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Health IT Summit Austin 2013 - Presentation "The Impact of All Data on Healthcare" Keith Perry, Associate VP 7 Deputy CIO, UT MD Anderson Cancer Center

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Presentation "The Impact of All Data on Healthcare" …

Presentation "The Impact of All Data on Healthcare"

Keith Perry
Associate VP & Deputy CIO
UT MD Anderson Cancer Center

With continuing advancement in both technology and medicine, the drive is on to make all data meaningful to drive medical discovery and create actionable outcomes. With tools and capabilities to capture more data than ever before, the challenge becomes linking existing structured and unstructured clinical data with genomic data to increase the industry’s analytical footprint.

Learning Objectives:

∙ Discuss the need to make all data meaningful in order to speed discovery of new knowledge
∙ Provide examples of an analytical direction that supports evolution in medicine
∙ Expose the challenges facing the industry with respect to ~omits

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  • 1. The Impact of All Data on Healthcare Keith Perry, MBA Associate Vice President Deputy Chief Information Officer UT MD Anderson Cancer Center 1
  • 2. Discussion Topics • View of Big Data • Quick Facts – Cancer – MD Anderson • Evolution of Medicine – Clinical Decisions – Genomic • Big Data Shaping Strategies: – APOLLO – Foundation Warehouse – Shaping Analytics – Pushing toward Cognitive Learning • Parting Thoughts 2
  • 3. Humanity and Big Data In 2010 we humans generated more bits of information than there are stars in the knowable universe. In 2009 humanity created more data than we have in all of human history.
  • 4. What is the Big Data Problem? • Diverse perspectives on Big Data (Quoted in LinkedIn Big Data and Analytics Group): “analysis of combined differed data” “mass accumulation of (un)/structured data” “get insight from infinite data” “making sense of unlimited non-sense data”… • Integration, analysis and visualization of large volumes of unstructured, semi-structured & structured data generated by/from objects, events, processes, etc. Stephen Gold, VP, World-wide Marketing at IBM Watson – “Big data is the fuel – it is like oil. If you have it in the ground, it doesn’t have much value. As soon as you extract the oil from the ground and start refine it, it amplifies not only its usefulness but its value.”
  • 5. Healthcare Big Data McKinsey Global Institute • Five distinct Big Data pools exists in the US healthcare domain 1)Pharmaceutical: R&D, Clinical Trials 2)Academic: Translational Research 3)Provider: Clinical Operations 4)Payer: Activity (claims) & cost 5)Patient behavior & sentiment
  • 6. Healthcare Trend -> Future • Big Data Trends in Healthcare – Unstructured data and natural language processes being used as the underlying technology in healthcare – Predictive analytics allowing to aggregate the data to see patterns realistically making a difference in the decisions – Cloud-based “Big Data” platforms to aggregate, analyze, manage and research data from various sources for better patient care at a lower price – Combining social and clinical data streams to create the world’s real-time behavioral health record
  • 7. Big Data and the Creative “Reconstruction” of Medicine Modality Megabytes HL7 CDA Doc 0.025 Health Patient Chart 5 Chest Xray 16 MRI 45 PET Scan 100 Mammography 160 CT Scan (64 slice) 3,000 Genome (seq data only) 3,000 Cellular Pathology Study 25,000 7
  • 8. Global Cancer crisis demands bold action • The disease is projected to become the nation’s leading killer over the next decade as the population ages and increases • More than 500,000 people in the U.S. die every year • Lifetime cancer risk: 1 in 2 men, 1 in 3 women • World’s costliest disease • Nearly $1 trillion annually in losses to death and disability • 95% failure rate in cancer drug development • We must reverse this situation 8
  • 9. Our Mission To eliminate cancer in Texas, the nation and the world through outstanding programs that integrate patient care, research and prevention, and through education for undergraduate and graduate students, trainees, professionals, employees and the public. 9
