Personalised and Participatory Medicine Workshop

1. Personalised
and
Par,cipatory
Medicine
Workshop,
15
May
2012
Fernando
Mar*n-­‐Sanchez
Ins$tute
for
a
Broadband-­‐Enabled
Society
&
Melbourne
Medical
School

2. Introduc$on
• Broadband
can
provide
many
opportuni$es
for
the
health
sector:
– Improving
youth
mental
health
and
aged
care
services
– Monitoring
health
condi$ons
– Enabling
shared
electronic
health
records
– Telehealth
• Convergence
with
other
technologies
towards
Digitally
Enabled
Personalized
and
Par$cipatory
Medicine

3. Aging
Well
– Mobile
and
broadband
technologies
for
ameliora,ng
social
isola,on
in
older
people
– Smart
Homes
for
the
Elderly
–
recent
developments
in
Korea
Youth
Mental
Health
− HORYZONS:
Online
Recovery
for
Youth
Onset
Psychosis

4. Telehealth
– Individual
Electronic
Health
Records
– The
Telestroke
Study
– Hap*c
Tele-­‐Rehabilita/on
– Teleden/stry
– Virtual
visits:
Inves*ga*ng
the
acceptability
of
webcam
consulta*ons
for
young
adults’
sexual
health
– Wireless
broadband
monitoring
of
knee
osteoarthri/s
– Overcoming
geographical
barriers
for
community
health
– Interpreter
mediated
cogni*ve
assessments
using
video
conferencing
soFware
– SeeCare
IPTV:
Personalised
Health
Literacy
Demonstrator
– Mobile
Augmented
Reality
– Interpreter
mediated
cogni/ve
assessments
using
video
conferencing
soFware
– High
resolu*on
monitoring
of
atmospheric
pollutants
to
iden*fy
their
impact
on
popula*on
health
– Overcoming
geographical
barriers
for
community
health
– Using
video-­‐conferencing
to
pilot
an
educa*on
and
clinical
support
package
for
rural
GPs
in
Mildura

5. Current
challenges
in
Medicine
• Need
of
earlier
diagnosis
• More
personalized
therapies
• Clinical
trials
and
the
development
of
new
drugs
need
to
be
faster
and
more
effec$ve
• Improve
disease
classifica$on
systems
• Risk
profiling,
disease
predic$on
and
preven$on
• Control
health
system
costs
• Ci$zens
should
take
more
responsibility
for
the
maintenance
of
their
own
health.

6. The Digitalization of Medicine
• Digital
revolu$on
in
other
domains
(banking,
insurance,
leisure,
government,…)
• The
incorpora$on
of
digital
systems
in
healthcare
is
lagging
behind
other
sectors:
– Reasons:
complexity,
privacy,
volume
of
data,
lack
of
demand
– It
has
greatly
affected
healthcare
at
the
hospital
or
research
centre
level.
– The
digital
revolu$on
has
not
yet
reached
medicine,
at
the
pa$ent/ci$zen
level
• BUT
THIS
IS
STARTING
TO
HAPPEN
NOW
!!!

7. Vision
• The
convergence
of
medicine
and
the
digital
revolu$on
will
produce
an
informa,on
ecosystem
that
will
facilitate
the
advent
of
safer
and
more
efficient
preven$ve,
diagnos$c
and
therapeu$c
solu$ons.
• The
ci$zen
will
have
access
to
her
gene,c
profile
and
clinical
record,
and
will
monitor
and
adjust
her
health
using
next
genera$on
sensors
and
social
networks
to
share
this
informa$on
with
peers,
clinicians
and
researchers.

8. High-­‐capacity
Broadband
technologies
and
networks
• The
availability
of
ultra-­‐high-­‐speed,
high-­‐capacity,
ubiquitous,
‘always-­‐on’
broadband
connec$vity
will
contribute
to
the
development
of
an
integrated
digital
infrastructure
for
medicine,
reaching
the
ci,zen,
that
will
make
feasible
the
concepts
of
personalized
medicine
and
par$cipatory
health.
• Ultra
high
speed
broadband
networks
will
be
required
to
transmit
enormous
volumes
of
data
from
pa$ents’
homes
to
health
prac$$oners
and
vice
versa
in
a
$mely
manner,
and
to
enable
the
processing
of
this
deluge
of
data.

