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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

Thank youEmail: ithan.peltan@imail.org

Twitter: @ipeltan