Big Data, Big Opportunities: It s time to Roll up our sleeves and get to work!

Transcription

1 Big Data, Big Opportunities: It s time to Roll up our sleeves and get to work! Brian D. Athey, Ph.D. Professor and Chair, Department of Computational Medicine and Bioinformatics Professor of Psychiatry and of Internal Medicine Director, Academic Informatics University of Michigan Medical School

2 I am founding chair of the S&TAB of Appistry, Inc.; St. Louis, MO I am a founding member of the SAB, Accentia Biosciences; Tampa, FL I am joining the SAB or AssureRx Health; Mason, OH I am chair of the Technical Advisory Board; 1Mind4Research, Washington, DC I serve (or have served) on numerous CTSA Steering Committees and Boards at Academic Health Centers and Clinics (e.g Marshfield Clinic) I serve on the ad hoc sub-committee of overseeing cabig and the NCI Informatics Strategy, reporting to the NCI Director and the NCI Board of Scientific Advisors I am no longer a consultant to the NIH CIO (ended March, 2011)

3 Outline Our new data intensive world/issues The science of Biomedical Informatics Genomics and Medicine: Toward Personalized Medicine Gearing up the Academic Health Center Enterprise data infrastructure Regional and national projects: Promise and Complexity Issues to think about to enhance future success 4/23/2012 Brian D. Athey, Ph.D. Department of Computational Medicine and 4/23/ Bioinformatics

4 Personal Computing Social Networks Mobile Cloud Big Data Media Driven Net = New way of life for us and for Academic Health Centers See also The Economist, Oct. 8 th 14 th, /23/2012 Brian D. Athey, Ph.D. Department of Computational Medicine and 4/23/ Bioinformatics

5 Lee Hood, IOM February 27, 2012

6 Lee Hood, IOM February 27, 2012

7 "IBM's chairman, Samuel Palmisano, said in a speech last September. "But there are also upward of a trillion interconnected and intelligent objects and organisms - what some call the Interconnected and intelligent objects and organisms - what some call the Internet of Things. all of this is generating vast stores of information. It is estimated that there will be 44 times as much data and content coming over the next decade...reaching 35 zettabytes in A zettabyte is a 1 followed by 21 zeros. And thanks to advanced computation and analytics, we can now make sense of that data in something like real time. New York Times, January 4, /23/2012 Brian D. Athey, Ph.D. Department of Computational Medicine and 4/23/20127 Bioinformatics

8 Data volumes are growing exponentially There are many reasons for this growth: the creation of nearly all data today in digital form a proliferation of sensors (e.g. Next-Generation Sequencing) new data sources such as high-resolution imagery and video. The collection, management, and analysis of data is a fast-growing concern of NIT research. Automated analysis techniques such as data mining and machine learning facilitate. Transformation of data into knowledge, and of knowledge into action. Every federal agency needs to have a big data strategy

9 In this data-rich world, your competitive advantage is your ability to transport, collect, store, organize, mine, visualize, and machine learn against data. This computational knowledge extraction lies at the heart of 21st century discovery. --Ed Lazowska Bill & Melinda Gates Professor at the University of Washington Co-Chair, PCAST NITRD Subcommittee responding to Athey-Glotzer U-Michigan CI Report This CI idea pervades all fields of modern research Brian D. Athey, Ph.D. Department of Computational Medicine and Bioinformatics 4/23/2012

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11 Associate the avalanche of genomic and high-throughput molecular information with disease risk Powerful computational methods and integrated cyberinfrastructure to enable sophisticated hierarchal systems modeling and analysis Effective linkages with better environmental, dietary, and behavioral datasets for eco-genetic analyses Credible privacy and confidentiality protections in research and clinical care Breakthrough tests, vaccines, drugs, behaviors, and regulatory actions to reduce health risks and cost-effectively treat patients globally. Omenn and Athey, 2010

12 Population(s) Athey and Omenn, 2010

13 Figu inte and repr vert chan com chan seco lifes brin of d of p den Achieving horizontal and vertical integration through multiplex mea new approaches to genetic engineering. Synthetic biology adds t and industrial process optimization to systems biology and also ad conventional biomedicine, such as bioenergy and bioremediation. Emergence of Quantitative and Systems Pharmacology: An NIGMS White Paper (Sorger et al., 2011)

