This document discusses integrated health monitoring and precision medicine. It defines precision medicine as using big data, clinical, molecular, environmental, and behavioral information to understand disease and improve prevention and treatment outcomes for patients. Integrated health monitoring combines data from various sources like personal health records, sensors, genomics, and environmental exposures to develop a dynamic model of a patient's health over time. Health informatics plays a key role in building systems to integrate these diverse data sources and enable precision medicine approaches.

1. Integrated health monitoring
Dr Fernando J. Martin-Sanchez FACHI FACMI
Professor and Chair of Health Informatics
Melbourne Medical School
&
Director, Health and Biomedical Informatics Centre (HaBIC)

2. Integrated Health Monitoring
Health
Informatics
Precision
Medicine
Participatory
health
Quantified
Self
Integrated
Health
Monitoring
Personal Health
Records
Exposome
Genome
Phenome
Big Data
Connected Health

3. Precision
Medicine

4. 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

5. Evolution - Precision medicine
• Work in this area is
aimed at redefining
disease
classification,
identifying common
underlying causes
and representing
them into new
taxonomies.
Toward Precision Medicine:
Building a Knowledge Network for Biomedical Research and
a New Taxonomy of Disease (2011)

6. 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.

7. 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

8. Participatory health
I. Personal genome services
II. Personal diagnostic testing
III. Personal medical image management
IV. Personal sensing and monitoring
V. Personal health records
VI. Patient reading doctor’s notes
VII. Patient initiating clinical trials
VIII. Patient reporting outcomes
IX. Patient accessing health information
X. Shared decision making
Collecting
data
Exchanging
and using
information
Participatory
health

9. The 4 V’s of BIG DATA

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

11. Exposome
The exposome has been
defined as the life-long
exposure to environmental
factors of an individual.

12. 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

13. Health self-monitoring

14. Quantified Self: The community

15. New market
Global annual wearable device
unit shipments crossing the 100
million milestone in 2014, and
reaching 300 million units five
years from now
Gartner hype cycle
Corporate health
plans – 13 Mill

18. 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)

20. 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/

21. 21
Zeo Sleep Manager
Fitbit
Actipressure
iBGStar
Sensaris Senspod uBiome
MoodPanda
23andMe
Variety of self-monitoring devices, sensors and services

22. 22

23. QS Lab

24. White Paper
http://www.broadband.unimelb.e
du.au

25. Second white paper – user guide

26. Second White Paper – Data integration methods
PHR
Integrated
Analysis
Individual analysis

27. All-in-one platforms for digital health
• WebMD - Healthy Target
• Samsung – S.A.M.I
• Apple – HealthKit
• Google – Google Fit
• Microsoft HealthVault
• Qualcomm Life – 2net

28. Benefits
If 10% adults USA began a
regular walking program, an
estimated $5.6 Billion in heart
disease could be saved.

29. Jawbone, California 6.0 Earthquake Aug 2014

30. Convergence between personalised and
participatory medicine

31. Health eHeart Study – A Digital Framingham Heart Study?
• One million people
• Monitor heart health in real time
using smartphone apps, sensors,
and other devices
• Information to be collected includes
blood pressure, diet, and sleep
habits
• Warning signs for various heart
conditions
• No doctor's visit is required in order
to participate!
• FHS collects data from its
participants every two years during
a physical checkup, leaving gaps
that Health eHeart's real-time data
collection can help fill.

32. 100 Person Wellness Project

34. The role of
Health
Informatics

35. Building blocks
Health Informatics
genomics, genomic epidemiology,
bioinformatics and computational
biology, molecular biology,
biochemistry, stem cells,
pharmacology, animal model testing,
clinical trials, clinical epidemiology &
biostatistics, clinical genetics,
biomedical engineering, imaging,
health economics, health services
research.

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

37. HaBIC
• The University has
recently 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 IBES.

38. Available platforms
• Infrastructure:
– UoM High-end computing. Alliance with ITSr – Research
Cloud, storage facilities, supercomputing
– VLSCI
• Platforms for data integration
– Biogrid (43 hospitals, clinical data, genomics)
– GRHANITE (GPs, labs, rural)
• Research support tools
– REDCap
– SAS Visual Analytics
– TranSMART
– PROMIS

39. Researcher
Hospital data
GP, labs, pharmacies data
Researcher-entered
data

40. Conclusions

41. Benefits
• Motivation
• Deepening understanding
of their health
• Self-improvement
• Risk profiling
• Prevention
• Shift terciary  secondary
 primary  home care
• Data donors for research
Challenges
• Privacy
• Security
• Education
• Cyberchondria
• Equity
• Regulation, accreditation
• Role of the clinician
• Infrastructure needs
• Therapeutic gap (ethics)
Conclusion

42. Almalki, M, Gray, K & Martin-Sanchez, F 2014, 'Classification of data and activities in self-
quantification systems', in proceeding of HISA BIG DATA 2014 conference.
Almalki, M, Gray, K & Martin-Sanchez, F 2014, 'Minimal Information about Human Computer
Interaction Framework: A Comprehensive Systematic Approach to the Practice of Self-
Quantification for Health Maintenance', paper submitted to 2015 Australasian Workshop on
Health Informatics and Knowledge Management.
Almalki, M, Gray, K & Martin-Sanchez, F 2014, 'The Use of Self-Quantification Systems for
Personal Health Information: Big Data Management Activities and Prospects', BMC Health
Information Science and Systems HISS.
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>.
42
References

43. Current meaning
• Precision medicine enables a safer, more
efficient, preventive and proactive
medicine, but needs to tackle the
complexity and diversity of personal health
information, beyond the genome
sequence.
Topol E. Cell 2014

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

46. •Both research into and clinical
application of stratified medicine, will
require comprehensive and robust
biomedical and health informatics
systems – a key rate-limiting step.
Stratified Medicine: Principles,
Promise and Progress
UK Academy of Medical Sciences
2013

47. © Copyright The University of Melbourne 2014
Thank you for your attention!