Translational Informatics: Enabling Knowledge-Driven Healthcare
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  • TB: Bullet point beginning with ‘p-values’ has a type in it, but I’m not sure what it’s supposed to say …

Translational Informatics: Enabling Knowledge-Driven Healthcare Translational Informatics: Enabling Knowledge-Driven Healthcare Presentation Transcript

  • Translational Informatics: Enabling Knowledge-Driven Healthcare The 2nd International Conference on Translational Biomedical Informatics Taicang, China, September, 2013 Philip R.O. Payne, Ph.D. Associate Professor and Chair, Biomedical Informatics (College of Medicine) Associate Professor, Health Services Management and Policy (College of Public Health) Associate Director for Data Sciences, Center for Clinical and Translational Science Executive-in-residence, Office of Technology Transfer and Commercialization
  • Outline  Motivation  The promise of translation  The evolution from reductionism to systems thinking  A central dogma for Biomedical Informatics  Exemplary Trends  Creating learning healthcare systems  Precision medicine  Big data  Next Steps  Strategic research foci  Implementation science  Workforce development  What’s Possible…  Discussion 2
  • Outline  Motivation  The promise of translation  The evolution from reductionism to systems thinking  A central dogma for Biomedical Informatics  Exemplary Trends  Creating learning healthcare systems  Precision medicine  Big data  Next Steps  Strategic research foci  Implementation science  Workforce development  What’s Possible…  Discussion 3
  • Basic Science Clinical Research Clinical and Public Health Practice Clinical and Translational Science (CTS): Translation in the Context of Biomedicine 4 Knowledge Generation Common information needs, including:  Data collection and management  Integration  Knowledge management  Delivery  Presentation Application Continuous Cycle T1 T2 The drive for CTS has been catalyzed by two major factors:  Extending timeline associated with the new therapy discovery pipeline  Data “tsunami” facing the life sciences
  • 5 Sarkar IN, Butte AJ, Lussier YA, Tarczy-Hornoch P, Ohno-Machado L. “Translational Bioinformatics: Linking Knowledge Across Biological and Clinical Realms” Journal of the American Medical Informatics Association. 2011. Jul-Aug;18(4):354-7. Part of the “Puzzle”: Linking Molecules and Populations
  • A Catalyst: From Reductionism to Systems Thinking 6  Historical precedence for reductionism in biomedical and life sciences  Break down problems into fundamental units  Study units and generate knowledge  Reassemble knowledge into systems-level models  Influences policy, education, research and practice  Recent scientific paradigms have illustrated major problems with this type of approach  Systems biology/medicine  Reductionist approach to data, information and knowledge management is still prevalent  HIT vs. Informatics  Informatics sub-disciplines
  • A Foundational Framework: An Emerging Central Dogma for Informatics 7 Data Information Knowledge + Context + Application This applies across driving problems:  Biological  Clinical  Populations
  • Outline  Motivation  The promise of translation  The evolution from reductionism to systems thinking  A central dogma for Biomedical Informatics  Exemplary Trends  Creating learning healthcare systems  Precision medicine  Big data  Next Steps  Strategic research foci  Implementation science  Workforce development  What’s Possible…  Discussion 8
  • Building an Argument for Translational Informatics: Current Trends 9 Learning Healthcare Systems • Instrumenting the clinical environment • Generating hypotheses • Creating a culture of science and innovation Precision Medicine • Rapid evidence generation cycle(s) • „omics‟ • Analytics/decision support Big Data • System-level thinking • Data science Integrated and High Performing Healthcare Research and Delivery Systems Learning from every patient encounter Leveraging the best science to improve care Identifying and solving complex problems Rapid Translation
  • Building an Argument for Translational Informatics: Current Trends 10 Learning Healthcare Systems • Instrumenting the clinical environment • Generating hypotheses • Creating a culture of science and innovation Precision Medicine • Rapid evidence generation cycle(s) • „omics‟ • Analytics/decision support Big Data • System-level thinking • Data science Learning from every patient encounter Leveraging the best science to improve care Identifying and solving complex problems Integrated and High Performing Healthcare Research and Delivery Systems
  • The Learning Healthcare System Dialogue 11
  • Clinical Informatics Public Health Informatics Translational Bioinformatics Clinical Research Informatics The Learning Healthcare System: A BMI Perspective Instrument Patient Encounters (Data + Tissue) Generate Hypotheses Verify and Validate Hypotheses Formalize Evidence Apply Evidence Improve Patient Care (Quality + Outcomes) Learn from every patient encounter so that we can improve their care, their family’s care, and their community’s care 12
  • Multi-dimensional Data and the Learning Healthcare System 13 Molecular Phenotype Environment Enterprise Systems and Data Repositories: EHR, CTMS, Data Warehouses Emergent Sources PHR, Instruments, Etc.