14 The Science in the Middle: Linking Core Facilities to Models and Driving Research Problems Lee Hood IOM February 27, 2012

15 The Scope of Biomedical Informatics and Cyberinfratstructure: Classical View Brian D. Athey, Ph.D. Department of Computational Medicine and Bioinformatics Ted Shortliffe, /23/2012

16 General Models we Must Consider Eric E. Schadt Molecular networks as sensors and drivers of common human diseases. (2009). Nature

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18 PCAST NITRD recommends development of: Electronic Health Records (EHR), Personal Health Records (PHR) and Health Information Exchange Universal Exchange Mechanism for Health IT Data Dynamic OMIC Analytics and Data Management Infrastructure for Longitudinal Patient-Centric EHR/PHR Pharmacogenetic Informatics Link Integrated Medication Systems to Basic Research Systems such as High-Through-put Sequencing Development of an Informatics-based Surgery Network Red indicates touch points to Genomics

19 It is recommended that a Dynamic Omics Analytics and Data Management Infrastructure for enhanced analysis and standardized interoperability with a Longitudinal Patient-Centric Electronic Health Record (EHR)/Personal Health Record (PHR) be created. This will enable Integration between multi-omics data at Patient/Research Participant level in EHR: Genomics; Epigenomics; Proteomics; Metabolomics Pharmacogenomics; Toxicogenomics Imaging; Cognitive and Behavioral measures; Environmental measures Secure links to Patient Data in EHR/PHR Socio-economic measures

20 The Generic Genomics Culprit Transcriptome Digital Gene Expression Methylation Small RNA ChIP-Seq Gene expression Enumerate gene expression mirna profiling Binding site detection Alternative Splice (AS) Variants Discovery Mutation discovery Gene Fusion Discovery

21 Colliding worlds of data production and storage Stein LD: The case for cloud computing in genome informatics. Genome Biology 2010, 11:207.

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23 George Poste, IOM Feb. 28, 2012

24 George Poste, IOM Feb. 28, 2012

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26 Spanning Discovery, Translation, and Patient Care Clinomics Epigenomics Pharmacogenomics Microbiome Biomarkers --Courtesy Gianrico Farrugia, M.D.; Mayo Clinic Rochester

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28 Every step of the translational research pathway requires Integration with HIT Westfall, J. M. et al. JAMA 2007;297: T4 Outcomes

29 Interoperability with Institutional EHR Systems Clinical transaction systems Clinical Data Repository (CDR) De-identification/Honest Brokering Tools to Facilitate Extracting/Downloading Data Software tools CTSI Portals Clinical Trial/Study Databases Genomic, Proteomic, and Metabolomic High-Throughput Data Repositories and Analysis Tools Clinical Imaging Data Repositories and Analysis Tools An Institutional Specimen Tracking System A CTSA Core Lab LIMS (Laboratory Information Management System) Population/Public Health Databases & Informatics Needs Standards to promote interoperation within and between CTSA sites Informatics Teaching & Training (Interface with CTSA Education Program) Biomedical Informatics Research in Support of C&T Research Faculty, Staff, and Administrative Structure for Biomedical Informatics CTSA Informatics Consortium Operations Committee Bill Hersh (OSHU) and Brian Athey (UMICH), co-chairs. 2007

30 We need more than IT Bioinformatics How to utilize basic science data to attain knowledge and make it useful Clinical Informatics How to organize, structure and manage clinical data to make it content rich Data Strategy, Architecture and Translation Functional output for: Science + Practice = Research Education Computer Science Computation Information Technology Patient Care Administration Science and research behind computing and data management capabilities: e.g. storage, speed, cost etc. Hardware + Software Where and how to capture, store, process and communicate data

31 William S. Dalton; Moffitt Cancer Center; IOM Feb. 27, 2012

32 Biological Models Technologies Algorithms Gender Ethnicity Age Weight Diagnosis Medical History Research Lab Tests Genes Proteins Literature Databases Terminologies Ontologies

