HEC 2016 Panel: Putting User-Generated Data in Action: Improving Interpretability for Consumer Informatics

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Putting User Generated Data in Action: Improving Interpretability for Clinical and Consumer Informatics
Pa#ent
generated
data
–
The
transi#on

from
“more”
to
“be6er”

HEC
2016
Workshop

WS
884
Pu(ng
User-‐Generated
Data
in
Ac8on:
Improving
Interpretability

for
Clinical
and
Consumer
Informa8cs

Aug
30
16:30
-‐
18:00

Panelists:
Thomas
WETTER,
Ying-‐Kuen
CHEUNG,
Sanjoy
DEY
,
XinXin
ZHU,

Bian
YANG

Moderator:
Pei-‐Yun
Sabrina
Hsueh


HEC/MIE
2016
Workshop:
PuJng
User-‐Generated
Data
in
Ac#on:

Improving
Interpretability
for
Clinical
and
Consumer
Informa#cs

Katie Zhu, PhD MD
(IBM TJ Watson Research, USA)
Sanjoy Dey, PhD
(IBM TJ Watson Research, USA)
Ken Cheung, PhD
(Columbia University, USA)
Bian Yang
(Norwegian University of Science of Technology,
Norway)
Thomas Wetter, PhD (Panel Discussant)
(University of Heidelberg, Germany
University of Washington, USA)
Pei-Yun Sabrina Hsueh, PhD (Moderator)
(IBM T.J. Watson Research, USA)

Agenda

• 
16:30-‐16:40

Opening
Remark

by
Dr.
Sabrina
Hsueh

•  EMERGING
HEALTHCARE
LANDSCAPE
SHIFT
WITH
PATIENT-‐GENERATED
DATA

• 
16:40-‐17:20

Presenta#ons

–  Dr.
Xinxin
Zhu:
So
we
got
sensor
data,
now
what?

–  Dr.
Sanjoy
Dey:
Enhancing
interpretability
of
computa#onal
model

–  Dr.
Ken
Cheung:
SMART-‐AR
to
evaluate
health
apps
for
outcome
op#miza#on

–  Dr.
Bian
Yang:
The
need
for
addressing
privacy
issues
with
be6er
interpretable
rules

• 
17:20-‐18:00

Discussant
summary
presenta#on
&
Panel

discussion/audience
Q&A

–  Dr.
Thomas
We6er:
Pa#ent
generated
data
–
The
transi#on
from
“more”
to
“be6er”

–  Panel
discussion
(moderated
by
Dr.
Sabrina
Hsueh)

Summary
on
Workshop
Theme
(1)

•  (1)
Iden#fy
immediate
ac#on
items
to
start
ini#a#ng

proposal
for
enabling
evidence-‐based
conversa#on

with
pa#ents/physicians/providers
in
the
loop

Summary
on
Workshop
Theme
(1)

•  2.
Implica#ons
and
lessons
learned
from
the
case

studies
-‐-‐
especially
the
gaps
you
perceived
as

barriers
of
entry

Summary
on
Workshop
Theme
(2)

•  3.
Requirements
for
successful
redesign
of

healthcare
systems
to
accommodate
pa#ent-‐
generated
informa#on
(with
a
sub-‐goal
of
iden#fying

the
areas
where
such
informa#on
can
make
most

impacts).

Ques#ons

•  1.
What
is
the
state-‐of-‐the-‐art?

•  2.
What
are
the
benefits
of
improving
interpretability
in
PGHD

in
ac#on?

•  3.
What
the
key
dimension
of
interpretability
of
PGHD?

What

are
the
barriers?
Technical/social?

•  4.
What
is
our
defini#on
of
interpretability?
What
are
the

likely
measures?

•  5.
What
is
the
opportunity
area
going
forward?

•  6.
What
are
the
likely
ac#on
items
to
be
suggested
to
the

community
to
further
the
discussion
about
improving

interpretability
for
PGHD?

–  In
the
field
of
consumer
health
informa#cs
or
beyond?

INTRODUCTION

EMERGING
HEALTHCARE
LANDSCAPE
SHIFT
WITH

PATIENT-‐GENERATED
DATA

Pei-Yun (Sabrina) Hsueh, PhD
Wellness
Analy8cs
Lead

Global
Technology
Outlook
Healthcare
Topic
co-‐Lead

Healthcare
Informa8cs
PIC
co-‐Chair

Computa8onal
Behavioral
and
Decision
Science
Group

Health
Informa8cs
Research
Dept.

