Biomedical Informatics Meets Big DataImplications for Research, Training, and Policy

Philip R.O. Payne, PhD, FACMIThe Ohio State University, Department of Biomedical Informatics

Justin B. Starren, MD, PhD, FACMINorthwestern University, Biomedical Informatics Center (NUBIC)

Peter J. Embi, MD, MS, FACMIThe Ohio State University, Department of Biomedical Informatics

ACMI 2016 Winter Symposium

Big Data in BiomedicineTerms, Definitions, and Concepts

What Makes Big Data “Big”?

VolumeVelocity

Variability Veracity

Rethinking Science

Aug. 2015VIVO 2015 ©Starren 2015

4

Theory Experimentation

Computation

Variants in TerminologyBig data refers to a process that is used when traditional data mining and handling techniques cannot uncover the insights and meaning of the underlying data.1

Data science is a broad field that refers to the collective processes, theories, concepts, tools and technologies that enable the review, analysis and extraction of valuable knowledge and information from raw data.1

Data analytics is a process of sifting, organizing, and examining vast amounts of information and then drawing conclusions based on that analysis.2

Sources: 1http://www.techopedia.com/; 2http://discovery.osu.edu/

Theories andMethods

Sources

Applications

SAMPLING

FILTERING

Big Data is About Filtering

Data per Individual

Kilobytes PetabytesTerabytesGigabytesMegabytes

Millions

Thousands

One

Claims

EHR Data

Basic Genomics

Clinical Imaging

Microbiomics, Cell Population Sequencing,

Proteomics

PHR Data

Microarray

Exposome / Sensor

Survey Data

Types of Big Data in Biomedicine

Epigenomics, etc

Num

ber o

f Pati

ents

Nec

essa

ry fo

r Big

Dat

a

Big Data in Biomedicine

Source: FSM Big Data Survey 2013

Note that “size” was not the

most common descriptor.

Data Scientist: Sexiest Job of the 21st Century

Source: Harvard Business Review, http://hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-century/

The “Hype Cycle” and Big Data

Sources: Gartner Whitepaper, “Gartner Hype Cycle for Emerging Technologies 2014”

Big DataData Science

The Healthcare Information Age?

Characteristics Before The Printing Press After The Printing Press

Cost HighPrinted materials only available to the extremely wealthy

LowPrinted materials become cost effective for general public

Ubiquity LowCopies of printed materials had to be transcribed by hand, limiting number of instances

HighMass production of printed materials leads to broad dissemination and access

Reproducibility LowErrors of transcription and omission very common

HighSystematic printing processes ensure fidelity of materials

The Advent of the Printing Press and the 1st Information Age

Characteristics Before HIT and Big Data After HIT and Big Data

Cost HighData sets generated and/or curated on a need basis

LowData production and storage costs decreasing in excess of Moores Law

Ubiquity LowProprietary data situated in vendor or project-specific repositories and formats

HighData becoming a renewable resource enabled by diverse re-use scenarios

Reproducibility LowErrors of transcription and omission very common

HighLinked public data enables the creation of “commons” model

Growth in HIT and Big Data in the Healthcare Information Age

Data, Data, Everywhere…

Molecular Phenotype

Environment

Enterprise Systems and Data Repositories:EHR, CRMS, Data Warehouse(s)

Emergent SourcesPHR, Instruments, Etc.

UbiComp + Sensors

13The Data Commons = An Ecosystem of Open Data and Tools That Can Be Adopted and Adapted in order to generate value

Open data Open source Open methodology Open peer review Open access Open educational resources

Cumulative, transparent, and reproducible science and innovation

Watson M. When will ‘open science’ become simply ‘science’? Genome biology. 2015;16(1):101.

Using Big Data to Answer Questions

Sources: IBM Whitepaper, “Learn Why Analytics Drive Better Business Outcomes Now”

Align

AnticipateAct

Transform

Learn

Goals and Information

See and Shape OutcomesDecide and Optimize

Data-driven Decision MakingIm

prov

e Ev

ery

Out

com

e

Is this really Data

Science?

Virtuous Cycle of Data Science and Informatics

Biological and Social Processes

Data

Data Science focuses on converting data (esp. big data) into knowledge

InformaticsIncludes both analysis and

creation of digital interventions

Observed, Measured or Instrumented to

Produce

New Knowledge and Insight

VIVO 2015 ©Starren 2015

Precision Medicine“Big Data meets Personalized Medicine”

16

Genotype

Current State

Environment

HealthHistory

Time

OptimalCurrent State

OptimalFuture State

ProbableFuture State

Over

all H

ealth

Con

ditio

n

Genetic Risk

Prediction

QualityInitiatives

Aug. 2015

Precision MedicineBig Data meets Personalized Medicine

Data ScienceThe Science of Big DataBig Data

The four V’sFiltering vs. Sampling

InformaticsData Science meets the Human Condition

Data AnalyticsProblem Solving Using Big Data

Relationship Models

Data ScienceInformatics

Synonymy

Problem: Informatics has an applied and sociotechnical component.

Data Science

Informatics

Subsumption

Problem: Informatics does implementation.

New Term for Big Data at NIHUses Subsumption model 1

Data Science

Informatics

Subsumption 2

Problem: Data Scientists will not accept this view. Data Science students learn things that informatics student often do not.

DataScience

ImplementationScience

Sociotechnical

Informatics

Intersection

Problem: Data Science does not have have a sociotechnical or Implementation science component.

DataScience

ImplementationScience

Sociotechnical

InformaticsCognitive

Workflow

HCI

Intersection 2

Problem: Sociotechnical and Implementation science are not the same.

ImplementationScience

SociotechnicalInformatics

DataScience

Informatics vs. Data Science?

InformaticsData Science meets the Human Condition

Thank you for your time and attention!• philip.payne@osumc.edu• justin.starren@northwestern.edu