  • What Happens When We Move Beyond Organizational Boundaries? Organization 1 Organization 2 Organization 3 Creating Virtual Organizations 14
  • Numerous Challenges to Creating Learning Healthcare Systems  High performance systems require rapid adaptation  Increasing demand for better, faster, safer, more cost effective therapies  Simultaneous demand for increased controls over secondary use of clinical data  Artificial partitioning of access to data for knowledge generation purposes  Critical overlaps and potential sources of conflict between these factors Regulatory, Technical and Cultural Barriers Between Data and Knowledge Generation Care Providers Researchers HIT + Biomedical Informatics Clinical InvestigatorsCI, Imaging, CRI, TBI, PHI Bioinformatics, TBI, CRI 15
  • Building an Argument for Translational Informatics: Current Trends 16 Learning Healthcare Systems • Instrumenting the clinical environment • Generating hypotheses • Creating a culture of science and innovation Precision Medicine • Rapid evidence generation cycle(s) • „omics‟ • Analytics/decision support Big Data • System-level thinking • Data science Learning from every patient encounter Leveraging the best science to improve care Identifying and solving complex problems Integrated and High Performing Healthcare Research and Delivery Systems
  • Precision or Personalized Medicine: The Four P‟s Use bio-marker technologies to predict risk of disease Use risk profile to plan preventive care delivery Design and deliver adaptive therapies Patients are actively involved in healthcare 17
  • Enabling Precision Medicine with BMI  Challenges:  Capture, represent and manage high-throughput, multi-dimensional phenotypic data  Hypothesis discovery  Rapid clinical study design and execution  Socio-cultural frameworks and human factors  Multi-scale computation and analytics Delivery and observation of clinical care Hypothesis generation and testing Clinical research Goal = generate and deliver evidence necessary to enable the provision of personalized healthcare 18
  • Building an Argument for Translational Informatics: Current Trends 19 Learning Healthcare Systems • Instrumenting the clinical environment • Generating hypotheses • Creating a culture of science and innovation Precision Medicine • Rapid evidence generation cycle(s) • „omics‟ • Analytics/decision support Big Data • System-level thinking • Data science Learning from every patient encounter Leveraging the best science to improve care Identifying and solving complex problems Integrated and High Performing Healthcare Research and Delivery Systems
  • Reasoning on Big Data Is Hard… 20  Unexpected problems  Algorithms behave differently  Applicability of convention metrics  P-values don‟t mean allot in petabyte scale data  Signal vs. noise  Detection  Understanding of patterns  Physical computing  Data storage  Computational performance
  • But the Promise of Big Data is Significant! 21  “Sergey Brin’s Search for a Parkinson’s Cure”  Wired Magazine, July 2010  Leveraging Google’s Computational Expertise to Mine Big Data  Distributed computing  Reasoning across heterogeneous data types  Exchanging traditional measures of result validity for the predictive power of increasingly large data sets  Resulting in differential time scales to generate analogous results  6 months vs. 8 years
  • Outline  Motivation  The promise of translation  The evolution from reductionism to systems thinking  A central dogma for Biomedical Informatics  Exemplary Trends  Creating learning healthcare systems  Precision medicine  Big data  Next Steps  Strategic research foci  Implementation science  Workforce development  What’s Possible…  Discussion 22
  • 23 Next Steps: Achieving the Vision of Translational Informatics Strategic Research Foci Implementation Science Workforce Development Translation + Systems Thinking
  • Strategies & Future Directions • Answering people-centric questions: • Workflow • Usability • Software Design Patterns • True platform integration: • SOA and Cloud Computing • Semantic web • Knowledge engineering • Visualization and HCI • Reasoning: • Data mining • Text mining/NLP • Data integration • Knowledge discovery • Enable all stakeholders to ask and answer questions • Includes informaticians 24
  • 25 Implementation Science and Workforce Development: Empowering Knowledge Workers Driving Biological and Clinical Problems Knowledge Workers Solutions to Real World Problems Critical Issues:  Workflows that enable engagement by Subject Matter Experts  Tight coupling of engineering efforts and research programs that can define driving “real world” problems  Facilitation and support of interdisciplinary, team science models (including basic and translational scientists, clinical researchers, and informaticians)  Incorporation of human and cognitive factors in all aspects of projects Biomedical Informatics ≠ Engineering Systems-level Approaches To Interoperability and Usability Are Essential