IBM
T.
J.
Watson
Research
Center

• 
Research
focus:
Pa8ent-‐genera8on
info
from
wearables
and
biosensor

devices/implants,
Personaliza8on
analy8cs,
Pa8ent
engagement
&
Adherence

risk
mi8ga8on,
Interpretable
machine
learning

Opening Remark

Source:
Based
on
McGinnis
et
al,
The
Case
for
More
Active
Policy

Attention
to
Health
Promotion,
Health
Aﬀairs,
2002.

Health
Determinants
Mismatches
Today’s
Spending
“We
need
to
invest
in
addressing
all

determinants
of
health…”

BIG DATA
Clinical + behavior
driven
Wellness Management

It’s Big Data! It is also not just Big Data!
SOURCE: Barbara J. Sowada, A Call to Be Whole:
The Fundamentals of Health Care Reform, July 30, 2003, Praeger.
IBM Watson // ©2015 IBM Corporation
NOISY, LARGE VOLUME,
UNCONTROLLED
Need minimum description
& quality/validity study

Solutions
Population Health
Management
Condition
Specific Care
Health
and Wellness
Social
Programs
Discovery
Solutions
Real World
Evidence
Ecosystem
Population Health
Management
Condition
Specific Care
Health
and Wellness
Social
Programs
Discovery
Solutions
Real World
Evidence
Individual
Social
Programs
Education
Governments
Home Health 
Agencies
Practitioners
Hospitals
Therapists
Health  
Plans
Family
Public Health
Medical Devices
and Diagnostics
Bio-Pharma
Employers
Payers
Data
Insight
To tap into the potential of DTR in open
deployment, accessing a vast amount of
untapped data could have a great impact

1
4
PGHD: Beyond Capturing Social/Behavioral
Determinants from EHR
Institute of Medicine
report (2016)

1
5
•  R.W. White, R. Harpaz, N.H. Shah, W. DuMouchel, and E. Horvitz.
Toward Enhanced Pharmacovigilance using Patient-Generated Data on the Internet, Nature CPT, April 2014.
Success Story:
PGHD for Pharmacovigilance

Success Story:
PGHD for Personalized Communication
Palmquist, A.E.L., Koehly, L.M., Peterson, S.K. et al. J Genet Counsel
(2010) 19: 473. doi:10.1007/s10897-010-9299-8

Success Story:
PGHD for Diagnosis
Identify the onset and progression of disease states
e.g., depression, Parkinson’s, PTSD
Assist with decision
making in ER
(e.g., FITBIT CHARGE HR)
Source:
1. http://www.androidauthority.com/fibit-charge-hr-save-patient-685205/
2. M. Sung, C. Marci, and A.S. Pentland, Objective Physiological and Behavioral
Measures for Identifying and Tracking Depression State in Clinically Depressed
Patients, MIT Technical Report, 595 (2005): 1-20.
3. S. Arora, V. Venkataraman, S. Donohue, K.M. Biglan, E.R. Dorsey, M.A. Little,
High accuracy discrimination of Parkinson’s disease participants from healthy
controls using smartphones, IEEE International Conference on Acoustics, Speech

Success Story: PGHD for Care Coordination
IBM Taiwan Collaboratory

19
Promoting patient activation for behavioral change
(Dietary intake: Burke et al., 05;
Physical activity: Prestwich et al., 09; Michie et al., 09)
Preventing lifestyle-related chronic diseases,
e.g., Type II Diabetes
Helmrich et al, 1991;Bailey, 2001; Scottish Intercollegiate
Guidelines Network, 2001; Finland National Type II Diabetes
Prevention Programme, 2007; American Diabetes
Prevention Program, 2008).
Increase awareness to self-monitoring
(Prestwich et al., 09; Burke et al., 05)
Triggering reminders to care plans
(Consolvo et al. 09; Hurling et al., 07)
Personalizing communication messages and
education materials
(Thaler and Sustein, ‘08)
Making
Sense
of
PGHD
for
Individuals

Nudge: Improving Decisions About Health
PERSONAL INFORMATICS TOOLS
(auto PGHD capturing + manual input)

2
0
The Failure of Scripps Trial
Patients who monitored their health were less likely to attribute health
outcomes to chance than those who didn’t monitor their health

Where do we meet in the middle?
???
Unsustainable, ill-supported
health consumers
Healthcare Triple aim