33 Essence of what we need out of the Data Factory Lawrence Shulman, Dana-Farber Cancer Institute IOM Feb 27,

34 This perspective highlights the importance of investing in foundational IT and architecture, and interfaces between silos to enable secure data flow across patient care, business operations, research, and education. External Organizations External Organizations Patient Care (Electronic Health Record) Multiple Clinical Systems Clinical Data Repository Enterprise Data Warehouse Operational Management (Historical. e.g. quality, billing, reporting etc.) Clinical Data Warehouse 1. CAD 2. QMP 3. HSDW 4. Clarity 5. Others.. Honest Broker De-Identification Anonymization Trials Consents Identity Management Vocabulary Mapping Biomedical Research & Education Population Research Research Warehouse Administration Systems Quality Reports Comparative Effectiveness Research Omics Repository Financial Reports 34

35 Data Warehouses must become Data Factories William S. Dalton; Moffitt Cancer Center; IOM

36 Campus Systems IT SERCUIRTY IT Security UMHS Data Architecture Unifying the Three Missions: Education, Research, & Patient Care Brian Athey & ECRIT 1/11/11 Admissions Comprehensive Clinical Assessment Exam Clinical Scheduling & Grading System Education Curriculum Eval. System Ctools/Saki 3 Visiting Student Application Service(VSAS) M-Pathways Education Knowledge Repository Research Pre, Post- Award Research Administration Systems Click Commerce (IRB) ethority (billing) Collexis Next-Gen Sequencing Bioinformatics Research Proteomics Core Facilities/ Omics Metabolomics Tissue Biorepositories ULAM RedCAP Research BioDBX Data Management Velos Systems OpenClinica Registries Populations Research & Quality Metrics Demographics Data Marts Individuals Diseases Others Clinical Quality Analysis Database Metrics Reporting (CAD) & Peer Others Review Research Research Data Warehouse HSDW Administration Enterprise Federated Data Warehouse CAD Data Warehouse Historical SPORES i2b2 Data HIPAA/IRB Services (Honest Broker, DE-ID Consent Management, ) Service-Oriented Information Bus CareLink/ Eclipsys Pharmacy Pathology Radiology HIM/ Documentation Biomedical Engineering Common Identifier Services (Patient, Provider, Research, Specimens, External Mappings) Vocabulary & Terminology Mapping Services (ICD-9/10 SNOMED, IMO, cadsr,...) Messaging Bus, ETL & External Collaboration Services (SOA, cagrid, SHRINE,...) CDR Ambulatory Emergency Med. Patient Care Systems Revenue Legacy+/Epic EHR Cycle Scheduling Others Epic Clarity IT IT Security Portals / Providers, Payors, P. Health Databases / HIEs / NHIN Health Sciences Library Resources NIH-Specific & External Data Resources (PubMed, GenBank, KEGG, GO, etc.) Bioinformatics and Systems Biology Workbenches High Performance Cloud Computing & Data Storage Reporting Visualization Analysis & Data Mining Data Sharing with External Collaborators cabig TCGA International Industry: Pharma/ I2b2/ Biotech CTSAs SHRINE

