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. 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. 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
- 5. New England Journal of Medicine, Nov 2011
- 7. Hype Cycle
- 8. “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)
- 9. Physiologic Data – Stanford Science Translational Medicine, 2010
- 10. Physiologic Data – Silicon Valley
- 11. Images – Stanford AMIA Annu Symp Proc, 2008
- 12. Images – Silicon Valley
- 13. Omics – Stanford Lancet, May 2010
- 14. Omics – Silicon Valley
- 15. Social Data – Silicon Valley Full disclosure: I serve on the medical advisory board for Doximity.
- 16. 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 engaged on multiple levels to become ‘coproducers’ in a safer practice of medical diagnosis.”
- 17. EMR Data - Stanford AMIA Proceedings, 2009
- 18. Finding Labs and Events that Predict Harm 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 1 8
- 19. 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.”
- 20. Science Translational Medicine, Nov 2010
- 21. 2012 IOM Report on “Learning Healthcare System”
- 22. Pediatrics, May 2011 Visit http://CRIT.stanford.edu to join a learning collaborative
- 25. Preliminary Results RN in involvement on Added approximately 1 rounds increased from minute to each patient’s 56% to 79% of rounds encounters mean 76 seconds Care change made in 1/3 median 56 seconds of patient encounters
- 26. 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
- 27. BMJ, Feb 2013 “Systems more likely to succeed provided advice for patients in addition to practitioners (OR 2.77).”
- 28. Journal of Quality and Patient Safety, Aug 2012
- 29. Journal of Participatory Medicine, Dec 2012
- 30. New England Journal of Medicine, Dec 2012 “Clinical decision support algorithms will be derived entirely from data, not expert opinion, market incentives, or committee consensus. The huge amount of data available will make it possible to draw inferences from observations that will not be encumbered by unknown confounding.”
- 31. How do we ensure that the healthcare system learns from every patient, at every visit, every time?