22
Reference Story:
Kaiser Permanente – Improved Outcome and Reduced Co
Individualized Guideline Improved Clinical Outcomes
§  Reduce 5-year CVD risk 2.4 times more than EHR+panel support tool alone (≈ 13% absolute risk reduction)
§  ≈ 6,000 myocardial infarctions (MIs) and strokes prevented annually if applied throughout KP (≈43%
increase over JNC7 guideline for the same cost)
Individualized Guideline Reduced Operational Costs
§  ≈ $7,000 cost savings per MI and stroke
§  ≈ $420M annual net savings if applied throughout KP
Source:
Eddy, et al. (2011). Individualized Guidelines: The Potential for Increasing Quality and Reducing Costs. Annals of Internal Medicine, vol. 154, no. 9, p.627-634.
http://www.annals.org/content/154/9/627.abstract
22

23
Kaiser Permanente – Improved Patient Motivation
and Adherence, Increased Clinician Confidence
(Respondents were)“…more likely to report that they have been
asked to change their medication, diet and exercise habits. ”
—Patient Survey
“…helped the doctor to motivate them and helped them participate
in their treatment choices, i.e., making lifestyle changes and
understanding the rationale for their suggested interventions.”
— Patient Focus Group
“All doctors agreed that it helps them to make the best clinical
decisions for their patients.”
— Clinician Survey
23

Adding High Touch by Lay Care Guides
•  Parallel-group randomized trial (2010-2012).
–  6 primary care clinics in Minnesota.
–  Adults with hypertension, diabetes, or heart failure.
–  Assigned in a 2:1 ratio to a care guide or usual care.

SO
WE
GOT
SENSOR
DATA,
NOW

WHAT?

•  MD
(Anesthesiologist)
from
China
Medical

University

•  PhD
in
Biomedical
Informa#cs
from

Columbia
University

•  Past
Experience

–  Chief
Medical
Informa#on
Officer
at
Kforce

Government
Solu#ons,
U.S.A.

–  Associate
Medical
Director,
Pfizer
Health

Solu#ons,
U.S.A.

–  Senior
Manager,
Pfizer
Health
Solu#ons,

U.S.A.

–  Clinical
Program
Manager,
Philips
North

America
Research
Center,
U.S.A.

–  Healthcare
Informa#cs
Subject
Ma6er

Expert,
Veterans
Affairs
Medical
Center,

U.S.A.

Xinxin (Katie) Zhu
•  Telehealth lead at IBM
Watson
•  External Advisory Board
member to Columbia Univ.
Center of Advanced
Technology

So
we
got
sensor
data,
now
what?

•  What
sensor
data
could
help
with
care?

•  How
to
determine
when
to
use
what?

•  Are
the
sensor
data
reliable?

•  What
is
the
context
when
data
were
collected?

•  How
to
interpret
data
in
context?

•  Clinicians’
concerns

What
sensor
data
could
help
with
care?

Use
case:
stress
management

Subjec#ve

Stressors

Psychological

Response

Physiological

Response

Stress
Hormones

Many
sensors
are
out
there…

Tinke

31

Approach
•  Plug into a smartphone
•  Scan finger
•  Provide stress/relax index
Data Tracked
•  Heart rate variability
•  Respiration rate
•  Blood oxygen level
Tinke Website

Spire

32

Data Tracked
•  Breathing pattern
•  Steps
Approach
•  Consistent breaths à Calmness
•  Uneven breaths à Tension
•  Fast and consistent breaths à Focus
•  Guided meditation

Pip

33

Data Tracked
•  Skin conductance (EDA)
Approach
•  Hold device between the
thumb and index fingers
•  Stress level via audio/
visual feedback

Many
sensors
are
out
there

Brain
Wave
(EEG sensor)
Skin
Conductance
(EDA sensor)
Blood
Volume
Pulse
(PPG sensor)
Skin Temperature
(Infrared Thermophile)
Heart
Rate
(PPG sensor)
Heart Rate
Variability
(ECG sensor)
Respiration
Rate/Volume
(RIP sensor)
RR Interval
Distribution
(ECG sensor)
Image Source: Neurosky, Empatica, Hexoskin

Hexoskin
V.S.
BioSens
Holter
ECG
Valida#on

Brain
Wave

36

Relaxed Reading a paper with a time limit
Delta
- Adult slow wave sleep
Theta
- Drowsiness, idling, inhibition
Alpha
- Relaxed, reflecting
Beta
- Alert, busy, anxious, thinking
Gamma
- Short term memory usage
Mu
- Rest state motor neuron activity
-  Produced by electrical pulses
from neuron communication
-  Frequency bands associated
with different behaviors and
emotions

How
can
people
make
sense
of
these?

