Keynote presentation at MIE 2015 Madrid. Conference organised by EFMI and SEIS.

1. Fernando J. Martin-Sanchez
Professor and Chair of Health Informatics
Melbourne Medical School
&
Director, Health and Biomedical Informatics Centre (HaBIC)
28 May 2015
The new Era of Digital Medicine:
New challenges for
Health Informatics

3. OUTLINE
• What is Digital Medicine
• HaBIC@UoM
• Research
– Precision Medicine
– Participatory Health
• The role of Biomedical Informatics
– Social Media
– Self-quantification
– Exposome Informatics
• Final remarks

4. Digital
Medicine

5. Interesting times…

6. Patients are
impatient

7. Availability of devices, sensors, apps, DTC services
and Social Networks
Wearables
Sensors
DTC lab tests
Apps

8. Digital Medicine (Convergence of digital revolution and
medicine)
• We
have
witnessed
the
impact
of
the
digital
revolu6on
in
other
domains
(banking,
insurance,
leisure,
government,
…)
• Although
digital
technology
has
greatly
affected
healthcare
at
the
hospital
or
research
centre
level.
• The
digital
revolu6on
has
not
yet
reached
medicine
at
the
pa6ent/ci6zen
level
• THIS
IS
STARTING
TO
HAPPEN
NOW
!!!
Shaffer, D.W., Kigin, C.M., Kaput, J.J. & Gazelle, G.S. Stud. Health Technol. Inform. 80,195–204 (2002)

9. Nat Biotech
VOLUME 33 NUMBER 5 MAY 2015
Technology and products that are undergoing rigorous clinical
validation and/or that ultimately will have a direct impact on
diagnosing, preventing, monitoring or treating a disease,
condition or syndrome.

10. HaBIC
@UoM

11. HaBIC
• The University has
established a collaborative
Health and Biomedical
Informatics Centre (HaBIC),
with support from the
Faculty of Medicine,
Dentistry and Health
Sciences, the School of
Engineering and the
Government of Victoria-
funded Institute for a
Broadband-Enabled Society
(IBES).

13. Translational research informatics
Researcher
Hospital data
GP, labs, pharmacies data
Researcher-entered
data

14. Health
Informatics
Precision
Medicine
Participatory
health
Quantified
Self
Social Media Exposome
Genome
Phenome
Big Data
Connected Health
Research areas
Digital
Health

15. Precision
Medicine

16. Origin
• In 1902, Archibald
Garrod suggested
individuals were
different not only
phenotypically,
but also at the
biochemical level.

17. Origin
• The term ‘personalised
medicine’ was coined in
1999 by Robert Langreth
and Michael Waldholz
(Wall Street Journal
reporters) in an article to
describe the development
by pharmaceutical
companies of:
“a cornucopia of personalized
medicines that will produce
huge profits into the next
century”.

18. Genome
regulation
Microbiome
Epigenome
Exposome
Inter and intra
individual
genetic variation
Phenome levels

19. Marc Rubin,
Nature 2015

20. Precision medicine
• Precision Medicine is an approach to discover
and develop medicines, vaccines or routes of
intervention (behavior, nutrition, etc.) that enable
disease prevention and deliver superior
therapeutic outcomes for patients, by integrating
“Big Data”, clinical, molecular (multi-omics
including epigenetics), environmental and
behavioral information to understand the
biological basis of disease.
• This effort leads to better selection of disease
targets and identification of patient populations
that demonstrate improved clinical outcomes to
novel preventive and therapeutic approaches.
C.M. Christensen et al.. The innovator’s prescription a disruptive solution for health care.
McGraw-Hill, 2008

21. Personalised
Medicine
Data sources:
Precision
Medicine
New data sources
Exposome
(environmental data)
Metabolomics
Proteomics
Microbiome
Epigenome
Genomics (genomic
variants)
Phenotype (clinical
records)
Personalised vs Precision Medicine
PM combines the knowledge of the patient’s characteristics with traditional medical records
and environmental information to optimize health.
PM does not only rely on genomic medicine but also integrates any other relevant information
such as non-genomic biological data, clinical data, environmental parameters and the patient’s
lifestyle.
Servant N et al. Front Genet. 2014; 5: 152.

