Big BioData: The Story of Us, a talk by Emanuel Brown @ Webvisions PDX 2014

WEBVISIONS PORTLAND / MAY 9, 2014
Big BioData:
The Story of Us

!
US HEALTH INDUSTRY
$3  
TRILLION 
2015

© 2014 Citizen, Inc. Proprietary and confidential.
Fee-For-Service doesn’t scale, taxes innovation.
Current Model is Unsustainable
5
Significant amounts of
data collected for use
Federal incentives to
use Big BioData
Pushing for evidence-
based practices
5
Cost Pressure
Healthcare expenditure
reached 17.6% of GDP,
with over $600B inefficient
spending
!
Moving to risk sharing
model instead of  
fee-for-service
More Supply
In 2005 <30% of
physicians used EMRs.
Today, 75% hospitals use  
e-record systems and 50%
of US hospitals participate
in information exchange
Government
$40B payment incentives
for EMR-using providers.
!
Over $500M invested to
release Big BioData
housed within government
science institutions.

Healthcare has lagged behind other
industries in the use of big data.
6

AVERAGE BRAIN SCAN
10MB/S

TYPICAL  
20 MINUTE SCAN
GIGABYTESOF DATA PER PERSON

Common Characteristics
Big Data Overview
9
VOLUMEVARIETY VELOCITY

Additional Characteristics
Big Data Overview
10
VOLUMEVARIETY VELOCITY
VERACITY VALUE

The business case has been widely accepted.
Big Data Deployments are Increasing
11
70%
ENTERPRISES
56%  
SMB

71% of current big data analytics
rely on data visualization not
advanced intelligence engines.
12

!
HEALTH IT
$57  
BILLION 
2017

IN 2001
GENOME SEQUENCING
$10,000/MB

IN 2014
GENOME SEQUENCING
<1¢/MB

Patient
Data
Medical
Technology
Scientific
Research

Domains of Big BioData
Data From the Clinic
Patient Records
Bedside information. This data
is collected from patients for
the purpose of building clinical
histories and treatment plans.
Drug History
The prescriptions of patients
are tracked and stored with
their medical files along with
dosage and disease history.
Clinical Outcome
Whether from surgical,
rehabilitative, or drug-based
treatment, clinical outcomes of
patients are vital to health.
18

Medical Technologies
Point of Care
Medicine practiced at the point
where - and when - treatment
is required.
Lab-on-a-Chip
The evolution (and shrinkage)
of decades-old clinical
technologies
Human-Computer Interface
Technologies that interact with
humans for the purpose of
treating or augmenting function.
19

Scientific Research
Prospective Research
All research that seeks to
investigate novel phenomena
add to the existing ocean of
scientific information.
Biology and Physiology
Includes the studies of:
neuroscience, genetics,
molecular biology, and systems
physiology.
Publications
A scientific paper is the
medium to communicate and
pass down knowledge through
scientific discovery.
20

Multidimensional information about the self
Combining the Domains
PATIENT RECORDS
This data is collected from patients for
the purpose of building clinical histories
and treatment plans.
DRUG HISTORY
The prescriptions of patients are tracked
and stored with their medical files along
with dosage and disease history.
CLINICAL OUTCOME
Whether from surgical, rehabilitative, or
drug-based treatment, clinical outcomes
of patients are vital to health.
POINT OF CARE
Medicine practiced at the point where -
and when - treatment is required.
LAB-ON-A-CHIP
The evolution (and shrinkage) of
decades-old clinical technologies
HUMAN-COMPUTER INTERFACE
Technologies that interact with humans to
treat or augment function.
PROSPECTIVE RESEARCH
All research that seeks to investigate
novel phenomena add to the existing
ocean of scientific information.
BIOLOGY AND PHYSIOLOGY
Includes the studies of: neuroscience,
genetics, molecular biology, and systems
physiology.
PUBLICATIONS
A scientific paper is the medium to
communicate and pass down knowledge
through scientific discovery.
21

Heterogenous Big BioData is being
acquired in huge volumes too fast
for humans to understand.
22

Biological Biometric Biographical

3.2 MILLION
BASE PAIRS IN EVERY
HUMAN CELL

37 TRILLION
CELLS IN AVERAGE HUMAN
~10 EXABYTES
OF BASE PAIRS TO ANALYZE

Machine learning aims to identify  
and exploit hidden patterns in data  
to do useful tasks.
26

Implement
Supervised Learning
CLASSIFICATION Discrete
Supervised learning algorithms
that classify data perform a
discrete mapping between
features and outputs.
Example: Hand-writing recognition
REGRESSION Continuous
Algorithms with a continuous
output, rather than finite, are
regressions that map features  
to an output value.
Example: Predicting stock price
27
Training Data:
Output Values:
• Exploratory data analysis
• Select appropriate model
Given new , predict
Training
Selection

Unsupervised Learning
28
Clustering consumer purchasing history
Habit 2
Habit 1
Clustering
Find latent structures in data
without clear labels or
assumptions. Hidden features
can be explored post hoc.

Over 43,000 mobile health apps
available yet majority receive fewer
than 500 downloads globally.
30

Approximately 42% of all  
US hospitals currently utilize  
electronic medical records systems.
32

After 20 years nearly 80% of
originally sourced data is either  
gone or unusable.
34

Purpose Vision Methodology

The need to establish data integrity
should be at the core of every  
Big BioData application, service,
business model or policy.
36

metadata.pluscitizen.com

@PLUSCITIZEN @EMANUELBROWN

Big BioData: The Story of Us, a talk by Emanuel Brown @ Webvisions PDX 2014

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Big BioData: The Story of Us, a talk by Emanuel Brown @ Webvisions PDX 2014