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Data Harmonization for a Molecularly Driven Health System

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Data Harmonization for a Molecularly Driven Health System

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Maximizing the value of data, computing, data science in an academic medical center, or 'towards a molecularly informed Learning Health System. Given in October at the University of Florida in Gainesville

Maximizing the value of data, computing, data science in an academic medical center, or 'towards a molecularly informed Learning Health System. Given in October at the University of Florida in Gainesville

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Data Harmonization for a Molecularly Driven Health System

  1. 1. Data Harmonization for a Molecularly Driven Health System Warren A. Kibbe, Ph.D. Professor, Biostats & Bioinformatics Chief Data Officer, Duke Cancer Institute warren.kibbe@duke.edu @wakibbe #DataSharing #LearningHealthSystem #DataHarmonization
  2. 2. Sections • Learning Health Systems • Data Commons • Data Harmonization
  3. 3. The World is Changing • Pace of Commercialization • Reach of Markets • Role of Data • Change in Healthcare • Change in Computing • Societal Changes
  4. 4. Is the US able to keep up?
  5. 5. R&D By Country
  6. 6. US R&D Funding as share of GDP
  7. 7. R&D spending / STEM
  8. 8. How do we continue to innovate?
  9. 9. Data Science
  10. 10. Twitter impacts science
  11. 11. Eric Topol
  12. 12. Changes in Computing • Converged devices • Converged IT • Ubiquity of devices, data, mHealth
  13. 13. 2017200220072012 10/23/2001 (~5yrsold) 1/9/2007 (~10yrsold) iPod(10GBmax) iPhone(EDGE,16GBmax) 9/16/1999 (~3yrsold) 802.11bWiFi 4/3/2010 (~13yrsold) iPad(EDGE,64GBmax) 4/23/2005 (~8yrsold) 9/26/2006 (~9yrsold) 7/15/2006 2/7/2007 Google Drive 4/24/2012 (~15yrsold)7/11/2008 (~11yrsold) iPhone3G (16GBmax) 9/12/2012 (~15yrsold) iPhone5(LTE,128GBmax) Google Baseline 3/9/2015 (~18yrsold) Apple ResearchKit HTCVRHeadset 4/5/2016 (~19yrsold) 7/14/2014 (~17yrsold) NextGen Courtesy of Jerry Lee, NCI Changes in Technology
  14. 14. Pace of Technology Adoption
  15. 15. Changes in Commercialization
  16. 16. Changes in Oncology • Cancer is a grand challenge • Anatomic vs molecular classification • Health vs Disease
  17. 17. Understanding Cancer • Precision medicine will lead to fundamental understanding of the complex interplay between genetics, epigenetics, nutrition, environment and clinical presentation and direct effective, evidence-based prevention and treatment. Ramifications across many aspects of health care
  18. 18. IOM (Now NAM) Report 2006-11
  19. 19. NAM Workshops
  20. 20. “Science, informatics, incentives, and culture are aligned for continuous improvement and innovation, with best practices seamlessly embedded in the delivery process and new knowledge captured as an integral by-product of the delivery experience.” —Institute of Medicine LEARNING HEALTH SYSTEMS
  21. 21. Another imperative is that such systems do their work: • Transparently (how does one learn without well documented processes?) • Reproducibly (good practices must always be repeatable at scale and scientifically reproducible) • Only with the above can the science in “data science” be done with sufficient rigor LEARNING HEALTH SYSTEMS
  22. 22. ASSEMBLE ANALYZE INTERPRET FEEDBACK CHANGE LEARNING HEALTH SYSTEMS
  23. 23. Learning Health Systems in NEJM
  24. 24. Goals • Contain rising cost of healthcare • Maximize the value of care • Increase public discourse and marketplace for healthcare
  25. 25. Drivers • Decision Making is too complex • Clinical decisions are based on practice, not evidence • Inefficiency and waste in healthcare
  26. 26. Human cognitive capacity is constant
  27. 27. Lack of Evidence
  28. 28. EHRs and the Learning Health System
  29. 29. LHS definition
  30. 30. Problems for LHS to solve
  31. 31. Inefficient Healthcare
  32. 32. Poor Health in spite of high expenditures
  33. 33. Curve hasn’t improved
  34. 34. 2015
  35. 35. View from 2006
  36. 36. EHRs are now ubiquitous But evidence-driven decision support remains a future vision
  37. 37. Hope Cloud computing, data commons, service-based computing provide some powerful tools for solving data access, data analysis, data analytics, and data visualization problems at scale, securely.
  38. 38. Sebastian Thrun
  39. 39. So what is a Data Commons
  40. 40. Commons Topology Compute Platform: Cloud or HPC Services: APIs, Containers, Indexing, Software: Services & Tools scientific analysis tools/workflows Data “Reference” Data Sets User defined data DigitalObjectCompliance App store/User Interface PaaS SaaS IaaS https://datascience.nih.gov/commons
  41. 41. Commons Compliance • Treat products of research – data, methods, papers etc. as digital objects • These digital objects exist in a shared virtual space • Digital object compliance through FAIR principles: – Findable – Accessible (and usable) – Interoperable – Reusable
  42. 42. Data Sharing and the FAIR Principles FAIR – Making data Findable, Accessible, Attributable, Interoperable, Reusable, and provide Recognition Force11 white paper https://www.force11.org/group/fairgroup/fairprinciples
