Lessons learned in scaling up
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Ethical, Legal, and Social Implications of ELSI Learning Health Systems 2017 Conference, University of Michigan. Learning from the experience and outcomes of every cancer patient
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Lessons learned in scaling up
- 1. Lessons Learned in Scaling up – Implications for LHSs Warren A. Kibbe, Ph.D. Professor, Biostats & Bioinformatics Chief Data Officer, Duke Cancer Institute warren.kibbe@duke.edu @wakibbe
- 2. Take homes • Data should be liquid • Data sharing needs to scale • Consent is a process • Precision Medicine means many common diseases will be a collection of rare diseases We need to learn from every patient
- 3. (10,000+ patient tumors and increasing) Courtesy of P. Kuhn (USC) 2006-2015: A Decade of Illuminating the Underlying Causes of Primary Untreated Tumors Omics Characterization Cancer is a grand challenge • Deep biological understanding • Advances in scientific methods • Advances in instrumentation • Advances in technology • Data and computation • Mathematical models Cancer Research and Care generate detailed data that is critical to create a learning health system for cancer Requires:
- 4. This redefinition has been driven by improved biological understanding
- 5. Health vs Disease • What is ’normal’? • Systematic and measurement error • Biological heterogeneity • Population Health
- 6. Team Science is critical Clinical Trials Biostatists Bioinformatics Clinical Care Clinical Research EHRs, Imaging, Lab Systems Data Science Analytics and Visualization Open Data enhances collaboration and team science!
- 8. How do we solve problems in Cancer • Support and incentives for team science, collaboration • We need FAIR, open data • Support open source, open science • Support for rapid innovation
- 9. From HBR, June 2016 Sharing and complexity
- 10. Data Liquidity • Data that is electronic ‘at birth’ is ideal. Lab data, sensor data, modern imaging • Manual annotation reduces liquidity, stops scaling, automation • Manual processes are an investment – one to carefully make Asking pathologists, nurses, clinicians, care team members to denote outcomes already present in labs, images, automated or automatable feeds introduces costs, errors, and impedes scaling
- 11. Machine Learning • Large data sets, particularly population-based with a well- annotated comparator set, are ideal • Machine Learning and Deep Learning on image features is feasible, accurate, reproducible and scalable
- 12. We need assistive devices for decision making
- 13. Sebastian Thrun
- 14. Biology and Medicine are now data intensive enterprises Scale is rapidly changing Technology, data, computing and IT are pervasive in the lab, the clinic, in the home, and across the population Data liquidity in healthcare is key
Notas do Editor
- +ve –ve protein expression levels, ALK- Anaplastic lymphoma kinase, Squamous is a cell type (epidermoid),
- We now have tools to let us both understand social determinants of health and build ‘early warning systems’ to identify people who are have acute medical issues
- The scale of genomics, population science and data science is dramatically changing!
- But people can make effective decisions on the same number of factors…
- How can we use machine learning and other techniques to reduce cognitive overload?