Exploring Markets of Data for Personal Health Information

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To realize preventive and personalized medicine, large numbers of consumers must pool health information to create datasets that can be analyzed for wellness and disease trends. To date, consumers have been reluctant to share personal health information for a variety of reasons. To explore how financial rewards may influence data sharing, the concept of Markets of Data (MoDAT) is applied to health information. Results from a global online survey show that a previously uncovered group of consumers exists who are willing to sell their de-identified personal health information. Incorporating this information into existing health research databases has the potential to improve healthcare worldwide.

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  • Trust addresses access control issues; motivation explains consumer eagerness and willingness to share; community incorporates social aspects of sharing; and informed consent such as Portable Legal Consent provides the legal constructs necessary to safeguard privacy.
  • Exploring Markets of Data for Personal Health Information

    1. 1. Exploring Markets of Data for Personal Health Information K. Thomas Pickard ICDM 2014: Designing the Market of Data - for Practical Data Sharing via Educational and Innovative Communications December 14, 2014
    2. 2. How I got here 2
    3. 3. My passion • Not golf • Research about health information sharing 3
    4. 4. Why health information sharing? • Implications for improving health 4
    5. 5. Two examples 1. Rare diseases 2. Common diseases 5
    6. 6. Meet the Beery’s 6 Source: http://www.nature.com/news/2011/110615/full/news.2011.368.html
    7. 7. DNA sequencing finds rare disease 7 • Dopa-responsive dystonia in children • Mom read about condition in newspaper • Single change on SPR gene • Improved with serotonin therapy Chromosome 2 SPR
    8. 8. Rare disease in U.S. • Defined to be 1 in 1,500 people Source: https://www.govtrack.us/congress/bills/107/hr4013 8
    9. 9. 30M rare disease patients in U.S. Another ~3,700 single-gene diseases suspected 3,500 single-gene diseases identified 9 Source: http://www.ncbi.nlm.nih.gov/pubmed/23504071
    10. 10. 95% of rare diseases have no FDA approved drug 10 Source: http://globalgenes.org/rare-diseases-facts-statistics/
    11. 11. Implications • Rare disease networks • Sharing eliminates guesswork • Solve more cases 11  What about common diseases?
    12. 12. Diabetes • Disease prevalence: World: 4% to ~40% U.S.: ~10% • Disease types: 30+ • U.S.: ~30M people  30 types  1M per type 12 Sources: http://care.diabetesjournals.org/content/29/suppl_1/s43.full.pdf http://healthintelligence.drupalgardens.com/content/prevalence-diabetes-world-2013
    13. 13. Schizophrenia • Disease prevalence: World: ~1% (with little variation) • Disease types: 7 or more • U.S. ~3.5M people  7 types  500k per type 13 Source: http://www.ncbi.nlm.nih.gov/pubmed/25219520 Nobel laureate John Nash
    14. 14. Autism spectrum disorders • Disease prevalence: World: ~1% • 10% explained by genetics • U.S. 2M people  > 6 types  ~300k per type 14 Sources: http://www.cdc.gov/ncbddd/autism/documents/asd_prevalence_table_2013.pdf http://www.nature.com/nature/journal/v515/n7526/full/nature13772.html Autistic activist Temple Grandin
    15. 15. Hospitals and patients • U.S. hospitals: ~6,000 facilities • U.S. population: ~320M people 15 Source: http://www.targetmap.com/viewer.aspx?reportId=3065  One hospital per 50,000 people
    16. 16. Patients per U.S. hospital • 1M diabetes patients 170 patients per hospital  1 in 300 • 500k schizophrenic patients 80 patients per hospital  1 in 625 • 300k autism spectrum disorder patients 50 patients per hospital  1 in 1,000 16  Rare disease: 1 in 1,500 Source: http://www.cdc.gov/datastatistics/
    17. 17. Implications • Complex and rare disease can look similar • Hospitals must share to solve diseases • 5,000 people with a disease are necessary for good genomic results 17 Source: http://jbjs.org/content/96/5/e38
    18. 18. A shift in consumer attitudes 18