22. Personalised medicine
• Improving therapy
• Looking for the right drug for
the right people
• Companion diagnostics to
stratify patients
• Use of genomics data
• Static - “Snapshot”
Precision medicine
• Improving Diagnosis
• Looking for the right drug for
the right disease
• New taxonomy of disease and
disease reclassification
• New/refined diagnostics methods
• Use of molecular (-omics) and
other (i.e. exposome) data sources
• Dynamic stratification - Modelling
patient journeys
Personalised vs Precision Medicine

23. Participatory
Health

24. History of Participatory Health
• Tom Ferguson MD (died
in 2006)
• Coined the term e-
patient
• “e-patient: how they can
help us to heal health
care”
Shenkin B, Warner D.
Giving the patient his medical record:
a proposal to improve the system.
NEJM, 1973

25. Participatory Health
Participatory Health
mobile
Social networks
sensors
games
Internet of things
self tracking devices
PHR
2009. à Patients empowered, informed and involved in
decision making, prevention and learning

26. Patient advocacy

27. Health Informatics and
Participatory health
I. Personal genome services (23andMe)
II. Personal diagnostic testing
III. Personal medical image management
IV. Personal sensing and monitoring (QS)
V. Personal health records
VI. Patient reading doctor’s notes (OpenNotes)
VII. Patient initiating clinical trials (PLM)
VIII. Patient reporting outcomes (PROMIS)
IX. Patient sharing data (Social Media)
X. Shared decision making
Collecting
data
Exchanging
and using
information
Participatory
health

28. Therapeutic
affordances of
Social Media
The role of Biomedical Informatics
(Mark Merolli’s PhD work)

29. Source: http://www.commpro.biz/social-media-zone/top-14-things-to-talk-about-on-social-a-quick-and-dirty-list/
Source: http://pointsandfigures.com/2011/07/24/periodic-table-of-social-media/

30. Research Question Research Aim
How can we explain
social media’s effect
on the health outcomes
of people with chronic
disease?
To develop a framework
to generate evidence
of health outcomes
from social media use
in chronic disease
management

31. Key Research Concepts
Merolli M, Gray K, Martin-Sanchez F. Developing a Framework to
Generate Evidence of Health Outcomes From Social Media Use in
Chronic Disease Management. Med 2.0, 2013. 2(2): e3.
1 2 3

32. Self-presentation
Connection
Exploration
Narration
Adaptation

33. Shared
Experiences
& Frequency of
Use
Social,
Psychological
and Cognitive
Health Reports
Correlated to..
Merolli M, Gray K, Martin-Sanchez F, Lopez-Campos G. Patient-Reported Outcomes
and Therapeutic Affordances of Social Media: Findings From a Global Online Survey
of People With Chronic Pain. J Med Internet Res, 2015

34. 1. Merolli M, Gray K, Martin-Sanchez F, Lopez-Campos G. Patient-Reported Outcomes and Therapeutic
Affordances of Social Media: Findings From a Global Online Survey of People With Chronic Pain. J
Med Internet Res, 2015
2. McAlpine H, Joubert L, Martin-Sanchez F, Merolli M, Drummond KJ. A systematic review of types and
efficacy of online interventions for cancer patients. Patient Educ Couns. 2015 Mar;98(3):283-295.
3. Merolli M, Martin-Sanchez F, Gray K. Social Media and Online Survey: Tools for Knowledge
Management in Health Research, in Seventh Australasian Workshop on Health Informatics and
Knowledge Management. HIKM 2014, J. Warren and K. Gray, Editors. 2014, Conferences in Research
and Practice in Information Technology (CRPIT): Auckland, New Zealand. p. 21-29.
4. Merolli M, Gray K, Martin-Sanchez F, Schulz P. Expert insights on the design and implementation of
interactive patient websites for people with chronic pain. Stud Health Technol Inform, 2014. 204:
110-115.
5. Merolli M, Gray K, Martin-Sanchez F. Therapeutic Affordances of Social Media: Emergent Themes
From a Global Online Survey of People With Chronic Pain. J Med Internet Res, 2014
6. Merolli M, Gray K, Martin-Sanchez F. Health outcomes and related effects of using social media in
chronic disease management: A literature review and analysis of affordances. Journal of Biomedical
Informatics, 2013. 46(6): 957-969.
7. Merolli M, Gray K, Martin-Sanchez F. Developing a Framework to Generate Evidence of Health
Outcomes From Social Media Use in Chronic Disease Management. Med 2.0, 2013. 2(2): e3.
8. Miron-Shatz T, Hansen MM, Grajales FJ 3rd, Martin-Sanchez F, Bamidis PD. Social Media for the
Promotion of Holistic Self-Participatory Care: An Evidence Based Approach. Contribution of the IMIA
Social Media Working Group. Yearb Med Inform. 2013;8(1):162-8.
Publications

