Quantifying the Digital Disruption of Health
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Transcript of Quantifying the Digital Disruption of Health
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Quan%fying Health: Digitally Disrup%ng the Health Care System
Jerry Sheehan, Chief of Staff California Ins%tute for Telecommunica%ons and
Informa%on Technology [Calit2]
Presenta%on to California Emerging Technology Fund Board, June 22nd, 2012
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My Car: An Analogy
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Calit2’s Digitally Enabled Genomic Medicine View of The Future is Emerging
July/August 2011 February 2012
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Quan%fying My Weight
Technology used, Withings Scale, see hLp://www.withings.com
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Quan%fying My Ac%vity and Caloric Intake
Source: Bodymedia, see hLp://www.bodymedia.com
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Quan%fying My Sleep
Technology used, Zeo, see hLp://myzeo.com
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Making a Game of My Measurements
Technology used, Nike+Fuel Band, see hLp://www.nike.com/fuelband
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Data Fuels Scien%fic Discovery
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Many Users, Rich Data, Big Data Challenges
• 23&Me: – 150,000 Users
• Nike+ Users:
– +5M Users
Source: BodyMedia Blog
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The World’s Most Self-‐Aware Man?: The Atlan%c July-‐August 2012
July-‐August 2012
hLp://www.theatlan%c.com/magazine/archive/2012/07/the-‐measured-‐man/9018/?single_page=true
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A Quick Message from the Measured Man
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The Exposome
“Genes load the gun, Environment pulls the trigger” – Francis Collins, MD, PhD
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The Exposome
Historical approaches to measuring environmental exposures and behaviors -‐ • Self report via quesEonnaires every few day or months (if
that…)
• Biomarkers (e.g. blood, urine) of exposure, some good but others that are oNen indirect and imprecise or focused on only one or two outcomes.
• Direct measurement of the environment across a broad geographic area yielding only crude inferences about person-‐level exposures in Eme and space. Moreover, these are almost always focused only on air and water.
BUT…MILLIONS OF NEW SENSORS AND DEVICES CHANGE THIS AND CREATE UNPRECDENTED OPPORTUNITIES FOR POPULATION LEVEL SENSING AND INTEVENTION
Dr. Kevin Patrick, UC San Diego
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PALMS Personal AcEvity LocaEon Measurement System
• Funded by NIH/NCI Grant 1 U01
CA130771-‐01 Genes, Environment and Health Ini%a%ve (GEI)
• Kevin Patrick, Jacqueline Kerr, Fredric Raab, Greg Norman, Barry Demchak, Ingolf Krueger, Suneeta Godbole
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PALMS Fuses Physical Ac%vity Data with GPS: Showing How and Where PA Occurs
Heart rate shown in Google Earth
resting light moderate vigorous
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PALMS Can Place These Data Within GIS To Provide CONTEXT
Heart rate shown in ESRI ArcGIS against land use
Heart Rate Fused with Land Use from ESRI ArcGIS
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K. Patrick | Slide 17
Determine Indoor / Outdoor
30 second epoch
Indoors
Outdoors
Tracking indoor and outdoor time
Research QuesEon: Is Time Spent Outdoors Related To Cancer Outcomes, Mental Health Status Or PolluEon Exposures Of Interest?
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Merged GPS & AcEvity Data
Sedentary
Light
Moderate
Research QuesEon: Which Park Features Support the Most Physical AcEvity?
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PALMS Users Worldwide ((5 – 7 days of data for 1800+ par%cipants)
As of 6/1/2012
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Ci%Sense
Always-‐on Par%cipatory Sensing for Air Quality
Principal Inves%gator: Bill Griswold, Computer Science & Engineering, UC San Diego Co-‐Inves%gators: Sanjoy Dasgupta, Tajana Rosing, Ingolf Krueger, Hovav Shacham, Kevin Patrick
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Measuring Air Quality in San Diego
10 EPA Sensors 3.1 Million Residents 4000 Square Miles
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The Ci%Sense Project: A UCSD/NSF Project
EPA
Ci%Sense
contribute
distribute
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Ci%Sense Hardware
Temperature, Humidity, Barometric Pressure 3 Electrochemical gas sensors: CO, NO2, Ozone
The Sensor Smart Phones for Data Storage/Analysis
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Aggregated User Data & User Feedback
“I guess I always just thought of the atmosphere as being evenly mixed but it is not”,
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The Looming Data and Computa%onal Challenge In Health
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You Are A Superorganism: Your Body Has Ten Microbes For Every Human Cell!
Source: Science v.330, p. 1619 (2010)
Firmicutes Are the Dominant Phyla in the Human Microbiome
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Integra%ve Personal Omics Profiling: 1000x the Leading Edge of Data Today
• Michael Snyder, Chair of Genomics Stanford Univ.
• Genome 140x Coverage • Blood Tests 20 Times in 14
Months – tracked nearly 20,000
dis%nct transcripts coding for 12,000 genes
– measured the rela%ve levels of more than 6,000 proteins and 1,000 metabolites in Snyder's blood
Cell 148, 1293–1307, March 16, 2012
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SDSC/Triton
Skaggs/Users
Storage Leichtag/Sequencer
Calit2/Storage
UCSD Next Genera%on Sequencer Example: Professor Trey Idekar
Source: Chris Misleh, Calit2/SOM
Next Gen Sequencers Generate ~1TB/Run
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New NCBC: integra%ng Data for Analysis, Anonymiza%on, and SHaring (iDASH)
funded by NIH U54HL108460 29
• Data Exported for Computa%on Elsewhere – Users download data from iDASH
• Computa%on Comes to the Data – Users access data in iDASH – Users upload algorithms into iDASH
• iDASH Exportable Cyberinfrastructure – Users download infrastructure
–
Private Cloud at SD Supercomputer Center Medical Center Data HosEng
HIPAA cerEfied facility
Source: Lucila Ohno-‐Machado, UCSD SOM
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The Future of Health is Understanding Networks
Source: New England Journal of Medicine, Network Medicine-‐From Obesity to the Diseasome”, July 2007 Editorial by Dr. Albert-‐Laszlo Barbasi