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When Perfect Algorithms Meet Imperfect Healthcare Systems: My talk at the Machine Learning for Healthcare Meeting 2018

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When Perfect Algorithms Meet Imperfect Healthcare Systems: My talk at the Machine Learning for Healthcare Meeting 2018

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When Perfect Algorithms Meet Imperfect Healthcare Systems: My talk at the Machine Learning for Healthcare Meeting 2018

  1. 1. When Perfect Algorithms Meet Imperfect Healthcare Systems Joyce Lee, MD, MPH Robert Kelch Professor of Pediatrics doctorasdesigner.com @joyclee on Twitter
  2. 2. Lenovo (Grant Funding) Disclosures @joyclee
  3. 3. www.doctorasdesigner.com medium.com/@joyclee Professor, Robert Kelch Professor of Pediatrics Clinical/Health Outcomes Research (Obesity/Diabetes) Learning Health Systems (Clinical Informatics, Quality Improvement, Participatory Design) Applications of Emerging Technologies in Healthcare (Ethnography, Analysis of Diabetes Online Maker Communities) @joyclee
  4. 4. @joyclee Agenda Why Diabetes Data Matters Diabetes Data Barriers Patient Innovation with Data
  5. 5. Why Diabetes Data Matters @joyclee
  6. 6. @joyclee It’s an infinite amount of math over a lifetime
  7. 7. It’s an infinite amount of math over a lifetime @joyclee
  8. 8. Blood Glucose Meters Continuous Glucose Monitoring (CGM) Systems Insulin Pumps
  9. 9. @joyclee Glucose levels change all the time Puberty Growth Activity
  10. 10. Glucose levels impact health outcomes
  11. 11. Management is truly patient-driven Provider Time managing diabetes (1 hour in a year?)Patient Time Spent Managing Diabetes
  12. 12. Data Nirvana for Cars @joyclee “The Autonomous Car”
  13. 13. The Singularity! @joyclee
  14. 14. @joyclee “The Artificial Pancreas” Continuous Glucose Monitor Insulin Pump Mobile Phone with Algorithm Data Nirvana for Diabetes
  15. 15. The Singularity! Goodbye Pediatric Endocrinologist! You have been eliminated! @joyclee
  16. 16. The Singularity! …not really @joyclee
  17. 17. @joyclee “The Artificial Pancreas” “Closing the Loop" Medtronic 670G DIY In Development
  18. 18. What are the Diabetes Data Barriers? @joyclee
  19. 19. No data Trapped data Infrequent data Isolated data Confusing data Biased data Judgmental data Suspicious data @joyclee
  20. 20. No data @joyclee
  21. 21. We still give out mostly analog tools ”It was unacceptable to me in 2002, when my son was diagnosed, to be given needles, an insulin vial, and a piece of paper.” - Jeff Brewer, Bigfoot CEO @joyclee
  22. 22. Blood Glucose Meters (100%) Insulin Pumps (50%) Continuous Glucose Monitoring (CGM) Systems (15%) Connected Pens (<1%) AP Systems (<1%) Device Adoption Rates (Type 1 Diabetes) @joyclee
  23. 23. Diabetes tools are very expensive $1411 Starter Kit $596 Transmitter (every 3 months) $349/month for Sensors $70-80 Starter Kit $120-150/month for Sensors @joyclee
  24. 24. Insurance companies don’t like to pay for things like blood glucose test strips, connected pens, and CGMs
  25. 25. Burnout + Stigma + Burden + Frustration = No BG data @joyclee
  26. 26. Trapped data @joyclee
  27. 27. Email to fax: xxxxxxx-diabetes-fax@med.umich.edu @joyclee
  28. 28. @joyclee
  29. 29. @joyclee
  30. 30. People Prefer Mobile to Desktop @joyclee
  31. 31. Infrequent data @joyclee
  32. 32. Downloads and diabetes decision- making only happens at clinic 3-4 visits per year with 15 minutes per visit spent on data Meter/pump/CGM is collected at the clinic visit and a data download is exported as a PDF and scanned into the media tab of the chart @joyclee