37 Campus Systems IT SERCUIRTY IT Security UMHS Data Architecture Unifying the Three Missions: Education, Research, & Patient Care Education Admissions Comprehensive Clinical Assessment Exam Clinical Scheduling Curriculum Evaluation System CTools/Sakai 3 M-Pathways Others Education Knowledge Repository Research Administratio n Systems Research Pre, Post- Award Click Commerce (IRB) ethority (billing) Collexis Research Administration Data Warehouse Research Core Facilities/ Omics Next-Gen Sequencing Bioinformatics Proteomics Metabolomics Tissue Biorepositories ULAM SPORES Research Data Management Systems RedCAP BioDBX Velos OpenClinica Registries Research Data Warehouse Research & Quality Metrics Data Marts Populations Individuals Diseases Demographics Others Historical Data HIPAA/IRB Services (Honest Broker, De-ID Consent Management, ) Biomedical Engineering Common Identifier Services (Patient, Provider, Research, Specimens, External Mappings) Vocabulary & Terminology Mapping Services (ICD-9/10 SNOMED, IMO, cadsr,...) Messaging Bus, ETL & External Collaboration Services (SOA, cagrid, SHRINE,...) i2b2 CAD Quality Metrics Reporting & Peer Review CIDSS Analytics & Reporting Tools Others HSDW Patient Care Systems Legacy + CareLink/ Eclipsys Pharmacy Pathology Radiology Centricity Documentation HIM CDR Epic EHR Ambulatory Emergency Med. Revenue Cycle Scheduling Others Epic Clarity Brian Athey & ECRIT 1/11/11 IT IT Security Portals / Providers, Payors, P. Health Databases / HIEs / NHIN Health Sciences Library Resources NIH-Specific & External Data Resources (PubMed, GenBank, KEGG, GO, etc.) Bioinformatics and Systems Biology Workbenches High Performance Cloud Computing & Data Storage Reporting Visualization Analysis & Data Mining Data Sharing with External Collaborators cabig TCGA International Industry: Pharma/ I2b2/ Biotech CTSAs SHRINE

38 Neonates Wellness Vulnerability Domains Honest Broker Aged Fatal Illness Recruitment Enrollment, Biospecimen Processing & Storage, EHR/PHI Capture Data Organization, Analyses, Integration & Sharing Participant Portal Genomic DNA + EHR/PHI No Restrictions MICHR Stewardship Sequence DNA Samples DNA Sequencing Core & Data PI Portal Sequence Data UM ClinicalStu dies.org Recruitment Layer Informed Consent Process/Forms Center for Health Communication Research Informed Consent Layer Genomic DNA + EHR/PHI Disease Only Genomic DNA + EHR/PHI Re-consent Permission Layer DNA Samples catissue Database EHR/PHI Data Asset Layer Research Data Warehouse I2b2/ EMERSE Access DNA Samples (De-ID or Re-ID) De-ID IRB review & approval Biomedical Informatics Layer PI-Driven Informatics Analysis (BIC) Design & Enable Specific Protocols (BERD) INSTITUTIONAL REVIEW BOARD

39 Data Organization, Analyses, Integration & Sharing Sequence DNA Samples DNA Sequencing Core & Data Sequence Data DNA Samples EHR/PHI Data Research Data Warehouse I2b2/ EMERSE Access DNA Samples (ID, De-ID) Honest Broker (ID, De-ID) IRB review & approval PI Portal PI-Driven Informatics Analysis Design & Enable Specific Protocols INSTITUTIONAL REVIEW BOARD

40 Marshfield Clinic UW-Madison UW-Milwaukee Med. College of Wisc. Wisconsin Genomics Initiative - Expertise/unique resources - Clinical Data - Biobank & Genetic Results - Genotyping Facilities - Biostatistics - Machine Learning Rarely a one-stop shop Expertise to collect, curate and maintain rich sources of data Expertise and resource to process rich sources of data Development of shared resources and networks Move beyond just big data

41 Past Present Future Handwritten Documents (Scanned, Paper Charts) Structured Data Images (PACS, Photos) Electronic Text Genomics How To Process It Optical Character Recognition (OCR) Manual Abstraction Data Warehouse (DW) Queries Image Analysis Manual Abstraction Natural Language Processing (NLP) High Performance Computing (HPC) The richest source of information isn t always the easiest to get to A single source of information rarely tells the whole picture Once processed, data doesn t always make sense Multi-disciplinary and iterative approach Scientists (what the goal of the data is) Data experts (how data is represented) Content experts (how the data is collected/created) Peissig P, et al. JAMIA 2012:19 Rasmussen L, et al. JAMIA 2011:ePub Starren J, Personal Communication

42 NIH National Center for Integrated Biomedical Informatics (NCIBI): Overview

43 Integrated Tools and that are Integrative and Analyzable Athey, B.D., Cavalcoli, J., H.V Jagadish, G.S. Omenn, B. Mirel, M. Kretzler, C. Burant, R. Isopheki, C. DeLisi, the NCIBI faculty, trainees, and staff The NIH National Center for Integrative Biomedical Informatics (NCIBI). J. Am. Med. Inform. Assoc. doi: /amiajnl

