Presentation given at Ballarat University by Prof Fernando Martin-Sanchez on 13 June 2012

1. Health and Biomedical informatics:
information processing for
preventative Medicine
Colloquia Series at University of Ballarat
June 13, 2012
Fernando J. Martin-Sanchez
Professor and Chair of Health Informatics
Melbourne Medical School
Faculty of Medicine, Dentistry & Health Sciences
&
Adjunct Professor, School of Engineering
Director, IBES Health and Biomedical Informatics Research Lab.

2. Outline
• Current challenges in Medicine
• The Vision: HebyEq
• Opportunities from Health Informatics and
Technology
• Personalised Medicine
• Participatory Health
• Self-omics
• The central role of Informatics
• HBIR @ UoM

3. Current
challenges in
medicine

4. Main problems
• Demographic change – Aging
population.
• Increasing number of patients with
chronic diseases
• Unhealthy life habits (sedentary,
fast food, alcohol, tobacco)
• Rapidly increasing cost of medical
technology
• Climate change – rise of infectious
disease
• Workforce shortage
àUnsustainability of the US National Center for Disease Control
Healthcare system?

5. Current challenges in Medicine
• Need of earlier diagnosis
• More personalized therapies Personalised
• Clinical trials and the development of new medicine
drugs need to be faster and more effective
• Improve disease classification systems Preventative
• Risk profiling, disease prediction and medicine
prevention
• Control health system costs
Participatory
• Citizens should take more responsibility for medicine
the maintenance of their own health.
àEmphasis on prevention, not cure

6. The Vision:
Health
by
equation
verse in which Prospero calls Caliban:
A devil, a born devil, on whose nature
Nurture can never stick; on whom my pains,
Humanely taken, all, all lost, quite lost;
William Shakespeare, The Tempest, 1610

7. G*E=P
• Disease phenotypes arise from complex
interactions among individual genetic
information and environment (way of life,
risk factors, external agents)

8. Activation factors
Way of life
Environmental Environmental
exposure risk
Mutagen agents Disease risk Disease
Mutations and Genetic
Inheritance polymorphisms risk

9. Visions of the future for the field of BMI
• Integrated environments for assessing and
modeling the relative contribution of factors
(genetic, environmental, phenotypic) that
confer an individual a relative risk of
developing a disease
• Models coupled to information systems to
contextualize the patient molecular
information and clinical decision making
support systems at the Point of Care for
personalized care

10. • Prevention is better than a cure; how to
prevent is the question.
• What if it was possible to calculate
accurately a person s medical strengths
and weaknesses as a combination of
genetic and environmental factors?
• This will illustrate how informatics and
technology will play a major role in a
new wave of preventative medicine, by
estimating risk factors as a personalised
profile and supporting personalized
clinical decision making.

11. Health by Equation - Rationale
• Research and Technology development in genetic
analysis, informatics, clinical devices
• New data and knowledge:
ü Availability of genomic personal information –
Characterising individual genetic variation – Human
Genome
ü Characterising human phenotypes, including disease –
Human Phenome
ü Knowledge about action mechanisms of environmental
factors (toxic agents, drugs, food, …) – Envirome or
Exposome

12. Background
• Well-being is not only the absence of disease. It is also related with
the risk of future problems.
• Future emphasis on understanding health protecting factors
(Healthome) instead of only causes of disease (Diseasome).

13. The Equation
Health
Profile
• The different genetic and environmental factors, will be weighted in terms of their contribution to
health maintenance or loss.
• The ratio between positive and negative factors yields a Health Profile that could be informative
of the current health status of an individual and even predictive of future health problems.

14. Life-long (longitudinal) records
Health =
Profile
f(
Healthome
Diseasome )
Health
Profile

15. HeByEq
• Health by Equation is an informatics system
for the prevention of diseases and the
maintenance of health.
• It can be readily accessed and used by
professionals around the world.
• By using its tables, decision matrices and
protocols, doctors can evaluate genetic,
clinical, and environmental data for a patient.
• They can then offer the patient
recommendations for treatment and disease
prevention.
• These recommendations are comprehensive,
individualised and safe, and are based on the
patient s health status and risk profile.

17. Opportunities
from
Health
Informatics
and
technology

18. 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
!!!

19. Enabling science and technology
• Broadband
technologies
and
networks
• High
performance
compu-ng
(and
A.I.
systems)
• Ubiquity
of
smartphones,
tablets,
and
apps
• Sensors,
imaging
and
wearables
• Personal
genome
sequencing,
gene-c
tes-ng
and
epigene-cs
• Metagenomics
and
the
Human
Microbiome
Project
• Social
networks,
games
and
the
Quan-fied
Self
• Knowledge
on
gene-c
diseases
and
gene-c
varia-on
• Systems
biology
modelling

20. Measuring the genome
• Human Genome Project
Maps of genetic
variation (Human
Variome)
DNA Sequencer –
designed to sequence
the entire human
genome in a day for
$1,000 Benchtop
Ion
Proton™

21. Measuring the phenome (physiology, …)

22. Measuring the Exposome
• Only 3% spent on prevention (USA), 2/3 of cancers are
preventable deaths (CDC) (NCI) (American Cancer Society),
72% of all chronic diseases are preventable
• 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)

23. Sensors for data collection
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

24. National Broadband Network

25. 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.

