Data Harmonization for a Molecularly Driven Health System
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The document discusses the importance of data harmonization and sharing within a molecularly driven health system to improve healthcare delivery and innovation. It highlights the challenges faced due to varying data sources and emphasizes the need for a learning health system that continuously improves through evidence-based practices. Key components of the proposed framework include data commons, compliance with FAIR principles, and the development of tools for translational interoperability among different data standards.
![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](https://image.slidesharecdn.com/harmonizationandlearninghealthuflwakibbe-191112183602/85/Data-Harmonization-for-a-Molecularly-Driven-Health-System-60-320.jpg)
Data Harmonization for a Molecularly Driven Health System
- 1. Data Harmonization for a Molecularly Driven Health System Warren A. Kibbe, Ph.D. Professor, Biostats & Bioinformatics Chief Data Officer, Duke Cancer Institute [email protected] @wakibbe #DataSharing #LearningHealthSystem #DataHarmonization
- 2. Sections • Learning Health Systems • Data Commons • Data Harmonization
- 3. The World is Changing • Pace of Commercialization • Reach of Markets • Role of Data • Change in Healthcare • Change in Computing • Societal Changes
- 4. Is the US able to keep up?
- 6. US R&D Funding as share of GDP
- 7. R&D spending / STEM
- 8. How do we continue to innovate?
- 9. Data Science
- 11. Eric Topol
- 12. Changes in Computing • Converged devices • Converged IT • Ubiquity of devices, data, mHealth
- 14. Pace of Technology Adoption
- 16. Changes in Oncology • Cancer is a grand challenge • Anatomic vs molecular classification • Health vs Disease
- 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
- 19. NAM Workshops
- 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. 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
- 23. Learning Health Systems in NEJM
- 24. Goals • Contain rising cost of healthcare • Maximize the value of care • Increase public discourse and marketplace for healthcare
- 25. Drivers • Decision Making is too complex • Clinical decisions are based on practice, not evidence • Inefficiency and waste in healthcare
- 26. Human cognitive capacity is constant
- 27. Lack of Evidence
- 28. EHRs and the Learning Health System
- 29. LHS definition
- 30. Problems for LHS to solve
- 32. Poor Health in spite of high expenditures
- 34. 2015
- 35. View from 2006
- 36. EHRs are now ubiquitous But evidence-driven decision support remains a future vision
- 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. Sebastian Thrun
- 39. So what is a Data Commons
- 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. 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. 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. “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 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. 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. A National Cancer Data Ecosystem Cancer Research Data Commons SBG CGC Broad FireCloud ISB CGC Courtesy NCI-CBIIT
- 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. 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 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 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
- 54. NCI Genomic Data Commons
- 55. NCI Genomic Data Commons
- 56. NCI Genomic Data Commons
- 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. Spanning the Semantic Chasm of Despair Building a Translational Bridge CD2H Thanks to Melissa Haendel
- 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. 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. INSERT CDE Browser Screenshot? CIBMTR Center for Cancer Research Over 35 NCI Programs, Plus Cancer Centers and Consortia GDC
- 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!