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Open models healthcare v5 7

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  • 1. THE  FUTURE  OF  OPEN  MODELS    FOR  DIABETES  CARE  2012  DiabetesMine  Innova1on  Summit  Ikhlaq  Sidhu,  Megha  Raghavan    UC  Berkeley   Fung Institute for Engineering Leadership UC Berkeley
  • 2. Macro  Market  Forces  in  Healthcare     •  Healthcare  Spending:   Total  NaDonal     $2.2T  in  US$6.6T  Globally   25% $5 Health  Expenditures   2019:  $4.6T   •  Growing  Need:  Baby   $4 Boomer  +  Diabesity   20% 2011:  $2.7T   $3 •  18%  of  the  US  economy!   15% $2 $1 •  Price  Pressure  and   Declining  Margins   10% $0 2000 2005 2010 2015 2020 •  Chaos!   Year  Source:  Centers  for  Medicare  &  Medicaid  Services,  Office  of  the  Actuary:  Data  from  the  Na1onal  Sta1s1cs  Group,  October  2010   2  
  • 3. Total Health Expenditure per Capita, U.S. and Selected Countries, 2008 $8,000 $7,538 $7,000 $6,000Per Capita Spending - PPP Adjusted $5,003 $5,000 $4,627 $3,970 $4,063 $4,079 $4,000 $3,677 $3,696 $3,737 $3,353 $3,470 $3,129 $2,870 $2,902 $3,000 $2,729 $2,000 $1,000 $0 Source: Organisation for Economic Co-operation and Development (2010), "OECD Health Data", OECD Health Statistics (database). doi: 10.1787/data-00350-en (Accessed on 14 February 2011). Notes: Data from Australia and Japan are 2007 data. Figures for Belgium, Canada, Netherlands, Norway and Switzerland, are OECD estimates. Numbers are PPP adjusted.
  • 4. Macro  Trend  #2:   A  Larger  Market  but  with  Lower  Margins   Annual  Revenue  Growth   Approximate  Breakdown   •  Services  =  80%   20% •  Devices  /  equipment  =  8%   16% •  Pharma  =  12%   12% Predic1on:   8% •  Target  across  sectors   4% •  Volume  Business   0% 2004 2006 2008 2010Source:    2011  free  cash  flow  and  revenue  growth  projected  by  JPMorgan  for  12  large  medtech  companies:  ABT,  BCR,  BAX,  BSX,  COV,  EW,  ISRG,  JNJ,  MDT,  STJ,  SYK,  ZMH.       4  
  • 5. Outline  •  The  current  state  of  diabetes  data  and   soiware    •  Why  this  is  a  problem  •  What  we  can  learn  from  other  diabetes   therapies  and  open  standards  in  IT  •  Recent  Progress  •  Key  ques1ons  
  • 6. Diabetes  Data  and  Soiware  Today   •  Ver1cal   •  Closed   •  Proprietary   •  Not   Standardized  
  • 7. Diabetes  Data  and  Soiware  Today  Source:    Timothy  Bailey,  Journal  of  Diabetes  Science  &  Technology  2007;  photo  provided  by  Dr.  Bruce  Buckingham      
  • 8. WHY  IS  THIS  A  PROBLEM?  
  • 9. SMBG  data  downloading     •  45%  of  907  type  1s  never  download  their   meter   •  75%  of  1,165  type  2s  never  download  their   meter   •  Only  37%  of  type  1s  are  sa1sfied  with   downloading  Source:    dQ&A  pa1ent  panel,  3Q2010.  Self  Monitoring  of  Blood  Glucose  Data  from  dQ&A  
  • 10. Pa1ents  Don’t  Download!     •  Data  from  the  T1D  Exchange  –  25,000  pa1ents   with  type  1  diabetes  at  69  leading  clinical   centers  across  the  US.   •  Blood  glucose  meter  downloading:     –  68%  never  download   –  21%  download  less  than  once  a  month     •  CGM  downloading:     Burden  on  doctors     –  43%  never  download   to  see  and     analyze  data   –  35%  download  less  than  once  a  month  Source:    n=25,000  pa1ents  in  the  Helmsley  Charitable  Trust  T1D  Exchange  
  • 11. …but  there  is  a  shortage  of  providers   •  New  York:  1.8  million  people  with  diabetes,   only  600  diabetes  educators  (2nd  highest  #  in   US)   •  “Overall,  there  appear  to  be  only  one-­‐half  the   endocrinologists  required  to  the  fill  the   needed  posi1ons  in  the  U.S.”   –  ~4,000  endocrinologists  to  care  for  the  ~25-­‐100   million  pa1ents  who  could  poten1ally  be  seen    Source:    D  Jornsay,  AADE  2012  Annual  Mee1ng,  Las  Vegas;  A  Stewart,    J  Clin  Endocrin  Metab  2008        
  • 12. …and  providers  have  li{le  1me  with   pa1ents!   •  In  a  study  of  pa1ents  with  type  2  diabetes,   medical  residents  spent  an  average  of  only  five   minutes  during  visits  discussing  diabetes!   •  Average  primary  care  visit  length  in  2005:  21   minutes   •  Best  case:  Four  hours  per  year  with  an   endocrinologist  vs.  8,756  hours  of  self-­‐ management      Source:  Barnes  et  al.,  Diabetes  Educ  2004;  Chen  et  al.,  Archives  of  Internal  Medicine  2009  
  • 13. SOFTWARE  CAN  HELP  BRIDGE  THE  GAP,  BUT…  
  • 14. HOW  DO  YOU  GET  MORE  PATIENTS  TO  USE  IT?  