26. Personalized
medicine

27. Definition
• Personalized medicine uses an
individual's genetic (and molecular)
profile and individual information
about environmental exposures to
guide decisions made in regard to
(risk profiling) and the prevention,
diagnosis, and treatment of
disease.
(Adapted from F. Collins, Director NIH)

28. Clinical applications of genomic information
• Pharmacogenetics –
Personalized Medicine
Coalition - 72 drugs in 2011
• Cystic fibrosis – successful
clinical trial for a specific
mutation
• Identification of metabolic
diseases

29. Personal Genomics

30. Participatory
Health

31. Participatory 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

32. DIY EHR- The Cathedral and the Bazaar

33. Social media & 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

34. 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)

35. 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
self tracking devices
Social web
games
Participatory Health
mobile Internet of things
sensors PCEHR

36. NBN and patient empowerment
Current NBN-enabled Driving forces: patient empowerment,
networks personalized medicine, social networks
EHR Personally Citizens are able to maintain and control
Controlled EHR their own health information
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

37. Social media strategy
• “The democratization of information through social media is shaping
clinical encounters and the patient-provider relationship (Wen-ying
Sylvia Chou, NCI)
• Many health care organizations are reshaping their social media strategy
from marketing to engage patients, interact with them and even provide
services at lower cost.
• “Participatory Health Research is helping to expand the conceptual
scope of medicine from the traditional focus on disease cure to the
personalised preventative medicine of the future” (Melanie Swan)
• Be careful! – terms for use of social media.

38. Self-
omics

39. • Self tracking / self quantifying / 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 quantifying his body
– Weight – physical activity: calories burnt (body media) –
Food intake – Sleep (Zeo) – blood chemicals (60 Markers) –
cholesterol/triglycerides / Apo B / Ω – 6, Ω – 3/ C-reactive
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

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

41. 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 infections in the data-gathering
period, which left their molecular signature in the analyses.
• During one of these infections, his blood glucose levels began
to approach those of a diabetes sufferer. After changing his
diet and exercise habits, glucose level returned to normal.
• This study shows that diseases are a product of an
individual’s genetic profile as well as interaction with the
environment and that disease can be treated based on
molecular information.
(Chen et al, Cell 148, 1293-1307 March 16 2012 )

42. Personal Quantified Smartphones
Sensors
omics Self & apps
Selfomics
(Personal molecular profiles, life
habits, physiological measures,
environmental exposure)
Social media &
networks
Big data (Cloud)
Personalized Preventative Participatory
Medicine Medicine Health

43. The central role
of informatics

44. Health and Biomedical Informatics
• Informatics is the science of information
• Information is data plus meaning
• Biomedical informatics is the science of
information in the context of biomedicine.
• Informaticians study information (data + meaning).
• Thus, HBI practitioners must understand the
context or domain (biomedicine).
• Health Informatics – use of information, often aided
by technology, to improve individual health,
healthcare, public health and biomedical research
4

45. Adapted from: Stead et al. 2011, Acad. Med.

47. “A man in his late
80s with congestive
heart failure, failing
kidneys, weight and
appetite loss,
declining cognitive
ability and the need
for extensive
assistance has a 69
percent chance of
Hierarchical Association dying within six
Rule Model months”.

48. Role of informatics - New taxonomy of diseases
Stratification of disease – ICD 11 – US Nat Academy – Towards Precision Medicine
New taxonomy based on human molecular biology
skin, colon, parathyroid – BRAF Mutation
MD Anderson CC – Breast, Ovarian, Uterine, Cervical – PIK3CA Mutation trial

49. Role of informatics - Network and systems
medicine

50. Role of informatics - Measuring the exposome
Environment-Wide
Association Study
on Type 2
Diabetes Mellitus
266 environmental
Factors
Future: combined
GWAS-EWAS?
(Patel et al. 2010 PloS One)

51. HBIR @ UoM

53. Conclusions
• The routine application of personalised medicine is still a long
way ahead, however we have now all the ingredients to
make it happen.
• The convergence of medicine and the digital revolution will
produce an information ecosystem that will facilitate the
advent of safer and more efficient preventive, diagnostic and
therapeutic solutions.
• The citizen will have access to her genetic profile and clinical
record, and will monitor and adjust her health using next
generation sensors and social networks to share this
information with peers, clinicians and researchers.
• But
all
of
this
will
only
be
possible
if
we
realise
that
it
is
9me
for
us
to
take
responsibility
for
our
own
health.

54. Thank you for your attention!
© Copyright The University of Melbourne 2011