  43. 43. “The Commons is an effort at creating a sharing economy and for building community. We hope for a more cost effective and productive research environment while bringing people together in a unique way.“ Phil Bourne
  44. 44. 44 Blue Ribbon Panel Report Cancer Moonshot℠ Blue Ribbon Panel “The Cancer Moonshot Task Force was directed to consult with external experts from relevant scientific sectors, including the presidentially appointed National Cancer Advisory Board(NCAB). A Blue Ribbon Panel of scientific experts was created to advise the NCAB.”
  45. 45. Vision: Enable the creation of a Learning Healthcare System for Cancer, where as a nation we learn from the contributed knowledge and experience of every cancer patient. As part of the Cancer Moonshot, we want to unleash the power of data to enhance, improve, and inform the journey of every cancer patient from the point of diagnosis through survivorship.
  46. 46. A National Cancer Data Ecosystem Cancer Research Data Commons SBG CGC Broad FireCloud ISB CGC Courtesy NCI-CBIIT
  47. 47. Data Commons Framework – What Is It? 47 Modular Components Secure user authentication and authorization Metadata validation and tools Domain-specific, extensible data models and dictionaries API and container environment for tools and pipelines Access to computational workspaces for storing data, tools, and results Reusable, expandable framework for a Data Commons Core principles and structures for a Data Commons Set of modular components that can be leveraged across Data Commons
  48. 48. Narrow Middle Architecture (End-to-End Design) 1. AuthN / AuthZ 2. Metadata validation 3. Extensible data model 4. APIs for containers, workflows & tools 5. Workspaces science outdata in Courtesy Bob Grossman, U. Chicago
  49. 49. 49 NCI Cancer Research Data Commons (CRDC) - Concept NCI Scope: “Create a data science infrastructure necessary to connect repositories, analytical tools, and knowledge bases” Data commons co-locate data, storage and computing infrastructure with commonly used services, tools & apps for analyzing and sharing data to create an interoperable resource for the research community.* *Robert L. Grossman, Allison Heath, Mark Murphy, Maria Patterson and Walt Wells, A Case for Data Commons Towards Data Science as a Service, IEEE Computing in Science and Engineer, 2016. Source of image: The CDIS, GDC, & OCC data commons infrastructure at the University of Chicago Kenwood Data Center.
  50. 50. 50 Data Commons Framework Clinical Proteomics ImagingGenomics Immuno- oncology Animal Models Cancer Biomarkers NCI Cancer Research Data Commons SBG CGC Broad FireCloud ISB CGC Elastic Compute Query Visualization Clinical Proteomics Tumor Analysis Consortium* Tool Deployment The Cancer Imaging Archive* TCIA Web Interface APIs Data Submission Authentication & Authorization Authentication & Authorization Data Models & Dictionaries Computational Workspaces Data Contributors and Consumers Tool Repositories Metadata Validation & Tools Analysis Courtesy NCI-CBIIT
  51. 51. Gen3 Data Commons
  52. 52. Gen3 Data Commons
  53. 53. Gen3 Data Commons
  54. 54. NCI Genomic Data Commons
  55. 55. NCI Genomic Data Commons
  56. 56. NCI Genomic Data Commons
  57. 57. Data Harmonization • The process of semantic and syntactic mapping of data to a set of definitions, predefined data elements, data model. • Validation and Harmonization of primary and secondary data is crucial to enable analysis and reuse
  58. 58. Spanning the Semantic Chasm of Despair Building a Translational Bridge CD2H Thanks to Melissa Haendel
  59. 59. Project Highlight: Harmonizing clinical data models Sentinel I2b2/ACT OMOP PCORNET ▪ Different countries use different “outlets”. ▪ There is a need for travel adapters. The Solution: ▪ Use a converter between various adapters. ▪ Allow researchers to ask a question once and receive results from many different sources
  60. 60. Project Highlight: LOINC2HPO ◆ Develop a software tool to map LOINC codes to HPO terms ◆ Develop software to convert EHR observations into HPO terms for use in clinical research Steps Develop a tool for converting LOINC laboratory codes and values into more phenotypically meaningful language (Human Phenotype Ontology) to allow for translational interoperability and new analytics 2657-5 “Nitrite [Mass/volume] in Urine” Numeric 20407-3 “Nitrite [Mass/volume] in Urine by Test strip” Numeric 32710-6 “Nitrite [Presence] in Urine” Positive/Negati ve 5802-4 “Nitrite [Presence] in Urine by Test strip” Positive/Negati ve 50558-6 “Nitrite [Presence] in Urine by Automated test strip Positive/Negati ve LOINC Outcome HPO: Nitrituria
  61. 61. INSERT CDE Browser Screenshot? CIBMTR Center for Cancer Research Over 35 NCI Programs, Plus Cancer Centers and Consortia GDC
  62. 62. Data Sharing Index • We need metrics for data, software, algorithm use, usability, conformance • Data sharing stimulates science, innovation, commercialization • Providing recognition and attribution to data providers and software & algorithm builders is critical for a robust data sharing ecosystem • Support and measure FAIRness!
  63. 63. Questions? Warren Kibbe, Ph.D. warren.kibbe@duke.edu @wakibbe

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