35. Self
quantification
The role of
Biomedical
Informatics
(Manal Almalki’s PhD work)

36. The Quantified Self community
• Quantified Self is a collaboration of users and tool
makers who share an interest in self knowledge through
self-tracking.
• We exchange information about our personal projects,
the tools we use, tips we’ve gleaned, lessons we’ve
learned. We blog, meet face to face, and collaborate
online. There are three main “branches” to our work.
– The Quantified Self blog and community site.
– Show and Tell meetings (Meetup groups) - Melbourne
– Quantified Self Conferences (US and Europe)
• Groups 177, Members 36,000, Cities 122, Countries 38

37. The IBES SELF-OMICS Project
• Addressing the information and communication needs of the
‘quantified individual’ for enabling participatory and
personalised medicine
• Funded by IBES (Institute for a Broadband Enabled Society)
- 2012-2013
• Resources:
http://www.broadband.unimelb.edu.au/health/monitoring/selfomics.html
http://www.scoop.it/t/selfomics
http://pinterest.com/hbir/self-omics-self-monitoring-quantified-self-omics/

38. QS Lab

39. 39

40. White Paper
http://www.broadband.unimelb.edu.au
Activity Theory
+
Patient Activation

41. 41
Classification of self-quantification systems
• Capture data directly from the user
(Primary or Secondary)
• Sensor Location (Mobile or Fixed)
• Involve skin pricking (In-contact or
On-body)
• Data type (Environmental or
Touchless)
• Location of data integration
(Software-based or Hardware-
based integration)
• Location of data
visualisation(Standalone, etc.)

42. Classification of Data and Activities

43. Data integration methods
PCEHR
Integrated
Analysis
Individual analysis
All-in-one-
Platforms

44. All-in-one platforms for digital health
• WebMD - Healthy Target
• Philips Salesforce
• Samsung – S.A.M.I
• Apple – HealthKit
• Google – Google Fit
• Microsoft HealthVault
• Qualcomm Life – 2net
• Validic
• Open Humans
• Human API

45. DeviceSample
Data
Where is
it stored
Units
Location
Time
Body part
(FMA)
Method
Name
Model
Manufacturer
Technical
Specs
Taxonomy
Body structure
Body function
Around body
(based on WHO)
Who/Which part/
Where/When?
What
How?
Processed
Raw
Minimum Information about a
Self Monitoring Experiment (MISME)
Procedures
EXPERIMENT
Measurement

46. Publications
Almalki, M, Gray, K & Martin-Sanchez, F 2014a, 'Classification of data and activities in self-quantification
systems', in proceeding of HISA BIG DATA 2014 conference.
G. Lopez-Campos, M. Almalki, and F. Martin-Sanchez, “Proposal for a Standardised Reporting Guideline to
Annotate Health-related Self-Quantification Experiments,” Stud Health Technol Inform, vol. 202, pp. 79-82,
2014.
Almalki, M, Gray, K & Martin-Sanchez, F 2014b, 'Minimal Information about Human Computer Interaction
Framework: A Comprehensive Systematic Approach to the Practice of Self-Quantification for Health
Maintenance', Proc. Australasian Workshop on Health Informatics and Knowledge Management.
M. Almalki, K. Gray, and F. Sanchez, “The use of self-quantification systems for personal health information:
big data management activities and prospects,” Health Information Science and Systems, vol. 3, no. Suppl
1, pp. S1, 2015.
Almalki, M, Martin-Sanchez, F & Gray, K 2013, Self-Quantification: The Informatics of Personal Data
Management for Health and Fitness, Institute for a Broadband-Enabled Society (IBES), The University of
Melbourne, Health and Biomedical Informatics Centre, University of Melbourne, 9780734048318,
<http://www.broadband.unimelb.edu.au/resources/white-paper/2013/Self-Quantification.pdf>.
Almalki, M, Gray, K & Martin-Sanchez, F 2015, 'The Use of Quantified-Self Technologies for Health Self-
Management: A Systematic Review of Empirical Research', IEEE Journal of Biomedical and Health
Informatics, Sensor Informatics and Quantified Self special issue. (Under review).