9. Collecting genome data
• Benchtop
Ion
Proton™
Sequencer
–
designed
to
sequence
the
en$re
human
genome
in
a
day
for
$1,000

10. Collec$ng
phenome
data

11. Collec$ng
exposome
data
• Compilation of exposures experienced over an individual
lifetime (Christopher Wild, 2005)
• OUTSIDE / INSIDE – Absorbed -- Industrial chemicals,
combustion emissions, radiation, response to stress,
physical activity levels – heat/cold, noise, food,
microbiome
• Evaluating Personal Exposures
• Phones: Light meters, GPS, Accelerometer
• Senspod Monitor (Ozone, carbon monoxide, CO2, NO,
Noise and UV)
• Arizona State University – Petroleum derived
hydrocarbons (Benzene, Toluene)

12. Remote
pa$ent
data
monitoring
and
data
collec$on
Environmental sensors Genomic sensors
Phenomic sensors
Environmental risk factors Biomarkers (DNA sequence,
(pollution, radiation, toxic agents, …) proteins, gene expression, epigenetics
Physiological, biochemical parameters
(cholesterol, temperature, glucose, heart rate…)
Integrated personal health record

13. Na$onal
Broadband
Network

14. Patient Data (sensors and imaging)
Sensors
Genomic Phenomic Environmental
Integrated Personal
EHR Health Record
Module 1 Health Profile GWAS
Assessment
Tables (weighted factors)
Modelling Risks
Diagnosis Personal
Health Profile
CDSS
Health Profile
Module 2 Improvement Trialbanks
Networks
Risk reduction Decision matrix, protocols
Follow-up Personalised
Therapy Health Recommendations

15. Personalized
medicine

16. Defini$on
• Personalized
medicine
uses
an
individual's
gene*c
(and
molecular)
profile
and
individual
informa*on
about
environmental
exposures
to
guide
decisions
made
in
regard
to
(risk
profiling)
and
the
preven*on,
diagnosis,
and
treatment
of
disease.
(Adapted
from
F.
Collins,
Director
NIH)

17. Clinical
applica$ons
of
genomic
informa$on
• Pharmacogene$cs
–
Personalized
Medicine
Coali$on
-­‐
72
drugs
in
2011
• Cys$c
fibrosis
–
successful
clinical
trial
for
a
specific
muta$on
• Iden$fica$on
of
metabolic
diseases

18. Personal Genomics

19. Self-­‐genomics
-­‐
Clinical
annota$on
of
individual
genomes
• Prof.
Quake
-­‐
Stanford
-­‐
-­‐
Nature
gene$cs
paper
-­‐
$50.000,
1
week,
Helicos.
Stanford
team
-­‐
• Clinical annotation of genome from
“patient Zero”
– Drug
metabolism
– Rare
gene$c
variants
-­‐
rare
diseases
– Common
gene$c
variants
-­‐
Risk
of
complex
diseases
Ashley et al. The Lancet, Volume 375, Issue 9725, Pages 1525 - 1535, 1 May 2010

20. First
personal
longitudinal
OMICS
profiling
exercise
• Combined
analysis
of
genomic,
transcriptomic,
proteomic,
metabolomic
and
immunological
profiles
from
a
single
individual
(one
of
the
authors-­‐
Prof.
Michael
Snyder),
over
a
14
month
period.
More
than
3
billion
measurements.
• He
contracted
two
mild
viral
infec$ons
in
the
data-­‐gathering
period,
which
lem
their
molecular
signature
in
the
analyses.
• During
one
of
these
infec$ons,
his
blood
glucose
levels
began
to
approach
those
of
a
diabetes
sufferer.
Amer
changing
his
diet
and
exercise
habits,
glucose
level
returned
to
normal.
• This
study
shows
that
diseases
are
a
product
of
an
individual’s
gene$c
profile
as
well
as
interac$on
with
the
environment
and
that
disease
can
be
treated
based
on
molecular
informa$on.
(Chen
et
al,
Cell
148,
1293-­‐1307
March
16
2012
)