37

Clinicians’
concerns

Information overload Unreliable data à false alarms
Clinical workflow
Context, context, context!

Summary
on
Workshop
Theme
(2)

•  2.
Implica#ons
and
lessons
learned
from
the
case

studies
-‐-‐
especially
the
gaps
you
perceived
as

barriers
of
entry

Summary
on
Workshop
Theme
(3)

•  3.
Requirements
for
successful
redesign
of

healthcare
systems
to
accommodate
pa#ent-‐
generated
informa#on
(with
a
sub-‐goal
of
iden#fying

the
areas
where
such
informa#on
can
make
most

impacts).

Ques#ons
(preliminary)

•  1.
What
is
the
state-‐of-‐the-‐art?

•  2.
What
are
the
benefits
of
improving
interpretability
in
PGHD

in
ac#on?

•  3.
What
the
key
dimension
of
interpretability
of
PGHD?

What

are
the
barriers?
Technical/social?

•  4.
What
is
our
defini#on
of
interpretability?
What
are
the

likely
measures?

•  5.
What
is
the
opportunity
area
going
forward?

•  6.
What
are
the
likely
ac#on
items
to
be
suggested
to
the

community
to
further
the
discussion
about
improving

interpretability
for
PGHD?

–  In
the
field
of
consumer
health
informa#cs
or
beyond?

ENHANCE
INTERPRETABILITY
WITH

PRIOR
KNOWLEDGE

Sanjoy
Dey
PhD.

Postdoctoral Research Scientist, Center of Computational Health, IBM T. J.
Watson Research Center, Yorktown Heights, NY 10598
Sanjoy
Dey’s
research
interests
lie
in
the
areas
of
health
care
informa#cs,
data
mining

and
machine
learning,
especially
in
building
interpretable
models
by
integra#ng

mul#ple
healthcare
datasets.
.
In
par#cular,
Sanjoy
is
interested
in
building
models

which
aim
to
incorporate
domain
knowledge
at
mul#ple
stages
of
model
development

(e.g.,
feature
selec#on,
cohort
selec#on
and
study
design)
so
that
these
models
can

infer
knowledge
that
are
complementary
to
the
already
known
clinical
prac#ces
and

guidelines.
Prior
to
this
posi#on,
he
earned
his
Ph.
D.
from
the
department
of
computer

science
at
university
of
Minnesota.

Improving Interpretability of Patients
Generated Data
45
DiseaseHealthy
Dataset 1 Dataset 2
Class
label

Relation across the datasets

Analysing
the
obtained
results

from
Complex
Models

•  Interpret
the
model
parameters
so
that
they

can
be
used
to
infer
meaningful
knowledge

•  Visualize
the
obtained
informa#on
from
a

model
in
a
meaningful
way

Taking
prior
knowledge
into

account

•  Many
#mes,
medical
knowledge
are

available
containing
useful
rela#onships

among
clinical
events

Dataset
Interpre#ng
Complex
Computa#onal
Models

•  Complex
model
parameters
can
be

converted
to
metrics
that
are
easily

understandable
by
domain
experts

–  Logis#c
Regression

–  LASSO
with
regulariza#on
to
perform

simultaneous
variable
selec#on

• 
Logis#c
loss
func#on
can
be
used
as

Log
Odds,
which
can
be
converted
to

Odds
Ra#o

-‐

where
β0
the
log
odds
for
smoking
for

men

•  Probabili#es
of
an
event
can
be

viewed
as
clinical
uncertainty

Class Level
Liu et al. 2011, Jeiping Ye et al., 2012

Visualiza#on
of
the
obtained
model

Decision
Boundaries
of
Logis#c

Regression

Rule
based
representa#on
of

Decision
Tree
and
Cart
based
Models

Graphical
Models
for
Disease
Models

Work
Environment

Gene

Disease

Symptom
s

Westra
et
al.
2011,
Manzi
et
al.

