The document discusses the importance of data sharing and liquidity in scaling up cancer research and care. It emphasizes collaborative team science, the use of advanced technologies, and the need for open data to enhance learning health systems. Key insights include the transformation of precision medicine and the necessity for machine learning to handle increasing data complexity.

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!

7. Scale is changing!

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

15. Questions?
Warren Kibbe, Ph.D.
warren.kibbe@duke.edu
@wakibbe