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Big DataIthan Peltan, MD, MSc
Assistant Professor, Intermountain HealthcareAdjunct Assistant Professor of Internal Medicine, University of Utah
Twitter: @ipeltan
Disclosures
• NIH (K23 GM129661, U01 HL143505)• CDC • Intermountain Research & Medical Foundation• Research support to institution from:o Immunexpress Inc.oAsahi Kasei Pharmao Janssen Pharmaceuticals
What is Big Data?
Structured Unstructured
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
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
Images courtesy of Wikimedia Commons, U.S. Air Force, Pixabay, Needpix
THEN NOW
What does Big Data mean for sepsis care?
Classical epidemiology
Prediction
Data mining
Operational analytics
Classical epidemiology
Prediction
Data mining
Operational analytics
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.
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
Perils of “Big Data” for classical epidemiology
Classical epidemiology
Prediction
Data mining
Operational analytics
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
Prediction
Henry KE et al. A targeted real-time early warning score (TREWScore) for septic shock. Sci Transl Med. 2015;7:299ra122–2.
Classical epidemiology
Prediction
Data mining
Operational analytics
Data mining
Knox DB et al. Phenotypic clusters within sepsis-associated multiple organ dysfunction syndrome. Intensive Care Med. 2015;41:814–22.
Data mining
Seymour CW et al. Derivation, validation, & potential treatment implications of novel clinical phenotypes for sepsis. JAMA. 2019;321:2003.
Classical epidemiology
Prediction
Data mining
Operational analytics
Operational analytics
Data for June 8, 2020 from coronavirus.utah.gov // CDC (https://www.cdc.gov/coronavirus/2019-ncov/covid-data/forecasting-us.html)
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.
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
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
Big Data for sepsisPotential & Peril
• Know your data• Choose analytic methods wisely• Watch out for bias• Consider adverse effects
