Quantifying the Digital Disruption of Health

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Presentation to the California Emerging Technology Fund Board on June 21st Regarding Digital Disruption of Health Care

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Quantifying the Digital Disruption of Health

  1. 1. 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    
  2. 2. My  Car:    An  Analogy  
  3. 3. Calit2’s Digitally Enabled Genomic Medicine View of The Future is Emerging July/August 2011 February 2012
  4. 4. Quan%fying  My  Weight   Technology  used,  Withings  Scale,  see  hLp://www.withings.com  
  5. 5. Quan%fying  My  Ac%vity  and  Caloric  Intake   Source:    Bodymedia,  see  hLp://www.bodymedia.com  
  6. 6. Quan%fying  My  Sleep   Technology  used,  Zeo,  see  hLp://myzeo.com  
  7. 7. Making  a  Game  of  My  Measurements   Technology  used,  Nike+Fuel  Band,  see  hLp://www.nike.com/fuelband    
  8. 8. Data  Fuels  Scien%fic  Discovery  
  9. 9. Many  Users,  Rich  Data,  Big  Data  Challenges  •  23&Me:       – 150,000  Users    •  Nike+  Users:   – +5M  Users   Source:  BodyMedia  Blog  
  10. 10. 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  
  11. 11. A  Quick  Message  from  the  Measured  Man    
  12. 12. The  Exposome  “Genes  load  the  gun,  Environment  pulls  the  trigger”    –  Francis  Collins,  MD,  PhD  
  13. 13. 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.   Dr.  Kevin  Patrick,   UC  San  Diego  BUT…MILLIONS  OF  NEW  SENSORS  AND  DEVICES  CHANGE  THIS  AND  CREATE  UNPRECDENTED  OPPORTUNITIES  FOR  POPULATION  LEVEL  SENSING  AND  INTEVENTION    
  14. 14. 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  
  15. 15. PALMS  Fuses  Physical  Ac%vity  Data  with  GPS:   Showing  How  and  Where  PA  Occurs   Heart  rate   shown  in   Google  Earth   resting light moderate vigorous
  16. 16. PALMS  Can  Place  These  Data  Within  GIS  To  Provide  CONTEXT   Heart  Rate  Fused  with  Land   Use  from  ESRI  ArcGIS     Heart rate shown in ESRI ArcGIS against land use
  17. 17.  K.  Patrick  |  Slide  17      Determine  Indoor  /  Outdoor   Research  QuesEon:  Is  Time  Spent  Outdoors  Related  To  Cancer  Outcomes,     Mental  Health  Status  Or  PolluEon  Exposures  Of  Interest?   Tracking indoor and outdoor time 30 second epoch Indoors Outdoors
  18. 18.    Merged  GPS  &  AcEvity  Data  Research  QuesEon:  Which  Park  Features  Support  the  Most  Physical  AcEvity?     Sedentary Light Moderate
  19. 19. PALMS  Users  Worldwide   ((5  –  7  days  of  data  for  1800+  par%cipants)   As  of  6/1/2012  
  20. 20. 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    
  21. 21. Measuring  Air  Quality  in  San  Diego   10  EPA   Sensors     3.1  Million   Residents     4000   Square   Miles  
  22. 22. The  Ci%Sense  Project:  A  UCSD/NSF  Project   contribute   Ci%Sense           EPA     distribute  
  23. 23. Ci%Sense  Hardware   The  Sensor   Smart  Phones  for  Data  Storage/Analysis  Temperature,  Humidity,  Barometric  Pressure  3  Electrochemical  gas  sensors:    CO,  NO2,  Ozone  
  24. 24. Aggregated  User  Data  &  User  Feedback   “I  guess  I  always  just  thought  of  the   atmosphere  as  being  evenly  mixed  but  it   is  not”,  
  25. 25. The  Looming  Data  and  Computa%onal  Challenge  In  Health  
  26. 26. You  Are  A  Superorganism:  Your  Body  Has  Ten  Microbes  For  Every  Human  Cell!   Firmicutes  Are  the  Dominant  Phyla     in  the  Human  Microbiome   Source:    Science  v.330,  p.  1619  (2010)  
  27. 27. Integra%ve  Personal  Omics  Profiling:  1000x  the  Leading  Edge  of  Data  Today   Cell  148,  1293–1307,  March  16,  2012   •  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   Snyders  blood  
  28. 28. UCSD  Next  Genera%on  Sequencer  Example:   Professor  Trey  Idekar    Leichtag/Sequencer            Storage   Skaggs/Users     Next  Gen     Sequencers   Generate     ~1TB/Run   Calit2/Storage   SDSC/Triton   Source:  Chris  Misleh,  Calit2/SOM  
  29. 29. New  NCBC:  integra%ng  Data  for  Analysis,     Anonymiza%on,  and  SHaring  (iDASH)  •  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   Private  Cloud  at  SD  Supercomputer  Center   Medical  Center  Data  HosEng  •  iDASH  Exportable  Cyberinfrastructure   HIPAA  cerEfied  facility   –  Users  download  infrastructure         –      Source:  Lucila  Ohno-­‐Machado,  UCSD  SOM   29   funded  by  NIH  U54HL108460    
  30. 30. 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  

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