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11 am sim


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11 am sim

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