2013

Integra#ng
Prior
Knowledge
with
Mul#-‐source

EHR
data
for
Enhancing
Interpretability

Diagnosis

Codes

(ICD-‐9)

Admission

Assessment

Survey

Discharge

Assessment

Survey

Home Healthcare
Dey et al. AMIA 13, Dey et. al., SDM 14, Westra et al. 11
Demographic, behavioral, pathological,
psycho-social factors, outcome variables.
Problem Formulation:
48
260,000 patients
Data source: CMS OASIS dataset
Outcome prediction

Enhancing Interpretability of Patterns
49
Predictive Power
Interpretability
•  Interpretability
(Relevance)

and
predic#on
power
are

diﬀerent
goals

•  Prior
rela#onships
present

in
the
data
can
be

incorporated
into
model

ICD-‐9
Group
1
ICD-‐9
Group
2

250.6:
Neurological

manifesta#on

401.1:
Benign

hypertension

290:
Demen#a
838:
Disloca#on
of
foot

331:
Alzheimer’s
disease

692.71:
Sunburn

331.9:
Cerebral

degenera#ons

V58.42:
Hip
joint

replacement

Neural disorders No common underlying disease
Interpretable
Predic8ve

•  Which
group
of
pa#ents
are
likely
to
improve

ambula#on?

•  Are
those
factors
clinically
interpretable
and
make
a

homogeneous
group?

G1

G2

Ideal

Proposed Approach
Key
Steps:

•  Integrate
both
survey
data
from
EHR
and

ICD-‐9
diagnoses
codes
to
predict
the

improvement
of
urinary
incon#nence

•  Use
clinical
prior
knowledge
such
as
Clinical

Classifica#on
Sotware
(CCS)
into
account
to

increase
the
interpretability

•  Develop
a
sta#s#cal
technique
called
Sparse

Hierarchical
Canonical
Correla#on
Analysis

(SHCCA)
to
address
these
challenges

50
X Y
Algorithm:

•  Take
the
hierarchy
of
the
CCS
tree
into
account
to

define
a
similarity
matrix
called
H
among
the
ICD-‐9

codes

•  Trade-‐off
between
the
data-‐driven
and
prior

knowledge
driven
similarity
of
ICD-‐9
codes
using

λh

•  Converted
into
convex
formula#ons

•  Solve
the
final
equa#on
based
on
gradient
descent

formula#on

Prior

Knowledge

λh trades off between domain-
driven and data-driven
knowledge
Dey et. al., SDM 14

Sparse Hierarchical CCA (SHCCA)
Parameter
Selec8on:

•  Op#mize
the
parameters
using
cross-‐valida#on
such
that
it

op#mizes
the
correla#on
on
valida#on
data

Evalua8on:

Predic8on
power:
how
well
the
selected
group
of
ICD-‐9
codes

can
predict
the
improvement
of
outcome

Interpretability:

–  I-‐score
based
on
the
co-‐occurrences
of
the
ICD-‐9
terms

belonging
to
a
group
C
in
PubMed
ar#cles

–  Domain
knowledge
by
physicians
and
nurses

51
ti is the set of articles found with ICD-9 code i
I-‐score(C)=∑𝑖ϵ 𝐶↑▒∑𝑗ϵ 𝐶↑▒| 𝑡↓𝑖  ⋂ 𝑡↓𝑗 |/| 𝑡↓𝑖  ∪ 𝑡↓𝑗 |

Results
•  SHCCA
has
similar
performances
as

the
baseline
methods,
but
with
fewer

components

•  It
enhances
the
interpretability

signiﬁcantly

Predictive power of SHCCA
52

Components from SHCCA
Survey
data
1
ICD-‐9
codes
1
Survey
data
2
ICD-‐9
codes
2

Age,

Prior
Memory
Loss,

Poor
Speech,

Poor
Cogni8ve

Func8on,

High
Confusion,

Memory
Deﬁciency,

Frequent
Behavioral

Problem

Demen8as,

Persistent

mental
disorders,

Alzheimer's

disease,

Cerebral

degenera8ons

Surgical

Wound,

Fully

granulated

Surgical
Wound

Acercare
for
healing
fracture
of

hip,

Knee
joint
replacement,

Hip
joint
replacement,

Acercare
following
surgery
of

the
musculoskeletal
system,

Acercare
following
joint

replacement,

Acercare
following
surgery
for

neoplasm,

Acercare
following
surgery
of

the
circulatory
system

53
Component relevant to
Mental health
Component relevant to surgical treatment

Summary
&
Limita#ons

•  Summary

–  Predic#on
power
and
interpretability
are
two
different
goals,
which
are
oten
hard
to

