Wireless Health 2012Panel Discussion: Big Data at the iDASH CenterLucila Ohno-Machado, MD, PhDDivision of Biomedical Infor...
21st Century Healthcare                             Which therapies work best                              for individual ...
Patient-Centered Outcomes Research• Genome  – Sequencing data• Phenotype  – Personal monitoring      • Blood pressure, glu...
Where does knowledge come from?• Small controlled studies with strict eligibility criteria• Does this apply to my patient?...
Big Data, Small Data, and Other Data                • Transcription                                        • Transcriptome...
Clinical Translational Science                                     • Integration of Clinical Data Warehouses              ...
Data for Personalized MedicinePrevention, Diagnosis and Therapy  –   Genetic predisposition  –   Biomarkers  –   Pharmacog...
Sharing Data               • Data use agreements across                 institutions                  –   Limited and comp...
iDASH        9
Mission  “A national center for biomedical computing  that develops new algorithms, open-source  tools, computational infr...
Models for Data Sharing     • Cloud Storage: data exported for       computation elsewhere       – Users download data fro...
Models for Sharing Data Access                                                 access control                       data 1...
Models for Sharing Data Access                                                        access control   MODEL 1. User downl...
Models for Sharing Data Access                                                        access control   MODEL 1. User downl...
Models for Sharing Data Access                                                        access control   MODEL 1. User downl...
Quality Improvement, Health Services Research      User requests data for       Quality Improvement          Are the data ...
Adjusting for Confounders     User requests data for      Quality Improvement          Are the data          or Research  ...
Shared Services and Infrastructure                                              Healthcare professionals,                 ...
Underlying Infrastructure     SaaS            Biomedical Researchers,     •   Resource virtualization               End-us...
Cyberinfrastructure Security• HIPAA (Health Insurance Portability and Accountability Act)  compliant Computing environment...
Shared Infrastructure• 315TB Cloud and project  storage for 100s of virtual  servers• 54TB high-speed database  and system...
Repository for Healthcare & Biomedical Data               5/18/2012
http://idash.ucsd.edu5/18/2012
Patient-Centered Data Sharing       User U requests Data                                                                In...
Acknowledgements• Slides contributed by                  • Division ofBrian Chapman        Shuang Wang                    ...
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Panel Discussion: Big Data; Lucila Ohno-Machado, MD, PhD

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Tuesday, October 23, 2012
Panel Discussion: Big Data

Moderator: Roozbeh Jafari, PhD – Electrical Engineering, UT Dallas
Panelists: Holly Jimison, PhD – Medical Informatics & Clinical Epidemiology, OHSU James McClain, PhD – Physical Activity Epidemiologist , Risk Factor Monitoring & Methods Branch, National Cancer Institute (NCI) Lucila Ohno-Machado, MD, PhD – Associate Dean for Informatics & Technology, School of Medicine; Founding Chief, Division of Biomedical Informatics; Professor of Medicine, UC San Diego

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  • Data upload can be done through a web interface or through client applications such as the Python module Pydas. Uploading into the users private repository allows the user to curate the data prior to moving it to a sharable location.
  • Panel Discussion: Big Data; Lucila Ohno-Machado, MD, PhD