  • 15. What  we  can  learn  from  •  1.  Characteris1cs  of  other  commercially   successful  diabetes  therapies  •  2.  Open  standards  in  informa1on  technology  
  • 16. 1.  Where  is  industry  growth  coming   from?   Share  of  Overall  Diabetes  Industry  Growth     Insulin  pumps   CGM   Others     GLP-­‐1s     Insulin   DPP-­‐4s    
  • 17. 1.  Why  are  DPP-­‐4s  so  successful?  •  Easy  for  pa1ents  –  once  per  day  pill  •  Li{le  thinking/educa1on  required  •  Low  HCP  hassle  –  no  training  needed,  not  1me   consuming  •  Low  adherence  barriers  for  pa1ents  •  Standardized  –  all  DPP-­‐4s  are  pre{y  much  the   same  
  • 18. 2.  What  can  we  learn  from  IT?   1980s:  IBM  was  late  so  they  offered  an  open   architecture  (5150)   1970’s:  Ver1cal,   • Higher  volume  proprietary,  closed   • Best  in  class  components   • Horizontal   Late  70s   1990s:  IBM  tries  to  go   Perfect  storm:  Chaos  +   back  to  proprietary  OS   growing  market     and  loses  market  share   And,  who  made  the  money?    
  • 19. Takeaways  from  the  IT  Case  1.  Open  standards  modularity:  It  will  happen.  (Usually   demanded  by  customers.)  2.  It  only  takes  one  single  player  (to  change  the   landscape  of  the  industry)  3.  Change  is  irreversible  once  it  happens  –  IBM   example  in  the  PC  market  
  • 20. What  are  the  parallels  to  diabetes  healthcare?   Millions   US  PC  Sales  Units   of  Units   US  PC  Sales  Units  10   150   8   6   100   4   50   2   0   0   1975   1980   1985   1990   1995   2000   2005   2010   2015   1975   1980   1985   1990   Introduc1on  of     Interconnec1vity       Mobile   Open  Standards   &  Networks   Data  In  personal  compu1ng,  the  drivers  of  mass  adop1on  were:   –  Ver1cal  closed  to  open  standard  (1980)   –  Giving  power  to  user  (1980  to  1990)   –  Widespread  Network  Connec1vity  (1995-­‐2000)   –  Mobile  compu1ng  and  mobile  data  (2005+)   Source:  ETForecasts  
  • 21. Recent  Progress  
  • 22. Recent  Progress  
  • 23. The  Future?   •  A  single  standardized   report  Automa1c   •  Accessible  on  any  Wireless   device  Transmission   •  Ac1onable  insights     •  Data  analy1cs  
  • 24. Key  Ques1ons  •  Could  open  standards  improve  diabetes  care?  •  What  is  preven1ng  more  open  standards  in   diabetes  care?  •  Why  don’t  more  pa1ents  use  soiware  and   download  data?  How  can  this  be  addressed?  •  ____  is  the  biggest  problem  with  obtaining/ interpre1ng  diabetes  data.  It  could  be  solved   by  ____  
  • 25. Master of EngineeringFung  InsDtute  for  Engineering  Leadership   Ikhlaq  Sidhu      Chief  Scien1st  and  Founder,  Fung  Ins1tute   Faculty  Director,  Center  for  Entrepreneurship  &  Technology   Academic  Director,  Engineering  Leadership  Professional  Program   2009  Emerging  Area  Professor  Award   sidhu@berkeley.edu