achieve
by
computa#on
models
simultaneously

–  Predic#ve
models
can
be
post-‐processed
and
visualized
to
make
them
more

interpretable

–  Leveraging
clinical
prior
knowledge
such
as
Clinical
Classifica#on
Sotware
(CCS)
into

account
can
increase
the
interpretability
substan#ally

•  Limita#ons

–  The
defini#on
of
interpretability
is
oten
subjec#ve
and
oten
requires
domain

exper#se

–  Prior
knowledge
about
a
par#cular
problem
is
not
oten
readily
available
in
many

clinical
applica#ons

–  Use
of
prior
knowledge
into
the
model
op#miza#on
is
oten
not
straighvorward

54

SMART-‐AR
to
evaluate
health
apps
for

outcome
op#miza#on

Ken
Cheung

Columbia
University

Ying
Kuen
(Ken)
Cheung

•  PhD
in
Sta#s#cs
(U
Wisconsin,
Madison
WI,
USA)

•  Professor
of
Biosta#s#cs,
Columbia
University,
New
York
NY,

USA

•  General
interest:
Transla#onal
research
in
all
phases

•  Speciﬁc
areas

•  Dose
and
treatment
selec#on
in
adap#ve
clinical
trials

•  Op#mal
behavioral
interven#on
for
secondary
stroke
preven#on

•  Analysis
of
high-‐dimensional
physical
ac#vity
data

•  N-‐of-‐1
trial
designs

•  Evalua#on
and
dissemina#on
of
mobile
technologies
for
mental

health

Objec#ve
&
Reinforcement
Learning

•  Data
sequence:
(X,
A1,
U1,
A2,
U2,
…,
AK,
Y)

–  X
=
Individual
characteris#cs

–  At
=
Apps
downloaded
(Ac#on)
at
#me
t

–  Ut
=
response
and
use
pa6ern
between
At
and
At+1

–  Y
=
Final
outcome
(depression
reduc#on)

•  Objec#ve:
Iden#fy
the
sequence
At
based
on
X
and

Ut
so
as
to
maximize
Y
(on
average)

•  Reinforcement
learning:
Q-‐learning,
OWL,
etc.

SMART
Design

•  SMART
(Sequen#al
Mul#ple
Assignment
Randomized
Trial)

App
1

App
2

Ac#ve
use

Ac#ve
use

Non-‐use

Non-‐use

App
1
+
App
3

App
2

App
2
+
App
3

App
2
+
reminder

App
1

App
1
+
reminder

App
2

App
3

Depression
reduc8on
at
6
months,
Y

Enrichment
based
on
intermediate

use
paeern,
U

P
=
2/3

P
=
1/3

P
=
0.3

P
=
0.7

P
=
0.6

P
=
0.4

P
=
0.6

P
=
0.4

P
=
0.3

P
=
0.7

SMART-‐AR
Design
(Cheung
et
al,
2015
Biometrics)

•  SMART
Design

ü  Allows
learning

✗  No
feedback
to
system

✗  Curse
of
dimensionality:
many
apps

in
prac#ce

•  SMART-‐AR

•  AR
=
Adap#ve
randomiza#on

•  Assign
more
users
to
more

promising
branches

•  Curse
of
dimensionality:
Sot

elimina#on
of
poor
performing
apps

à
Improve
signal-‐noise
ra8o,
hence

interpretability
of
the
recommender

0 20 40 60 80 100
9101112131415
Enrollment number
BDIreduction
o o o o o o o o o o o o
+ + + + + + + + + + + +
p p p p p p p p p p p p
m m m m m m m m m m m m
Scenario 1
CODIACS
Balanced
Application in conventional depression program
Cheung et al, 2015 Biometrics

Some
Simula#on
Illustra#on

Non-‐adap8ve
SMART
SMART-‐AR

Balanced

randomiza#o
n

CODIACS

randomiza#o
n

Scenario
1

Probability
of
iden#fying
the

op#mal
sequence

0.91
0.94
0.95

Expected
adjusted
value*
0.98
0.99
0.99

Variance
of
adjusted
value
3.1
2.3
1.3

Scenario
3

Probability
of
iden#fying
the

op#mal
sequence

0.53
0.51
0.51

Expected
adjusted
value*
0.95
0.95
0.96

Variance
of
adjusted
value
8.5
11.0
7.4

Health
informa#cs
support
issues

•  SMART-‐AR
requires
real

#me
transmission
between

data
site,
apps
cura#on
site,

compu#ng
site

–  Large
volume:
Use
data

pre-‐processing

–  Privacy
&
security

•  Health
outcomes

–  Valida#on
of
outcomes

0 10 20 30 40
Days since download first app
AppID
1234567891011121314

ADDRESSING
PRIVACY/SECURITY

CONCERN
WITH
INTERPRETABILITY

Bian’
intro
goes
here….