  • Outline  Motivation  The promise of translation  The evolution from reductionism to systems thinking  A central dogma for Biomedical Informatics  Exemplary Trends  Creating learning healthcare systems  Precision medicine  Big data  Next Steps  Strategic research foci  Implementation science  Workforce development  What’s Possible…  Discussion 26
  • High Throughput Hypothesis Generation  Asking and answering important questions in large scale, multi- dimensional data sets  Challenges:  Heterogeneity of data sets  Availability of knowledge resources that can be used to annotate targeted data  Methods:  Constructive induction  Outcomes:  Able to identify novel hypotheses relating bio-molecular markers and clinical phenotypes that may be able to inform diagnostic or therapy planning approaches to multiple cancers Phenotype Bio-molecular Markers Biospecimens
  • Putting Conceptual Knowledge to Work: Constructive Induction (CI) & Hypothesis Generation Conceptual Knowledge Constructs (CKCs) • Conceptual knowledge-anchored concepts + relationships • Higher order constructs (multiple intermediate concepts) • Controls for concept granularity (search depth) • Basis for inference of hypotheses concerning relationships between data elements
  • Experimental Context: CLL Research Consortium  NCI-funded Program/Project (PO1)  Translational research targeting Chronic Lymphocytic Leukemia (CLL)  Established in 1999  Cohort of over 6,000 patients  Comprehensive phenotypic and bio-molecular data sets, as well as bio-specimens  8 participating sites  Informatics platform:  Research networking  Clinical trials management  Correlative data management  Bio-specimen management
  • Multi-part CI Evaluation Study in CLL (1) Efficacy (2) Verification & Validation (3) Mining Domain Literature  CKC Evaluation • 108 data elements • 822 UMLS concepts • 5800 CKCs • 5 SMEs • Random sample (250) • 86% valid • 90% “meaningful”  Search depth controls  TOKEn browser  Automated lit. queries • Random sample (50)  SME “gold standard” • Support metric  Critical relationship •  support metric •  “meaningful” • Significant correlation1. Payne PR, Borlawsky T, Kwok A, Dhaval R, Greaves A. Ontology-anchored Approaches to Conceptual Knowledge Discovery in a Multi-dimensional Research Data Repository. AMIA Translational Bioinformatics Summit Proc. 2008. 2. Payne PR, Borlawsky T, Kwok A, Greaves A. Supporting the Design of Translational Clinical Studies Through the Generation and Verification of Conceptual Knowledge-anchored Hypotheses. AMIA Annu Symp Proc. 2008. 3. Payne PR, Borlawsky T, Lele O, James S, Greaves AW. The TOKEN Project: Knowledge Synthesis for in-silico Science. Journal of American Medical Informatics Association (JAMIA). 2011  Mining CLL literature • Medline, 2005- 2008  Comparison • Literature-based CKCs • Ontology-based CKCs  Critical findings • No overlap • Differing granularity • More timely (SMEs)
  • CKC Visualization Cytogenetic & Chromosomal abnormalities Bio-molecular Products Hematologic Malignancies Bone Marrow Morphology Tissues of Origin Solid Tumors Myelogenous Malignancies TOKEn CKC Network: CLL Research Consortium Metadata
  • Cytogenetic Abnormalities Treatment Response Bone Marrow Morphology Lymphomas Leukemia's Chromosome Loss Laboratory Findings Protein Expression Molecular Abnormalities Tissues of Origin Tissues of Origin TOKEn CKC Network: Semantic Partitions
  • Critical Dimensions of this Project…  Focus on a translational informatics approach to knowledge generation  Based upon a systems-level conceptual model  Leverages data generated during clinical care to support hypothesis generation (learning healthcare system)  Deals with big-data (3 V‟s)  Targets hypotheses that can support adaptive therapies for CLL  Involves a multi-disciplinary research team with cross- cutting Biomedical Informatics acumen  Supported by rigorous implementation science principles 33
  • Outline  Motivation  The promise of translation  The evolution from reductionism to systems thinking  A central dogma for Biomedical Informatics  Exemplary Trends  Creating learning healthcare systems  Precision medicine  Big data  Next Steps  Strategic research foci  Implementation science  Workforce development  What’s Possible…  Discussion 34
  • A Multi-Scalar Approach to Knowledge Synthesis
  • 36 Collaborators:  Peter J. Embi, MD, MS  Albert M. Lai, PhD  Kun Huang, PhD  Po-Yin Yen, RN, PhD  Yang Xiang, PhD  Marcelo Lopetegui, MD  Tara Borlawsky-Payne, MA  Omkar Lele, MS, MBA  Marjorie Kelley  William Stephens  Arka Pattanayak  Caryn Roth  Andrew Greaves Funding:  NCI: R01CA134232, R01CA107106, P01CA081534, P50CA140158, P30CA016058  NCATS: U54RR024384  NLM: R01LM009533, T15LM011270  AHRQ: R01HS019908  Rockefeller Philanthropy Associates  Academy Health – EDM Forum Acknowledgements Laboratory for Knowledge Based Applications and Systems Engineering (KBASE):
  • 37 Thank you for your time and attention! • philip.payne@osumc.edu • http://go.osu.edu/payne