47. Exposome
Informatics

48. Genotype * Exposure à Phenotype
Coriell Personalised Medicine Collaborative

49. Health
Informatics
Bioinformatics
Proteomics
and
Metabolomics
Data
Gene
expression
Data
Genomic
Data
Patient
generated
Data
Population
Data
Clinical
Data

50. BMI - from particle to population
Altman RB, Balling R, Brinkley JF, Coiera E, Consorti F, Dhansay MA, Geissbuhler A, Hersh W, Kwankam
SY, Lorenzi NM, Martin-Sanchez F, Mihalas GI, Shahar Y, Takabayashi K, Wiederhold G. "Commentaries
on Informatics and medicine: from molecules to populations". Methods Inf Med. 2008;47(4):296-317.
PMID: 18690363
E
N
V
I
R
O
N
M
E
N
T

51. GenomeExposome
Phenome
Biomarkers (DNA sequence,
Epigenetics)
Environmental risk factors
(pollution, radiation, toxic agents, …)
Anatomy, Physiological, biochemical parameters
(cholesterol, temperature, glucose, heart rate…)
Social media / Integrated personal health record / Personal Health Systems
Availability of new sensors for data collection

52. Exposome informatics (JAMIA 2014)

53. Biomedical Informatics
Martin-Sanchez F et al. J Am Med Inform Assoc. 2014
Biomedical
Informatics

54. Exposome Resources
ENCODE
UN IPCC GHG
QIIME
CDC NHANES
EPA HPVIS
EPA NCBI
DDBJ
WormBase
VectorBase
NIOSH NOES
EPA CHAD
EPA NHAPS
EPA IUR
EPA TRI
Household Products DB
Cosmetic Voluntary Reg.
DB
SGD
NDAR
Protein Data Bank
GenBank
EU ESIS
Actor EPA
ToxRefDB
EPA Pesticide Usage Data
ATSDR Tox Profiles
FlyBase
CMIP3
PRISM
CTD
GO
ToxCastDB
ExpoCastDB
BioRefDB
DEA NFLIS
DevToxDB
ECOTOX DB
CESAR
DOE Indoor Air
NHEXAS
Tox21
IRIS
HPVIS
ChemSpider
PubChem
CTEPP
EPA NATA
EPA AIRS/AFS
SEER
VDW
TCGA
BAM
MCAPS
GEO/SRA
Ensembl
Factorbook
CGHub

55. Exposome related projects around the world
• USA - Funded by the NIEHS
– HERCULES. It is a joint centre between Emory University
and Georgia Institute of Technology
• Europe - European Commission funded
– HELIX – Coordinated by the Centre for Research in
Environmental Epidemiology (Barcelona, Spain)
– EXPOSOMICS Coordinated by Imperial College of London
– HEALS - Coordinated by the University Pierre and Marie
Curie (Paris, France) Health Environment Association
based on Large population Surveys
– NEW GENERIS - Newborns and Genotoxic exposure risks

56. Large scale studies with healthy individuals
Health Data Exploration
´Snyderome´
100 People Wellness

57. 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 effective
• Improve disease classification systems
• Risk profiling, disease prediction and
prevention
• Control health system costs
• Citizens should take more responsibility for
the maintenance of their own health.
àEmphasis on prevention, not cure
Precision
medicine
Participatory
medicine
Preventive
medicine

58. Opportunities for Biomedical Informatics
• Big, small, smart, fast data
• Standards for data collection and annotation
• Systems design and human-computer
interaction
• Integration of data with Clinical information
systems (EHR)
• Data analytics, visualisation and presentation
• Behaviour change
• Shared Decision support
• Validation, measurement of outcomes and
generation of scientific evidence
• Privacy and security
• Supporting new clinical trials n-of-1
• Understanding genetic-environment
interactions in disease progression

59. Final remarks
(with a note of humor)

62. © Copyright The University of Melbourne 2015
Thank you for your attention!