Using Big Data to Shift from Evidence-   based Practice to Practice-based              Evidence            Christopher Lon...
Packard Children’s Hospital at Stanford•   Opened in 1991•   Mission: To provide extraordinary family-centered care•   311...
Pediatrics, December 2005
Pediatrics, May 2010
New England Journal of Medicine, Nov 2011
Hype Cycle
“Big Data” Signals in Biomedicine & HealthcarePhysiologic signals (remote monitoring, quantified self)Images (radiology, p...
Physiologic Data – StanfordScience Translational Medicine, 2010
Physiologic Data – Silicon Valley
Images – StanfordAMIA Annu Symp Proc, 2008
Images – Silicon Valley
Omics – StanfordLancet, May 2010
Omics – Silicon Valley
Social Data – Silicon ValleyFull disclosure: I serve onthe medical advisoryboard for Doximity.
AHRQ, 2007“Information technology must be deployedand reengineered to overcome growingproblems associated with information...
EMR Data - StanfordAMIA Proceedings, 2009
Finding Labs and Events that Predict HarmTrue Positive Rateand False PositiveRateBest performing labsand events  Best sens...
IEEE Intelligent Systems, April 2009“The first lesson of web-scale learning isto use available large-scale data ratherthan...
Science Translational Medicine, Nov 2010
2012 IOM Report on “Learning Healthcare System”
Pediatrics, May 2011Visit http://CRIT.stanford.eduto join a learning collaborative
EMR-Enabled Checklist
Preliminary ResultsRN in involvement on      Added approximately 1rounds increased from     minute to each patient’s56% to...
Compliance w/ CABSI Prevention Bundle   100%                                                *        *    90%    80%    70...
BMJ, Feb 2013“Systems more likely to succeedprovided advice for patients in additionto practitioners (OR 2.77).”
Journal of Quality and Patient Safety, Aug 2012
Journal of Participatory Medicine, Dec 2012
New England Journal of Medicine, Dec 2012“Clinical decision support algorithms will be derived entirelyfrom data, not expe...
How do we ensure that the healthcare system learns from every patient, at every visit, every                     time?
Christopher Longhurst, MDclonghurst@lpch.org
iHT2 Health IT Summit San Francisco 2013 - Christopher Longhurst, MD, CMIO, Lucile Packard Children's Hospital, Case Study...
iHT2 Health IT Summit San Francisco 2013 - Christopher Longhurst, MD, CMIO, Lucile Packard Children's Hospital, Case Study...
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iHT2 Health IT Summit San Francisco 2013 - Christopher Longhurst, MD, CMIO, Lucile Packard Children's Hospital, Case Study "Using Big Data to Shift from Evidence-based Practice to Practice-based Evidence"

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Christopher A. Longhurst, MD, MS
Chief Medical Information Officer
Lucile Packard Children’s Hospital
Clinical Associate Professor of Pediatrics
Stanford School of Medicine

Case Study "Using Big Data to Shift from Evidence-based Practice to Practice-based Evidence"

Packard Children's Hospital at Stanford has been on a journey towards a comprehensive electronic medical record (EMR) since 2004. These efforts resulted in national attention in 2010 with the publication of the first-ever correlation between implementation of computerized physician order entry (CPOE) and a decrease in hospital-wide mortality. This landmark was followed in 2011 with a paper in the NEJM describing the first documented use of aggregate EMR data to make a real-time patient care decision. In this talk, Dr. Chris Longhurst will share the "story behind the story" of these accomplishments and highlight opportunities for using applied clinical informatics to improve the value of healthcare we deliver to our patients.

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iHT2 Health IT Summit San Francisco 2013 - Christopher Longhurst, MD, CMIO, Lucile Packard Children's Hospital, Case Study "Using Big Data to Shift from Evidence-based Practice to Practice-based Evidence"

  1. 1. Using Big Data to Shift from Evidence- based Practice to Practice-based Evidence Christopher Longhurst, MD, MS Associate Professor of Clinical Pediatrics, Stanford University Chief Medical Information Officer, Packard Children’s Hospital
  2. 2. Packard Children’s Hospital at Stanford• Opened in 1991• Mission: To provide extraordinary family-centered care• 311 bed pediatric/obstetric tertiary-care facility• Hospital stats (FY 2010) • 4500 Deliveries • 13k Discharges • 140k Clinic visits
  3. 3. Pediatrics, December 2005
  4. 4. Pediatrics, May 2010
  5. 5. New England Journal of Medicine, Nov 2011
  6. 6. Hype Cycle
  7. 7. “Big Data” Signals in Biomedicine & HealthcarePhysiologic signals (remote monitoring, quantified self)Images (radiology, pathology, dermatology, ophthalmology)Omics (genomics, microbiomics, proteonomics)Social data (network analysis, crowdsourced)EMR data (structured and unstructured)
  8. 8. Physiologic Data – StanfordScience Translational Medicine, 2010
  9. 9. Physiologic Data – Silicon Valley
  10. 10. Images – StanfordAMIA Annu Symp Proc, 2008
  11. 11. Images – Silicon Valley
  12. 12. Omics – StanfordLancet, May 2010
  13. 13. Omics – Silicon Valley
  14. 14. Social Data – Silicon ValleyFull disclosure: I serve onthe medical advisoryboard for Doximity.
  15. 15. AHRQ, 2007“Information technology must be deployedand reengineered to overcome growingproblems associated with informationoverload. Finally, and most importantly,patients will have to be engaged onmultiple levels to become ‘coproducers’in a safer practice of medical diagnosis.”
  16. 16. EMR Data - StanfordAMIA Proceedings, 2009
  17. 17. Finding Labs and Events that Predict HarmTrue Positive Rateand False PositiveRateBest performing labsand events Best sensitivity: urea nitrogen Best specificity: feeding tube response Best overall: indirect bilirubin 18
  18. 18. IEEE Intelligent Systems, April 2009“The first lesson of web-scale learning isto use available large-scale data ratherthan hoping for annotated data that isn’tavailable.”
  19. 19. Science Translational Medicine, Nov 2010
  20. 20. 2012 IOM Report on “Learning Healthcare System”
  21. 21. Pediatrics, May 2011Visit http://CRIT.stanford.eduto join a learning collaborative
  22. 22. EMR-Enabled Checklist
  23. 23. Preliminary ResultsRN in involvement on Added approximately 1rounds increased from minute to each patient’s56% to 79% of roundsencounters mean 76 secondsCare change made in 1/3 median 56 secondsof patient encounters
  24. 24. Compliance w/ CABSI Prevention Bundle 100% * * 90% 80% 70% * 60% 50% 40% 30% 20% 10% 0% Insertion Line Dressing Cap Port Bundle Necessity Change Change Change
  25. 25. BMJ, Feb 2013“Systems more likely to succeedprovided advice for patients in additionto practitioners (OR 2.77).”
  26. 26. Journal of Quality and Patient Safety, Aug 2012
  27. 27. Journal of Participatory Medicine, Dec 2012
  28. 28. New England Journal of Medicine, Dec 2012“Clinical decision support algorithms will be derived entirelyfrom data, not expert opinion, market incentives, orcommittee consensus. The huge amount of data availablewill make it possible to draw inferences from observationsthat will not be encumbered by unknown confounding.”
  29. 29. How do we ensure that the healthcare system learns from every patient, at every visit, every time?
  30. 30. Christopher Longhurst, MDclonghurst@lpch.org

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