  33. 33. Mental Health Issues 20,028 calls in 2017 @joyclee School Forms Supplies Prior Authorization Insurance Child Protection Services DME Pharmacy
  34. 34. Patient data review outside of clinic is reactive, not proactive Reporting blood glucose numbers over the phone PDF attached to a Portal Message (page limits) Fax (email-fax) 48 hour turnaround time
  35. 35. Isolated data @joyclee
  36. 36. @joyclee
  37. 37. We get upset when the Color Printer is not working in clinic @joyclee
  38. 38. Confusing data @joyclee
  39. 39. Technology is about culture change ”We’re living through this time right now where technology is a Trojan Horse for change. We say technology, but we mean innovation. We say interoperability and open data, but we mean culture change.” -Susannah Fox @joyclee
  40. 40. Onboarding Patients to the Patient Portal and Diabetes Data Platforms is no one’s job Not the Health IT Specialists Not the Medical Assistants Not the Educators Not the Doctors @joyclee
  41. 41. There is no formal patient education focused on how to download data, how to interpret it, and how to use it to adjust insulin doses “Am I supposed to still keep a logbook?” “I don’t feel comfortable making dose adjustments without first consulting with the CDE or endocrinologist. I mean, it’s his life, you know?” @joyclee
  42. 42. “Routine Downloaders” = Downloaded data at least once between routine clinic visits every 3 months which was four or more times in the past year (40%) “Routine Reviewers’’ = Reviewed the data at least ‘‘most of the time’’ he/she downloaded (27%) Lower A1c for Routine Reviewers: 7.8% vs. 8.6% (p=0.001) Diabetes Data Platform Use Wong et al, 2015 @joyclee
  43. 43. There is a lack of standardization for data visualizations @joyclee
  44. 44. @joyclee There is a lack of standardization for data visualizations
  45. 45. Biased data @joyclee
  46. 46. @joyclee Are they covering all carbs? Are they carb counting carefully? Are they inputting all the carbs / BG values into the pump? Are they bolusing before or after meals? Can I trust the data? Insulin lasts 2-3 hours There are A Lot of Unmeasured Variables
  47. 47. Judgmental data @joyclee
  48. 48. Guilt Loss of Control Alienation Obsession Data is not always empowering Isolation @joyclee CSCW 2017, Kaziunas, Ackerman, Lindtner, Lee
  49. 49. Suspicious data @joyclee
  50. 50. Patients and clinicians don’t trust a black box “I don’t know what it’s doing so how can I trust it?” “I wouldn’t give up my DIY AP” @joyclee
  51. 51. And that doesn’t even include linking this vital diabetes data to the clinical EHR data! @joyclee “Garbage data”
  52. 52. Lack of User-Centered Design for the EHR No culture of human-centered design in Health IT Design without a strategic understanding of what metrics are needed to improve care Build without enough input from users Deploy without iteration and testing Physician Resentment/Anger @joyclee
  53. 53. Clinical EHR: A combination of Microsoft Word and Pinterest @joyclee Clinicians are inputting data in unstructured format in the notes Data is being lost and/or underutilized Patient paper questionnaires and the diabetes data are scanned to PDF
  54. 54. @joyclee No data Trapped data Isolated data Infrequent data Confusing data Suspicious data Garbage data PDFMediaTab
  55. 55. How do we address these Diabetes Data Barriers? @joyclee
  56. 56. @joyclee PDFMediaTab
  57. 57. @joyclee MediaTab
  58. 58. @joycleeKumar RB et al J Am Med Inform Assoc. 2016 May;23(3):532-7.
