Presentation Title Calibri 40 Pt. › wp-content › uploads › PELTAN-BIG-DATA.pdf · Disclosures...
Transcript of Presentation Title Calibri 40 Pt. › wp-content › uploads › PELTAN-BIG-DATA.pdf · Disclosures...
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Big DataIthan Peltan, MD, MSc
Assistant Professor, Intermountain HealthcareAdjunct Assistant Professor of Internal Medicine, University of Utah
Twitter: @ipeltan
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Disclosures
• NIH (K23 GM129661, U01 HL143505)• CDC • Intermountain Research & Medical Foundation• Research support to institution from:o Immunexpress Inc.oAsahi Kasei Pharmao Janssen Pharmaceuticals
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What is Big Data?
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Structured Unstructured
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Big data
Unstructured EMR data
Structured EMR data
Claims dataLabs
Vitals
Structured data entry
Free-text notes
Diagnostic tests
Other databases
Prescriptions
Embedded sensors
Wearables
Environmental
Images
Vital records
GenealogicMany, many others
MD/hospital data
Trackers
Meds
Clinical
Adapted in part from: Iwashyna TJ, Liu V. What's so different about big data? Ann Am Thorac Soc. 2014;11:1130–5.
AV data
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VelocityVelocity
What is big data?
Volume Variety
Laney D. 3D Data Management: Controlling Data Volume, Velocity, and Variety. Gartner Blog Network. 2001. https://blogs.gartner.com/doug-laney/files/2012/01/ad949-3D-Data-Management-Controlling-Data-Volume-Velocity-and-Variety.pdf
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Images courtesy of Wikimedia Commons, U.S. Air Force, Pixabay, Needpix
THEN NOW
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What does Big Data mean for sepsis care?
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Classical epidemiology
Prediction
Data mining
Operational analytics
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Classical epidemiology
Prediction
Data mining
Operational analytics
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Classical epidemiology
Rhee C et al. Incidence and trends of sepsis in US hospitals using clinical vs claims data, 2009-2014. JAMA. 2017;318:1241–9.
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Classical epidemiology
Liu VX, Fielding-Singh V, Greene JD, et al. Am J Respir Crit Care Med. 2017;196:856–63. Peltan ID, Brown SM, Bledsoe JR, et al. Chest. 2019;155:938–46. Seymour CW, Gesten FC, Prescott HC, et al. New Engl J Med. 2017;376:2235–44.
StudyNumber of sepsis patients
Adjusted mortality (OR) per hour delay in antibiotics
Seymour 2017 49,331 1.03
Liu 2017 35,000 1.09
Peltan 2019 10,811 1.16
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Perils of “Big Data” for classical epidemiology
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Classical epidemiology
Prediction
Data mining
Operational analytics
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PredictionGenerative
adversarial networks
Convolutional neural networks
Random forests
Regression analysis
Human decisions
Adapted from: Beam AL, Kohane IS. Big data and machine learning in health care. JAMA. 2018;319:1317–8.
Data/sample size1 10 102 103 104 105 106 107 108 109 1010
Rela
tive
hum
an-to
-mac
hine
inpu
tGeneralized adversarial networks
Diabetic retinopathy
identification
Facebook photo tagging
Google searchMELD
score
CHA2DS2-VASC score
EMR-based CV risk prediction
Clinical wisdom
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Prediction
Henry KE et al. A targeted real-time early warning score (TREWScore) for septic shock. Sci Transl Med. 2015;7:299ra122–2.
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Classical epidemiology
Prediction
Data mining
Operational analytics
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Data mining
Knox DB et al. Phenotypic clusters within sepsis-associated multiple organ dysfunction syndrome. Intensive Care Med. 2015;41:814–22.
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Data mining
Seymour CW et al. Derivation, validation, & potential treatment implications of novel clinical phenotypes for sepsis. JAMA. 2019;321:2003.
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Classical epidemiology
Prediction
Data mining
Operational analytics
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Operational analytics
Data for June 8, 2020 from coronavirus.utah.gov // CDC (https://www.cdc.gov/coronavirus/2019-ncov/covid-data/forecasting-us.html)
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Operational analytics
Regression discontinuity
Interrupted time series
Difference-in-differences
Walkey AJ, Drainoni M-L, Cordella N, Bor J. Ann Am Thorac Soc. 2018;15:523–9.
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Shared characteristics Reliable dataEase of collection
Tackle novel problems
Unreliable data (“Garbage in/garbage out”)Ethical challenges
Classical epidemiology Improved power & precision Minimal important difference
Prediction Improved accuracyGeneralizability
Real time options
Practical applicationGeneralizability
Black box problemComplex analytics
Data mining Identify novel patternsPersonalized care
Data mining/alpha inflation
Operational analytics Inputs to learning health systemReal time data
Risk of misleading analyses
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Prediction
Obermeyer Z et al. Dissecting racial bias in an algorithm used to manage the health of populations. Science. 2019;366:447–53.
Develop prediction model to predict cost of care
Use model to select patients for care
coordination program
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Big Data for sepsisPotential & Peril
• Know your data• Choose analytic methods wisely• Watch out for bias• Consider adverse effects