  • 10. MD Anderson Quick Facts MD Anderson has been ranked the nation’s No. 1 cancer hospital for ten of the past 12 years in U.S. News & World Report’s “Best Hospital” survey. • The largest critical expertise of scientists and clinicians in every key area, rare or common • Exemplary science – most NCI grants; $648 million in research annually • Leading clinical research program: nearly 8,500 patients enrolled in 1,000 clinical trials exploring novel treatments • More than 115,000 patients treated each year • 19,000 employees and 1,300 volunteers with a single mission: eliminate cancer 10
  • 11. What is a moon shot? • A rigorous, multidisciplinary, highly focused and milestone-driven effort to overcome a specific cancer • Each project combines the latest genomic knowledge and technologies with a comprehensive, systematic approach to identify and advance the most promising cancer-fighting strategies • Define the future of cancer research and drive discoveries to our patients more efficiently and faster • Foremost, the moon shots are about helping patients
 13
  • 12. The goals Steered by genomics and executed with engineering precision, the moon shots aim to dramatically reduce incidence and mortality of the cancers. • Short term (5-10 years): Convert current knowledge into prevention and early-detection strategies, and more effective combinations of existing drugs. • Longer term: Discover a moon shot cancer’s root causes; identify all genetic targets that drive and sustain it; translate resulting knowledge into risk-control strategies and new medicines.. 14
  • 13. Fascinating Times “Clinical practice will never be the same. The endpoint will not be does this drug combination extend the life of a patient, but does the algorithm for choosing the best triple combination extend lives.” Mary Edgerton, M.D., Ph.D., Associate Professor, Pathology, The University of Texas MD Anderson Cancer Center “Let the patient teach us what is important” Gordon Mills, M.D., Ph.D., Chair, Systems Biology, Director, Kleberg Center for Molecular Markers, M. D. Anderson Cancer Center. Scientific progress depends increasingly on the management, sharing, and analysis of data from diverse sources. In cancer centers, informatics expertise and resources are critical shared resource functions. The Office of Cancer Centers of the National Cancer Institute Policies and Guidelines Relating to the Cancer Center Support Grant
  • 14. Clinical Domain is complicated Facts per Decision 1000 Proteomics and Other effector molecules 100 Functional Genetics: Gene expression profiles 10 Structural Genetics: e.g. SNPs, haplotypes 5 Decisions by Clinical Phenotype 1990 2000 2010 2020 With appreciation to William W. Stead, M.D., 2007 AMIA Panel Presentation, “Why We Need Internal Development”, November 11, 2007
  • 15. Big Data Supports More Precision 18
  • 16. Precision Disease Classification 19 Source: Genzyme Genetics, as presented in Allison, Malorye, “Is Personalized Medicine Finally Arriving?”, Nature Biotechnology, Vo.l 26, No. 5, May 2008, p 517.
  • 17. DNA Sequencing is Just the Beginning of (Really) Big Omics Data DNA →RNA→Protein→Metabolism →You • • Epigenetics • RNA • Proteomics • Metabolomics • Interactome • Microbiome • 20 DNA Connectome!
  • 18. Cost of Sequencing
  • 19. APOLLO enables adaptive learning Patient Consent, Biospecimen Collection, QC, Banking, Biomolecule Processing Clinical Information and Data Treatment Decisions & Response Assessment Omics & Research Data Big Data Warehouse Big Data Analytics TCGA/ICGC Pubmed Patent database Social media Big Data Warehouse as a single source of longitudinal patient data (clinical and research) Watson Solutions Insight discovery Clinical decision support Business Analytics Proprietary and Confidential 23
  • 20. Big Data Architecture Oncology Expert Advisor IBM WATSON NeXT Bio Translational Research Center Interactive Genomics Viewer Dashboards & Analytics BIG DATA ANALYTICS Healthcare Data Warehouse Foundation Computing Power – Data Warehouse Appliance Big Data Storage – Database File System Natural Language Processing Pipeline BIG DATA WAREHOUSE COMPONENTS BIG DATA PLATFORM Treatment Decisions Response Assessment Clinical Data Genomic Data Research Data Patient Database Patient Consent, Biospecimen Collection, QC, Banking, Biomolecule Processing Primary Patient Data TCGA/ICGC PubMed Social Media Security and Governance Controls