  59. 59. @joyclee MediaTab
  60. 60. Patients, caregivers, clinicians and researchers work together to choose care based on best evidence; together they drive discovery as natural outgrowth of patient care; and ensure innovation, quality, safety and value, all in real-time. - C3N Project @joyclee “
  61. 61. Design for Users Quality Improvement Implementation, Sustainability, Outcomes Human-centered Design Data Outcomes Health IT @joyclee
  62. 62. Aim: To decrease the % of the population with HbA1c ≥ 9% and increase the % of the population with ≥ 0.5% HbA1c interval improvement Preference driven treatment and effective self- management Enhanced registry population management & Pre-visit planning Peer/community support Education/training to support technology use and patient viewing and problem solving with blood glucose data between visits Interventions/toolkits for addressing barriers to adherence Efficient use of technology and data to support care Access to care and regular follow-up Screening for depressionPsychosocial Support Shared decision making Partnership between engaged patients and the care team Effective use of EHR by diabetes team for population management • % of pts. testing ≥4 times/day or using CGM (6/7 days/week) • % of pts. giving 3 or more short-acting boluses/day • % of pts reviewing data between visits • % pts setting, documenting, and reviewing goals • % completed pre-visit planning • % with ≥ 4 visits per year • % of pts with annual CDE/RD/SW visit • % of pts on case mgmt. pathway • % pts screened for depression Developing a Clear Measurable Aim and a Theory of Change Care Process Measures @joyclee
  63. 63. Local Infrastructure to support an LHS Team (Director, Associate Director, Patient Partner/Advisor, Project Manager/Analyst) Patient Engagement (Patient Advisor/Advisory Board, Website/Newsletter) QI interventions (Depression Screening, High-risk Patient Recall, Portal Onboarding, Data Engagement Curriculum) Improving Data/Technology Systems @joyclee
  64. 64. @joyclee
  65. 65. @joyclee
  66. 66. @joyclee
  67. 67. @joyclee “No one is going to use that tool if you can’t BOLD the text!” Rogue commas Tool for Data Input Insulin sensitivity 12AM 90 2:30 AM 110 4 AM 230* 10 AM 160 Patient Instructions Insulin sensitivity 12AM 12AM 90 2:30 AM 110 4 AM 23, 0* 10 AM 160
  68. 68. @joyclee MediaTab Outcomes that Matter Tools for Structured Data Collection Patient Reported Outcomes (Portal Questionnaires) Clinical Interface Redesign Tools for Population Management
  69. 69. How might our machine learning colleagues help? @joyclee
  70. 70. Understand how, when, and what data is desired by different stakeholders to solve the right problems Think ecosystems not apps @joyclee
  71. 71. Population Level Monitoring Prioritization of high-risk patients Tools for Seamless Data Acquisition Data Pattern Review Documentation Communication
  72. 72. Watch patient experts solve problems @joyclee
  73. 73. Who’s the real medical expert? @joyclee
  74. 74. @joyclee
  75. 75. @joyclee
  76. 76. @joyclee
  77. 77. 21. France 22. Netherlands 23. New Zealand 24. Mexico 25. Croatia 26. South Africa 27. Israel 28. Japan 29. Switzerland 30. Hungary 31. Belarus 29,000+ Int’l 11. Australia 12. Portugal 13. Denmark 14. Germany/Austria 15. Turkey 16. Russia 17. Romania 18. Korea 19. Ireland 20. Brasil 55,000+ worldwide 1. Italy 2. UK 3. Spain 4. Sweden 5. Bulgaria 6. Norway 7. Poland 8. Czech+Slovakia 9. Canada 10. Finland
  78. 78. Software Innovations @joyclee
  79. 79. Hardware Innovations @joyclee
  80. 80. How do you get your CGM in the Cloud? @joyclee
  81. 81. “The DIY Artificial Pancreas” Loop/Loopkit OpenAPS AndroidAPS @joyclee
  82. 82. Interoperability and choice matter Reduce the mental burden Proactive not reactive Be transparent What should I consider and why? Give access to all of the data @joyclee
  83. 83. The machine is not an end. An airplane is not an end: it is a tool. Tools are created to allow you to reach greater goals, and machines should not distract from this pursuit. In fact, you should barely notice that they’re there. @joyclee “
  84. 84. Jacob Dwyer, Ashley Garrity, Valeria Gavrila, Emily Hirschfeld Dorene Markel, Ram Menon, Amy Ohmer, Lilia Verchichina, Michelle Wichorek, Pediatric Diabetes Team, The Nightscout Foundation, T1D Exchange Acknowledgements @joyclee Joyce Lee, MD, MPH doctorasdesigner.com joyclee@med.umich.edu http://www.nightscout.info/ https://www.nightscoutfoundation.org/contribute-data/ AHRQ, R21HS023865 and PCORI Engagement Award (1442-UMich)

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