    19. 19. “Given the choice between pizza and privacy…a remarkable number will opt for the pizza.” 19
    20. 20. Insights 1. Consumers are willing to share health data under the right conditions. 2. Education seems to play a strong role. 3. Consumers want to be connected to their data. 4. Develop models to encourage sharing. 20
    21. 21. Health Information Sharing Model Trust Motivation Community Informed Consent Consumer 21
    22. 22. Can we go faster with MoDAT? • Markets of Data (MoDAT) • Will consumers share if paid? • Online survey • 400 participants • 7 questions 22 Ask Your Target Market (aytm.com)
    23. 23. A global survey Responses by Continent – Asia (40%) – Europe (35%) – North America (17%) – Africa (5%) – Oceania (2%) – South America (2%) 59 countries represented South America 2% Oceania 2% Africa 5% North America 17% Europe 35% Asia 40% Responses by Continent 23
    24. 24. Willing to share When asked about sharing, 88% responded “Yes” or “In some cases.” 50% 33% 17% 0% 10% 20% 30% 40% 50% 60% Yes In some cases No If I could remove my name, age, etc. from my health information, I would share it... 24
    25. 25. Share with financial reward 65% 26% 9% 0% 10% 20% 30% 40% 50% 60% 70% More likely to share my health information Neither more likely nor less likely to share my health information Less likely to share my health information If I were rewarded financially, then I would be... 25
    26. 26. Share/Sell Data Types 0 20 40 60 80 100 120 140 160 180 200 Responses I would share/sell these types of health information... Share Sell 26
    27. 27. Share/Sell with… 0 20 40 60 80 100 120 140 160 180 Responses I would share/sell my health information with... Share Sell 27
    28. 28. Financial Reward vs Difference in Average Number of Items Selected -2.00 0.00 2.00 Yes No DifferenceinAverageNumberofItemsSelected Response to Selling with Financial Reward "Yes/No" Response to Selling with Financial Reward vs Difference in Average Number of Items Selected 28
    29. 29. Household Income 0% 10% 20% 30% 40% 50% 60% Annual Household Income Over half of the respondents reported a household income of less than $25,000 USD. 29
    30. 30. One-time payment 0% 5% 10% 15% 20% 25% Responses(percent) In exchange for this health information, I would expect to receive a one-time payment of: 30
    31. 31. Age: Sample vs World Age ranges from 18 to 74. Average age cohort is 30-34 years. 0% 5% 10% 15% 20% 25% 30% Age Cohort Age Sample population World population Source: U.S. Census Bureau, 2014 World Midyear Population by Age 31
    32. 32. Age and reward 0 10 20 30 40 50 60 70 18-24 25-29 30-34 35-39 40-44 45-49 Responses Age cohort Age Cohort vs "Yes" Response to Selling with Financial Reward R² = 0.87 32
    33. 33. Age and one-time payment $0 $50 $100 $150 $200 $250 18-24 25-29 30-34 35-39 40-44 45-49 Averageone-timepayment(USD) Age cohort Age vs Average One-time Payment (20 or more responses per cohort) R² = 0.68 33
    34. 34. GDP and one-time payment $- $50 $100 $150 $200 $250 $300 $350 $- $500 $1,000 $1,500 $2,000 $2,500 $3,000 One-timepayment(USD) GDP in $B GDP $B vs Average One-time Payment (excluding lowest and highest expected payment amounts) $56 $169 $250 $304 $489 $878 $1,875 $2,013 $2,471 R² = 0.46 Croatia Romania Philippines Malaysia Poland Indonesia India Italy United Kingdom 34 Source: United Nations 2012 GDP Data
    35. 35. Limitations 1. Paid to participate 2. Convenience sample (n=400) 3. Education level 35
    36. 36. Our sample was educated 0% 5% 10% 15% 20% 25% 30% Education 64% Completed at least four years of college vs 25% Global tertiary education enrollment average 36 Source: UNESCO Institute for Statistics in EdStats. “Teritary education – A global report,” 2012.
    37. 37. Summary • Rare and complex diseases can look similar • Share health information to solve disease • Some consumers view sharing health information like a “free pizza” 37
    38. 38. Thank you K. Thomas Pickard ktpickard [at] startcodon.org @kthomaspickard www.linkedin.com/in/kthomaspickard Blog: www.genomedad.com 38

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