Evidence Farming and Open Architecture

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Presented at Mobile Health 2011, May 2011, Stanford.

Published in: Technology, Health & Medicine
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Evidence Farming and Open Architecture

  1. 1. Evidence  Farming1:  Implications  for Open  Architecture Ida  Sim,  MD,  PhD Director,  Center  for  Clinical  and  Translational  Informatics University  of  California  San  Francisco May  5,  2011 1With  thanks  to  Rich  Kravitz  MD,  UC  Davis  and  Naihua  Duan,  Columbia
  2. 2. Rephrasing  “Does  it  Work?”(Complexes of) Outcome Exposures strength of association? Increased Text4Baby individual breastfeeding population
  3. 3. Current  Approaches:  RCT Asthma App ER visits at 1 year 50 people 100 people Usual Care ER visits at 1 year 50 people population• Tests  prespecified  interventions  and  outcomes• To  confirm  a  hypothesis  at  the  population  level• Strong  internal  validity• Problems:  slow  to  set-­‐up,  expensive,  short-­‐term,  lack relevance  to  the  real  world
  4. 4. Current  Approaches:  Data  Mining EHR Exposures Outcomes ? Apps population• Exposures  and  outcomes  from  care  process  systems• To  generate  hypotheses  at  the  population  level• Problems:  limited  to  data  collected,  weak  internal validity  (data  not  complete  or  systematic)
  5. 5. Current  Approaches: N-­‐of-­‐1  Studies Asthma app Usual Care Asthma app peak flow peak flow Usual Care Asthma app Usual Care individual• Within-­‐subject  multiple  crossover• Only  formal  method  for  determining  individual treatment  effectiveness• Problems:  complicated  to  set  up,  analysis  is difficult,  little  known,  not  widely  used
  6. 6. Evidence  Extraction• Evidence  is  something  to  be  extracted from  the  care  process – mining  it  from  the  data – directly  manipulating  the  care  process  with rigid  and  pre-­‐defined  protocols
  7. 7. Evidence  Strip  Mining
  8. 8. Evidence  Farming Hay, et al. J Eval Clin Prac 14(2008):707-713.
  9. 9. Rooting  for  Evidence
  10. 10. Industrial  Evidence  Farming Asthma App ER visits at 1 year 50 people100 people Usual Care ER visits at 1 year 50 people population
  11. 11. Personal  Evidence  Gardens Asthma app Usual Care Asthma app peak flow peak flow Usual Care Asthma app Usual Care individual
  12. 12. Personal  Evidence  Gardens Flovent Flovent PRN Flovent dancing dancing Flovent PRN Flovent Flovent PRN individual
  13. 13. Crowdsourcing  What  Matters• (Complexes  of)  Exposures – does  chocolate  trigger  (my)  asthma? – testing  common  regimens  (ACEI,  statin,  b-­‐blocker), complementary  medicines• (Complexes  of)  Outcomes – what  outcomes  do  patients  care  about?
  14. 14. Evidence  MacrosystemRooting for Industrial Evidence Personal Evidence Evidence Farming Gardens
  15. 15. How  can  we  scale  evaluation?
  16. 16. Stovepiped mHealth• Health  apps  built independently – little  data  sharing  and interoperability• Limits  efficiency  and impact  of  quality mHealth
  17. 17. Internet  Hourglass  Model• Standardize  and make  open  the “narrow  waist”• Reduces  duplication, spurs  community innovation,  supports commercial  and  non-­‐ profit  uses
  18. 18. OpenmHealth.org Estrin DE, Sim I. Science; 330: 759-60. 2010.
  19. 19. OpenmHealth.org• The  waist  should  support the  evidence  macrosystem
  20. 20. Open  Architecture  for  an Evidence  Macrosystem• Modules  for  usage  analytics – #  of  text  messages,  #  of  sessions,  etc.• Rooting  for  (glocal)  evidence – data  sharing  with  shared  syntax  and  semantics• Industrial  farming,  e.g.,  with  RCTs – modules  for  informed  consent,  randomization,  adaptive treatment  strategy,  mixed  methods,  etc.• Personal  evidence  gardening,  e.g.,  N-­‐of-­‐1 – modules  for  scripting  and  analyzing  individualized  N-­‐of-­‐ 1  protocols,  etc.
  21. 21. Open  Architecture  for  an Evidence  Macrosystem• Social  media  for  discovery  of  exposures  and outcomes  that  matter• Shared  libraries  of  validated  measures  and instruments  (e.g.,  PROMIS) – measures  that  get  at  finer-­‐grained  mechanisms  based on  theoretical  models  of  change,  etc.
  22. 22. Goal  for  mHealth  Evidence• A  learning  community  coupled  with  an open  architecture  for  broad,  rapid,  and iterative  dissemination  of  evaluation methods  and  findings  that  matter
  23. 23. • Ida  Sim  ida.sim@ucsf.edu• Deborah  Estrin  destrin@cs.ucla.edu• http://openmhealth.org/

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