21. Par$cipatory
Health

22. Par$cipatory
Health
•
• From Web 1.0 – Use of internet to find health information to Web 2.0 –
web-based communities and services. NHS Social Care Model (NHS)
• A survey of 1,060 U.S. adults by the PwC Health Research Institute found
that a third of respondents are gravitating toward social media as a place
for discussions of health care.
• Pew Internet study – 27% of US internet users had tracked health data
online
• Care management, disease management, supported self-care, promoting
better health à Patients empowered, informed and involved in decision
making, prevention and learning

23. Par$cipatory
Health
self tracking devices
Social web
games
Participatory Health
mobile Internet of things
sensors PCEHR

24. PCEHR
• Quality = patients reviewing their own records - Shared
Medical Records
• MyHealth@Vanderbilt – information on prescriptions is
shared. Knowledge management team – consumers will have
convenient e-access to their medical records and genetic
profiles to social media & games
• Facebook
• Lifeline – support line for suicide
• Organ donor status
• Blood type – app will contact the user

25. DIY
EHR-­‐
The
Cathedral
and
the
Bazaar

26. Social
media
as
a
research
tool
• We
are
witnessing
a
transi$on
from
research
informa$on
systems
centralized
at
hospitals
and
clinical
research
centres
to
distributed
systems
that
reach
out
to
the
residence
of
any
ci$zen
/
pa$ent
who
opts
in.
• Clinical
Research
with
the
pa$ents,
not
on
the
pa$ents
• Examples
– 23andMe
–
Parkinson’s
Disease
–
PLoS
Gene$cs,
2
new
gene$c
associa$ons
– Pa$entsLikeMe
–
Nature
Biotech.
Self-­‐reported
data
from
600
pa$ents
on
the
use
of
lithium
for
Amyotrophic
Lateral
Sclerosis
(ALS)

27. Crowdsourced
clinical
trials
• DIY science, Crowdsourced Health Research Studies,
Citizen science, Amateur Scientist, Self-
Experimentation
• Patients Like Me – 125.000 members. 1000 condition-
based communities –25 Papers published in PNAS, Nat
Biotech, JMIR, …
• 23andme – 23 and we –
• Acor, RevolutionHealth, Curetogether, Genomera,
Althea Health

28. • Self
tracking
/
self
quan$fying
/
self
monitoring
• The
belief
that
gathering
and
analysing
data
can
help
them
improve
their
lives!
• QS’ers
doubling
every
year.–
5524
members,
42
meetup
groups
• Larry
Smarr–
10years
quan$fying
his
body
– Weight
–
physical
ac$vity:
calories
burnt
(body
media)
–
Food
intake
–
Sleep
(Zeo)
–
blood
chemicals
(60
Markers)
–
cholesterol/triglycerides
/
Apo
B
/
Ω
–
6,
Ω
–
3/
C-­‐reac$ve
protein
-­‐
Ultrasound
–
(plaque
in
arteries)
–
stool
test
–
colonoscopy
–
DNA
–
Microbiome
• Fitbit
–
Sleep
–
Movement
• +9000
health
apps,
each
person
connected
to
140
devices,
9
billion
of
connected
devices
now,
24
billion
by
2020
• NODE
Sensor
Environment

29. Pa$ent
empowerment
Current NBN-enabled Driving forces: patient empowerment,
networks personalized medicine, social networks
EHR – Personally Citizens are able to maintain and control
Electronic Controlled EHR their own health information
Health Record
Gene-disease Personal Citizens ask for genetic analysis of their
association genomics DNA through the Internet and receive
studies reports on various aspects of their health
Clinical trials Crowdsourced The patient voluntarily shares information
clinical trials on treatments and evolution of his/her
illness with other patients

30. Barriers
• New
regulatory
framework
(new
models
of
clinical
trials)
• New
informa$cs
methods
to
compile
and
interpret
all
the
informa$on
• Educa$on
of
pa$ents
and
health
professionals
• Ethics,
data
security
and
confiden$ality
issues
• Wide
availability
of
clinical
decision
support
systems
at
the
point-­‐of-­‐care
• New
cost-­‐effec$veness
assessment
and
financial
models
of
care
• Need
to
prove
clinical
effec$veness
before
DTC
services
are
offered.

32. Thank
you
sms@unimelb.edu.au
www.healthinforma$cs.unimelb.edu.au
Twiuer:
@ibeshbir