    1. 1. Wireless Health 2012Panel Discussion: Big Data at the iDASH CenterLucila Ohno-Machado, MD, PhDDivision of Biomedical InformaticsUniversity of California San DiegoEditor-in-Chief, Journal of the American Medical Informatics Association The analyses upon which this publication is based were performed under Contract Number HHSM-500-2009-00046C sponsored by the Center for Medicare and Medicaid Services, Department of Health and Human Services.
    2. 2. 21st Century Healthcare Which therapies work best for individual patients? What is the influence of genetics, environment?
    3. 3. Patient-Centered Outcomes Research• Genome – Sequencing data• Phenotype – Personal monitoring • Blood pressure, glucose – Personal health records – Behavior monitoring • Adherence to medication, exercise• Environment – Air sensors, food quality – Location Source: DOE
    4. 4. Where does knowledge come from?• Small controlled studies with strict eligibility criteria• Does this apply to my patient?Hopefully, but we need a lot of data to answer this question:• We need to build infrastructure to access large data repositories – Lower the barriers to share data• We need to share tools to analyze the data – Algorithms and computational facilities
    5. 5. Big Data, Small Data, and Other Data • Transcription • Transcriptome • Translation • Clinical Data Biomarkers Phenotype Population •Proteome Genotype • Registries • Genome Protein • Lab RNA • Data integration across biological scales • Data analysis from multiple sources • Data ‘anonymization’ and privacy preservation 5/18/2012
    6. 6. Clinical Translational Science • Integration of Clinical Data Warehouses from 5 University of California Medical Centers and affiliated institutions (>10 million patients) – Aggregate and individual-level patient data will be accessible according to data use agreements and IRB approval • Objectives – Monitor patient safety – Improve outcomes Funded by the UC Office of the – Promote research President to the NIH-funded CTSAs
    7. 7. Data for Personalized MedicinePrevention, Diagnosis and Therapy – Genetic predisposition – Biomarkers – Pharmacogenomics – Health records – SensorsHandling Protected Health Information - Secure Electronic Environment • Electronic Health Records • Genetic Data
    8. 8. Sharing Data • Data use agreements across institutions – Limited and complicated – Specific to a particular study – Resources for sharing are limited – Security/privacy constraints are hard for small institutions to follow • Sharing data today – Little incentive – Only one model: users download data – Yes/No decision on sharing
    9. 9. iDASH 9
    10. 10. Mission “A national center for biomedical computing that develops new algorithms, open-source tools, computational infrastructure, and services that will enable biomedical and behavioral researchers nationwide to integrate Data for Analysis, ‘anonymization,’ and Sharing” Supported by the NIH Grant U54 HL108460 5/18/2012 to the 10
    11. 11. Models for Data Sharing • Cloud Storage: data exported for computation elsewhere – Users download data from the cloud • Cloud Compute and Virtualization: computation goes to the data – Users analyze data in the cloud – Users download virtual machines funded by NIH U54HL108460 11
    12. 12. Models for Sharing Data Access access control data 1 Contributor data 1 Data Use Agreement DUA Data Owner QA DUA tool 1 tool 2 Contributor tool 2 Quality Assurance QA Tool tool 3 QA Creator VM 1 Contributor VM 2 System QA VM 212/9/ Creator Supported by the NIH Grant U54 HL108460 to the University of California, San Diego
    13. 13. Models for Sharing Data Access access control MODEL 1. User downloads iDASH data data 2 User DUA data 2 tool A User A data 1 Contributor data 1 DUA Data Owner QA tool 1 Contributor tool 2 tool 2 QA Tool tool 3 Creator VM 1 Contributor VM 2 System QA VM 212/9/ Creator Supported by the NIH Grant U54 HL108460 to the University of California, San Diego
    14. 14. Models for Sharing Data Access access control MODEL 1. User downloads iDASH data data 2 User DUA data 2 MODEL 2. User computes with iDASH tool A User A hosted data in iDASH environment data 1 Contributor data 1 User DUA Data Owner DUA QA tool 1 User B Contributor tool 2 tool 2 QA Tool tool 3 Creator VM 1 Contributor VM 2 System QA VM 212/9/ Creator Supported by the NIH Grant U54 HL108460 to the University of California, San Diego
    15. 15. Models for Sharing Data Access access control MODEL 1. User downloads iDASH data data 2 User DUA data 2 MODEL 2. User computes with iDASH tool A User A hosted data in iDASH environment data 1 Contributor data 1 User DUA Data Owner DUA QA tool 1 User B Contributor tool 2 tool 2 QA Tool tool 3 Creator MODEL 3 User performs iDASH computation in his own environment VM 1 data C Contributor VM 2 System QA VM 2 VM 2 User Creator C12/9/ Supported by the NIH Grant U54 HL108460 to the University of California, San Diego
    16. 16. Quality Improvement, Health Services Research User requests data for Quality Improvement Are the data or Research accessible? How many patients over 65 Trusted are on Warfarin or Broker(s) Dabigatran? •Identity & Trust Management What are the major and •Policy enforcement minor bleeding rates for Security Entity patients on these drugs? Diverse Healthcare AHRQ R01HS19913 / EDM forum Entities Count queries and statistics across data in 3 different states warehouses (federal, state, private)
    17. 17. Adjusting for Confounders User requests data for Quality Improvement Are the data or Research accessible? Trusted Broker(s) •Identity & Trust Management •Policy enforcement Security Entity Diverse HealthcareAHRQ R01HS19913 / EDM forum Entities Distributed regression models in 3 different states Wu Y et al. Grid Binary LOgistic REgression (GLORE): (federal, state, private) Building Shared Models Without Sharing Data. JAMIA 2012
    18. 18. Shared Services and Infrastructure Healthcare professionals, SaaS End-user services Researchers, Developers PaaS CollaboratorsBusiness/Service Operators, IaaS Developers, Collaborators • Software as a Service • PlatformBody/Platform • Infrastructure • Security & Policies • Scalability & ReliabilityFrame/Infrastructure 12/9/2012 • Flexibility & Extensibility
    19. 19. Underlying Infrastructure SaaS Biomedical Researchers, • Resource virtualization End-user services Researchers, Developers • Security PaaS Collaborators • Scalability IaaS iDASH Operators, • Flexibility Developers, Collaborators 5/18/2012 Figure courtesy of Dallas Thornton
    20. 20. Cyberinfrastructure Security• HIPAA (Health Insurance Portability and Accountability Act) compliant Computing environment• Segmentation (Zones) of iprojects & functionality• Physical and Environmental Protection of compute hardware• Access control with Two Factor Authentication• Secure (encrypted tunnel) system access and upload capability• Centralized logging, intrusion detection• Proxies and filters• Hardened (secured) system configurations 5/18/2012
    21. 21. Shared Infrastructure• 315TB Cloud and project storage for 100s of virtual servers• 54TB high-speed database and system storage; high- performance parallel Research data from several institutions: databases Clinical & genomic data hosting in a HIPAA compliant facility• 10Gb redundant network environment; firewall and IDS to address HIPAA requirements• Multiple-site encrypted storage of critical data
    22. 22. Repository for Healthcare & Biomedical Data 5/18/2012
    23. 23. http://idash.ucsd.edu5/18/2012
    24. 24. Patient-Centered Data Sharing User U requests Data Informed D on individual I for Are the data Consent Quality Improvement available? Management or Research No System Yes Do I wish to disclose data D Preferences Trusted to U? Broker(s) Yes No •Identity Management Information Patient I •Trust Exchange Inspection Management Registry I can check who Home Security Entity or which entity looked (wanted to look) at the data Healthcare Entity for what reasonsAHRQ R01HS19913 / EDM forum NIH U54HL10846 Privacy Registry
    25. 25. Acknowledgements• Slides contributed by • Division ofBrian Chapman Shuang Wang BiomedicalClaudiu Farcas Staal Vinterbo InformaticsDallas Thornton Vineet BafnaDanielle Mowery Wendy ChapmanHyeon-eui Kim Winston Armstrong • Funding by NIHJihoon Kim Xiaoqian Jiang AHRQKamalika Chaudhuri PCORINatasha Balac UCOPRon Joyce UCSD
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