- February 4, 2015
-  Hacker broke into the medical
insurance database
-  80 million records stolen in
plaintext
-  Insurance company’s database
are not required to be encrypted
by HIPAA
-  administrator's credentials were
compromised

Now
…
the
hot
term
for
2016
–
«ransomware»

-  More than half of hospitals (in US)
hit with ransomware in last 12
months
(HealthcareITNews, April 07, 2016)
-  Good business model for the hackers
-  Low risk
-  Good cost-benefit efficiency
-  Easy to build "reputation" for
the service –
(https://www.theguardian.com/technology/
2016/feb/17/los-angeles-hospital-hacked-
ransom-bitcoin-hollywood-presbyterian-
medical-center)

More
A6ack
vectors
to
Pa#ent
Data
Security

More attacking vectors opened due to …
the shifts of healthcare patterns - now and future
•  hospital -> home / cyber space (telemedicine, IoT, mobile
technologies, care research)
•  in-hospital treatment -> prevention (big data, health analytics,
health electronics, e-drugs)
•  doctor-centered -> patient-centered (telemedicine, big data,
machine intelligence, cloud storage and computing)
•  health care organizations -> associated business partners in
liability (law and regulations, e.g., HIPAA -> HITECH (2009))
•  Local service -> global service (service across the borders)
•  the “SafeHarbor” agreement
•  Facebook fined by the Belgian court

What
does
security
mean
for
eHeath/mHealth’s
future

-  new breaches and "business models for hacking" would
continue to come… (but take it easy)
-  More liability to IT tech enablers and business associates
(e.g, HIPAA ->HITECH)
-  cloud / SDN makes "security as a service" that can be
outsourced
-  IT Tycoons (Microsoft, IBM, Google, etc.) could finally
take it over (capable to take risk, more resources, global
threat intelligence, etc.)

Future
Solu#ons

-  Data ownership re-definition
-  Generating incentives for industry to migrate from
data silos to data sharing
-  Patients’ awareness of their interests in their own
data
-  Patients’ convenience in accessing their own data
-  Legal support
-  Technology: security / privacy by design

DISCUSSANT
SUMMARY

PRESENTATION

Prof.
Dr.
Thomas
Weeer

•  MSc
/
PhD
in
mathema8cs
from
Aachen
Technical
U,

Germany

•  PostDoc
with
IBM
Scien#ﬁc
Center
Heidelberg,
Germany

•  Since
1997
Prof.
of
Medical
Informa8cs,
Heidelberg
U

–  Interna8onal
assignments
to
Boca
Raton
(FL),
Aus#n
(TX),
Salt
Lake

City
(UT),
Sea6le
(WA)

–  Aﬃl.
Faculty
with
Dept.
BIME,
U
of
Washington,
Sea6le

–  Author
of
textbook

Consumer
Health
Informa#cs:
New
Services,
Roles
and

Responsibili#es;
Heidelberg
(Springer)
2015
(eBook)
resp.
2016

(Hardcover)

Pa#ent
generated
data
–
The
transi#on

from
“more”
to
“be6er”

Thomas
We6er

May be obsolete here with the title slide already
using this paraphrase of the workshop title

Growth
is
everywhere

•  More
modali#es
to
collect
and
store
data
are
oﬀered

•  More
communica#on
media
carry
health
info

•  More
condi#ons
suggest
to
be
monitored

•  More
ins#tu#ons
consider
usage

•  More
consumers
buy
in

•  Does
this
make
sense?

•  How
can
we
move
towards
meaningful
ac#on?

•  How
can
we
protect
against
unethical
exploita#on?

More
data
is
more
op#ons

.

More
data
is
more
op#ons

Period

But
beware

Date
are
not
the
world;
data
map
the
world
–
truthfully?

What
is
the
ci#zen‘s
contribu#on
to
the
mapping?