  • 21. Foundation Warehouse Overview • Create a comprehensive centralized clinical data repository supporting clinical/institutional analytics, decision making, and business intelligence needs • Central repository for historical clinical and genomic data • Break-down data silos Dashboards Pharmacy Radiology KPI’s Labs Analytic Reports Periop EMR Source Systems Healthcare Data Model Analytic Structures Analytics & Reporting 25
  • 22. Big Data Warehouse Components Health Data Warehouse Foundation Database Natural Language Pipeline Data Warehouse Appliance Big Data Storage Database File System
  • 23. Big Data Volumes to Date 1,014,548 Patients (1944) 23,146,101 Medications (2011) 68,919,788 Lab Results (2011) 1,131,182 Billing Diagnoses 453,837 NLP Documents 5,660 Molecular Diagnostic Lab Samples 4,000 Genomic Level 3 Files And Growing Daily! Big Data Warehouse
  • 24. Natural Language Processing (NLP) Natural Language Processing extracts valuable clinical information, embedded in transcribed notes to: • • • Enhance electronic patient records Decrease error rates • Facilitate integration Decrease manual effort Clinical Notes Text Parsing Context Analysis Disease Confirmation Disease Categorization New NLP Pipeline Established Comorbidity Loaded to Big Data
  • 25. Typical Research Process Researcher has Hypothesis Who has the Data? Researcher Submits Question Analyst Gathers Data Analyst Submits Results to Researcher Researcher Reviews and Asks Follow-up Question Protocol Submission / IRB Approval Researcher Pursues Hypothesis in Greater Depth Find Data and Acquire Access Profile and Integrate Data Standardize & Prepare Data Hypothesis is Confirmed or Disproved Cohort selection process can take weeks for one iteration 31
  • 26. Enhanced Research Process Researcher has Hypothesis Researcher Asks Question Researcher Reviews and Asks Follow-up Question TRC (Translational Research Center) Protocol Submission / IRB Approval Researcher Pursues Hypothesis in Greater Depth FIRE (CDM/ODB) Find Data and Acquire Access Profile and Integrate Data Standardize & Prepare Data Hypothesis is Confirmed or Disproved Cohort selection process takes minutes 32
  • 27. Oracle Cohort Explorer - Selection Clinical Research Need: Identify patients with similar comorbidity and genomic copy number variation characteristics to my current patient, so that past treatment options can be reviewed and applied effectively. Cancer Patients Cohort Explorer allows clinicians and researchers to quickly identify a similar cohort of patients across various criteria to meet the clinical research need. Leukemia Patients With a Comorbidity of Diabetes With Genomic Copy Number Variations
  • 28. Cohort Explorer – Genomic Use Case 1 • Identify two patient cohorts: Cohort 1) Patients with MDS that progressed Cohort 2) Patients with MDS that did not progress • Compare the copy number variation of these two cohorts to see if there are any differences. 34
  • 29. DEMO – Cohort Explorer Use Case 1 15 Patients MDS with progression 45 Patients MDS ONLY 35
  • 30. DEMO – Cohort Comparison 36
  • 31. Oncology Expert Advisor • Cognitive Clinical Decision Support • Deliver today’s best to all • Patient-centric • Standardization & adoption EvidenceBased Learning Natural Language Processing • Today’s best is not good enough • Patient-oriented discovery research • Learning from every patient; n=all • Convert knowledge into improved care standard Hypothesis Generation
  • 32. Dynamic summary of patient profile Patient Evaluation Rx & Management Plan Care Pathway Advisory Patient-Driven Research
  • 33. In the era of Big Data, amid the country’s medical, economic and policy challenge and as modern technology heads toward the "1,000 genome" one main biomedical challenge will be finding ways to actually use it in the clinical setting, by providing unique risk profiles or a basis for customized therapy. NIH makes big deal of big data Healthcare IT News, Jan14, 2013
  • 34. Summary Thoughts. • It is cliché but this really is an awesome time to be in technology! • We need to share this excitement and encourage new thought leaders to innovate in this uncharted space • We are on a journey (albeit one step off the starting line) where it is possible to leverage more data to: – speed knowledge discovery; – disseminate, collaborate and share best practice; and – impact the quality of healthcare today! 44