44 Harvard i2b2 Michigan Partnership: Enterprise SMART i2b2 SMART Application SMART Application Patients SMART Application Research SMART Container Physicians PHR CTMS ETL ETL ETL ETL i2b2 SHRINE CDR Cohort Discovery Federated Queries EMR Epic Cerner Vista SHRINE Cohort Discovery Federated Queries i2b2 i2b2

45 Example SMART enabled i2b2 EMR View running inside i2b2 web client Searchable / scrollable list of installed SMART apps

46 The Meducation SMART app processes medication lists from the patient record and then enables viewing and printing of simplified medication instructions in any of a dozen languages (

47 The SMART Clinical Researc app allows investigators to automatically populate clinical research forms (following the CDISC CDASH content standard) for the Demograpics (DM) and Prior and Concomitant Medications (CM) domains for

48 Priority Contact is an application that enhances the work process of a clinician by managing contact with patients after they have left the clinic and new information relevant to their treatment plan has been obtained (e.g. the results of tests).

49 Analysis Results RBM SNP Internal Clinical Research and Trials Clinical Data Gene Expression Curation ETL Master Data Non-clinical gene expression ETL Data Warehouse Mining ETL Federation Curated Content Lucene Index Text Corpus (DIP, Conference Abstracts)

50 Users Data And Providers

51 Clinical data after Database Load; deidentified, anonymized) Gene expression data (Affymetrix, Next-Gen Sequencing) Protein profiling data (Rules-based Medicine panels) Genetic data (candidate SNPs) Metabolomics data ELISA assays Laboratory chemistry data Proteomics data

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54 etriks Academics Pharma / Biotech Subcontractors IC London Project Code and Data Commu nity Manage Core r Develop er(s) transmart Foundation Government Non-Profits Committers

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56 Science is global and thrives in the digital dimensions; Digital scientific data are national and global assets; Not all digital scientific data need to be preserved and not all preserved data need to be preserved indefinitely; Communities of practice are an essential feature of the digital landscape; Preservation of digital scientific data is both a government and private sector responsibility and benefits society as a whole; Long-term preservation, access, and interoperability require management of the full data life cycle; and Dynamic strategies are required.

57 An Emerging Need in Academic Health Center IT Organizations The new guy CIO vs. CRIO CIO EHR, ERP, Quality, PACS JCAHO, AHRQ, MU, ACO, HIPAA, OSHA, HIE Customers: Physicians, nurses, CXO, managers HIMSS 1000s FTEs EDW (Clinical, Quality) and, IT vs. Analytics CRIO CTMS, IRB, Bio Banking, Grants Mgt, etc I2b2, cabig, VIVO, IRB/HIPAA, clinicaltrials.gov Customers: Researchers, students, faculty AMIA 10s-100s FTEs EDW (Research, Education) The new buzzword IT Operations and workflows Technologies Security Predictability, reproducibility Engineering, implementation Analytics Decision support Knowledge Risk Unpredictability, innovation Architecture, research Umberto Tachanardi, Ph.D U Wisconsin CTSA Informatics IKFC

58 Classic IT Classic Academia/NIH IOM 2011

59 Let s get to work! "If your daily life seems poor, do not blame it; blame yourself that you are not poet enough to call forth its riches." --R. M. Rilke

60 NCIBI Program Officer (PO) Dr. Karen Skinner, NIDA NCIBI Lead Science Officer (LSO) Dr. Jane Ye, NLM Director of Bioinformatics and Computational Biology Dr. German Cavelier, NIMH; NCIBI Science Officer Dr. Peter Lyster, NIGMS; Center for Bioinformatics and Computational Biology Director, Center for Bioinformatics and Computational Biology, NIGMS; Dr. Karin Remington Elaine Collier, NCRR NIGMS/NIDA U54-DA UL-1RR024986/NCRR CTSA transmart: Johnson & Johnson Corporation Garry Neal, Corporate VP Pharma R&D; John Shon, Director, Clinical and Translational Programs