•  Carrier
of
implanted
sensors

•  Operator
of
a6ached
and
mobile
sensors

•  Witness
of
health
signs

•  Interpreter
of
health
signs

•  Self
therapist

•  Health
plan
contractor

More
data
is
more
tempta#ons

Ci#zens

•  Trust
more
than
warranted

•  Shit
focus
from
senses
to
data

Clinicians

•  Shit
focus
from
senses
to
data

Researchers,
public
health

•  Urge
to
ﬁnd
something

Big
business

•  More
big
business

More
data
is
more
tempta#ons:
Ci#zens

Percep#on
of
the
presumably
unfailable
objec#ve

givens
as
proxy
for
truth

•  Mental
ﬁxa#on
on
data

•  Unwarranted
trust
as
decision
aid

•  Adverse
reac#on
to
contradictary
data

•  Overreac#on
upon
alarming
data

More
data
is
more
tempta#ons:

Researchers,
Public
health

Percep#on
that
regarding
the
massive
volume
of
data

there
cannot
be
no
effects

•  Do
the
big
data
mechanics

•  Spot
peculiari#es

•  Publish
results

Knowing
that
5%
of
significant
studies
are
not

substan#ated
through
an
effect

Curb: Complexity reduction –
Sanjoy Rey, Ken CheungRisk: Blindfolded actionism
Curb: Plausibility, context – Katie Zhu
Risk: Funding agency expectations Curb: Research ethics

More
data
is
more
expecta#ons

If
scien#st
dispose
of
more
data
their
methods
are

challenged:

•  Profound
interpreta#on
and
predic#on
–
Sanjoy
Dey

•  Parsimony,
wise
selec#on
–
Sanjoy
Dey

•  Secure

storage/communica#on
–
Bian
Yang

•  Insight
–
Ken
Cheung,
Sabrina
Hsueh

More
is
more
expecta#ons

If
ci#zens
volunteer
their
data,
they
expect
services:

•  Serious
PGHD
into
PHR
into
EHR
integra#on

•  No
data
leakage

•  Explana#ons
of
the
unexplainable

•  Emergency
rescue
in
response
to
alarming
data

More
data
can
be
the
hays#ck

•  where
we
don‘t
ﬁnd
the
needle

•  while
being
distracted
by
–
hay

•  but
someone
needs
the
needle
–
now

Claude
Shannon
1948
1)

„Informa#on
is
that
which
reduces
uncertainty“

Which
the
needle
in
the
hays#ck
does
not
do

1)  A mathematical theory of communication
Bell Systems Technical Journal

Bring
forth
the
signal
from
the
noise

•  Concentrate
trials
on
treatments
with
emerging

posi#v
prognosis
(Ken
Cheung)

•  Select
data
with
high
interpreta#ve
or
predic#ve

power
(Sanjoy
Dey)

•  Regard
context
to
detect
noise
(Ka#e
Zhu)

Are
we
achieving
quality
that
sa#sﬁes
doctors?

•  Not
a
ma6er
of
taste

•  Doctors‘
code
of
conduct
regulates
that

when
trea#ng
diagnosed
pa#ents
he

–  assumes
responsibility
for
correct
recordings
of
devices
he

hands
to
the
pa#ents

–  has
to
waive
liability
for
data
generated
through
other

pa#ent
solicited
devices

while,
when
coaching
for
healthy
lifestyle

–  anything
goes

Are
we
achieving
quality
that
sa#sﬁes
doctors?

•  Under
a
treatment
contract
a
doctor
is
held

responsible
for
medical
errors.

•  Morally,
he
cannot
be
held
responsible
for
decisions

based
on
false/faked
data
from
outside
his
control

•  Pa#ents
want
their
data
used

•  They
cannot
guarantee
correct
data

•  A
classical
gridlock
1)

1) In NY/NY en.wikipedia.org/wiki/Gridlock


Who
can
do
what

to
solve
the
gridlock?

Are
we
achieving
full
transparency?
Do
we

want
it?

Imagine
that
a
certain
set
of
sensor
data
is
so

characteris#c
of
you
that
you
need
not
register,
just

deliver
a
sample
and
they
know
who
you
are.

Are
we
suppor#ng
personalized
medicine?

•  If
the
wealth
of
our
data
is
so
large
that
we
can

iden#fy
data-‐twins

–  A
treatment
for
the
second
twin
should
work
if
it
did
for

the
ﬁrst

–  The
end
of
clinical
trials

Thank You
Merci
Grazie
Gracias
Obrigado
Danke
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