.
Copyright notice: Copyright © 2013 Deloitte Development LLC. All rights reserved.
Member of Deloitte Touche Tohmatsu Limited