Data data everywhere Joe Donahue Senior Vice President BioReference Laboratories
October 13, 2015
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Preparing for this panel…
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Done!
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Let me be a bit more thoughHul…
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Let me be a bit more thoughHul…
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I learned a lot about Pharma…
• We’re inefficient (on pre.y much everything)
• We’re expensive
• Not very good at integra;ng data
• Slow to adopt new technologies
• Stagnant pipelines
• Etc…!
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But no-‐one said we don’t have data…!
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But no-‐one said we don’t have data…!
We’ve had lots of data for a long Mme
• Combinatorial Libraries
• HTS
• Genome
• Laboratory notebooks / e-‐notebooks
• Images
• Health Records (electronic and otherwise)
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Why all the commoMon all of a sudden?
PaMent Data
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PaMent Data
• It’s helping us interpret what we have
• Some of it is ‘dirty’
• There’s a lot more of it from daily life – sensors
• Lab Data (not as ‘dirty’)
• EHR data (‘standards’ challenging)
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PaMent Data
• It’s helping us interpret what we have
• Some of it is ‘dirty’
• There’s a lot more of it from daily life – sensors
• Lab Data (not as ‘dirty’)
• EHR data (‘standards’ challenging)
• What are we allowed to REALLY do with it (‘Consents’)?
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Decision Support
Knowledge
Informa;on
Data
Pre-‐Omics TesMng
Results\Data
Informa;on
Knowledge
Wisdom\Management Decisions
Post-‐Omics TesMng
Lab
Provider
Lab
Provider, Research Hospital, Pharma, PaMents
Wisdom\Management and Research Decisions
Laboratories / Lab Data taking on a new role
New Data
• Genomics technologies
• Laboratory Tes;ng Data
• Imaging
• Healthcare data mining
• Safety monitoring from social media
• Anything said on social media
• Data from Pa;ent Groups / Disease Founda;ons
• Digital medicine
• Wearable devices (daily pa;ent data)
• Microbiome
• No longer n-‐of-‐1 • It’s n-‐of-‐many
• DNA/RNA technologies and omics (in general) are going to be the bulk of data for a while
• Even with all the sensors, those ‘phenotypes’ s;ll need ‘genotypes’
• Machine learning and experts will help with the data challenges and contribute knowledge that helps us ‘connect the dots’
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Discovery I II III Launch MarkeMng & Sales Life Cycle Management
Phase IV
• 10
• 100
• 1000
PaMents (thousands)
Pharma currently uses very li.le RWPD in the R&D process. What they do use is
highly controlled
T = ~20 years $1.4 – 2.6B
Pharma-‐owned highly controlled clinical trials data
Real World PaMent Data (RWPD / RWE)
Discovery I II III Launch MarkeMng & Sales Life Cycle Management
Phase IV
• 10
• 100
• 1000
PaMents (thousands)
Access to Real World Pa;ent Data can help make be.er decisions throughout the Discovery, Development and Launch phases of bringing a medicine to market
T = ~20 years $1.4 – 2.6B
Pharma-‐owned highly controlled clinical trials data
Clinical prac;ce, pa;ents, payers and providers own rights to the data
Real World PaMent Data (RWPD / RWE)
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Joe Donahue Senior Vice President [email protected] +1-551-223-9716 (mobile)