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iHT² CMIO & Physician & Executive Summit “Using Big Data to Shift from Evidence-based Practice to Practice-based Evidence” with Christopher Longhurst

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  • 1. Using Big Data to Shift from Evidence-based Practice to Practice-based Evidence Christopher Longhurst, MD, MS Chief Medical Information Officer, Stanford Children’s Health Associate Professor of Pediatrics and Medicine, Stanford University
  • 2. 2 Stanford Children’s Health • Opened in 1991 • Mission: To provide extraordinary family-centered care • 311 bed pediatric/obstetric tertiary-care facility • Hospital stats • 4200 Deliveries • 13k Discharges • 300k Clinic visits
  • 3. 3 Pediatrics, December 2005
  • 4. 4 Pediatrics, May 2010
  • 5. 5 Pediatrics, May 2010
  • 6. 6 AMIA Proceedings, 2009
  • 7. 7 New England Journal of Medicine, Nov 2011
  • 8. 9 Big Data and the Gartner Hype Cycle
  • 9. 10 “Big Data” Signals in Biomedicine & Healthcare • Physiologic signals (remote monitoring, quantified self) • Images (radiology, pathology, dermatology, ophthalmology) • Omics (genomics, microbiomics, proteonomics) • Social data (network analysis, crowdsourced) • EMR data (structured and unstructured)
  • 10. 11 “Big Data” Signals in Biomedicine & Healthcare • Physiologic signals (remote monitoring, quantified self) • Images (radiology, pathology, dermatology, ophthalmology) • Omics (genomics, microbiomics, proteonomics) • Social data (network analysis, crowdsourced) • EMR data (structured and unstructured)
  • 11. 12 Physiologic Data – Stanford (Science, 2010)
  • 12. 13 Physiologic Data – Silicon Valley
  • 13. 14 “Big Data” Signals in Biomedicine & Healthcare • Physiologic signals (remote monitoring, quantified self) • Images (radiology, pathology, dermatology, ophthalmology) • Omics (genomics, microbiomics, proteonomics) • Social data (network analysis, crowdsourced) • EMR data (structured and unstructured)
  • 14. 15 Images – Stanford (AMIA Annu Symp Proc, 2008)
  • 15. 16 Images – Silicon Valley
  • 16. 17 “Big Data” Signals in Biomedicine & Healthcare • Physiologic signals (remote monitoring, quantified self) • Images (radiology, pathology, dermatology, ophthalmology) • Omics (genomics, microbiomics, proteonomics) • Social data (network analysis, crowdsourced) • EMR data (structured and unstructured)
  • 17. 18 Omics – Stanford (Lancet, May 2010)
  • 18. 19 “Big Data” Signals in Biomedicine & Healthcare • Physiologic signals (remote monitoring, quantified self) • Images (radiology, pathology, dermatology, ophthalmology) • Omics (genomics, microbiomics, proteonomics) • Social data (network analysis, crowdsourced) • EMR data (structured and unstructured)
  • 19. 20 Social Data – Stanford (JAMIA, 2013)
  • 20. Social Data – Silicon Valley Full disclosure: I serve on the medical advisory board for Doximity.
  • 21. This infographic shows a snapshot of Northern California doctors and their referrals, where each doctor is represented by a blue dot and the connecting lines represent a referral.
  • 22. US primary care connections 70% of PCP Colleagues are within 100 miles
  • 23. US specialist connections are more regional
  • 24. 25 AHRQ, 2007 “Information technology must be deployed and reengineered to overcome growing problems associated with information overload. Finally, and most importantly, patients will have to be engaaged on multiple levels to become ‘coproducers’ in a safer practice of medical diagnosis.”
  • 25. 26 “Big Data” Signals in Biomedicine & Healthcare • Physiologic signals (remote monitoring, quantified self) • Images (radiology, pathology, dermatology, ophthalmology) • Omics (genomics, microbiomics, proteonomics) • Social data (network analysis, crowdsourced) • EMR data (structured and unstructured)
  • 26. 28 EMR Data – Stanford (AMIA Proceedings, 2009)
  • 27. 29 Finding Labs and Events that Predict Harm 2 True Positive Rate and False Positive Rate Best performing labs and events  Best sensitivity: urea nitrogen  Best specificity: feeding tube response  Best overall: indirect bilirubin
  • 28. 30 IEEE Intelligent Systems, April 2009 “The first lesson of web-scale learning is to use available large-scale data rather than hoping for annotated data that isn’t available.”
  • 29. 31 EMR Data – Stanford (Nature Pharmacology 2013)
  • 30. 32 2012 IOM Report on “Learning Healthcare System”
  • 31. 33 Science Translational Medicine, Nov 2010
  • 32. How do we ensure our healthcare system learns from every patient, at every visit, every time?
  • 33. Christopher Longhurst, MD, MS clonghurst@stanfordchildrens.org "We make a living by what we get, we make a life by what we give." - Winston Churchill
  • 34. Upon this gifted age, in its dark hour, Rains from the sky a meteoric shower Of facts…they lie unquestioned, uncombined. Wisdom enough to leech us of our ill Is daily spun, but there exists no loom To weave it into fabric… Edna St. Vincent Millay, Upon this age, that never speaks its mind. In: Colleted Sonnets, 1939.