Lecture at the Royal Children's Hospital CREW (Campus Research and Education Week) 2 Nov 2012

1. The rise of the ‘ePatient’:
how it is affecting
clinical practice and research
Fernando J. Martin-Sanchez
Professor and Chair of Health Informatics
Melbourne Medical School
Faculty of Medicine, Dentistry & Health Sciences
&
Director, IBES Health and Biomedical Informatics Research Lab.

3. • PERSONALIZED PREVENTIVE
HEALTH MAINTENANCE
• PARTICIPATORY MEDICINE
• PATIENT-CENTRED CARE
• DEMOCRATIZATION OF HEALTH
INFORMATION

4. E-patients
• Gimme my damn data!
• The patient will see you now…
• Let patients help
• Nothing about me without me!
• Dave de Bronkart
• Regina Holliday
• Hugo Campos
• Salvatore Iaconesi
• Marian Sandmaier

5. Participatory
Health
Regina Holliday

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

8. Participatory health
• Personal genomics
• Personal diagnostic testing
• Personal health records
• Personal medical images management
• Patient reading physician’s notes
• Patient-initiated clinical trials
• Patient reported outcomes measurement
• Sensors for Self-monitoring and self-quantifying
• Shared decision making

9. Personal Genomics

10. Personal diagnostic testing
• “Test at home, treat online”
• Urinary tract infection, strep throat, flu, cholesterol, Lyme
disease, Mono, sexually transmitted diseases, pregnancy,
yeast infections and others.
• (Not yet evaluated by regulatory agencies)

11. Personal Health Records
Australian PCEHR

12. Personal medical image management

13. Open Notes – Patients reading Doctor’s notes

14. Crowdsourced clinical trials
• Clinical Research with the patients, not on the patients
• Examples
– 23andMe – Parkinson’s Disease – PLoS Genetics, 2 new genetic
associations
– PatientsLikeMe – Nature Biotech. Self-reported data from 600
patients on the use of lithium for Amyotrophic Lateral Sclerosis
(ALS)
– Acor, RevolutionHealth, Curetogether, Genomera, Althea Health

15. Patient reported outcomes
• Health services
and outcomes
research
• Measuring quality
of care from the
patient
perspective NHS PROMs
NIH

16. • Self tracking / self quantifying / self monitoring
• The belief that gathering and analysing data can help
them improve their lives!
• QS’ers doubling every year.– 10K members, 65 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
• NODE Sensor Environment

17. 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

18. National Broadband Network

19. Shared decision making

20. The role of
informatics

21. Interpretation of personal genome

22. Shared decision support systems

23. Social Media for clinical guideline development

24. Apps for health - Appatient
Stress
Glucose
ECG
Heart rate
temperature
Diet
Saturation
Drug reminder
LifeWatch V

25. Participatory Health – Self-Omics (funded by IBES)

26. Participatory Health – Social media
!

27. Visualising personal health risks profiles
(Juhan Sonin, MIT) (Univ. Missouri)

28. GIS for Personal Health Information
disease
Acute
Spatial
Location
Exposome
Symptoms /
Chronic
disease
EHR
Body location
Microbiome
Epigenome
disease
Time
Acute
Genome
Volume of data
Data Types

30. BIG DATA – Melbourne April 2013

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

32. Dr. Charles Safran, AMIA

33. Education
HBIR @ UoM

34. UoM offer of HBI studies – Feb 2011
Subjects
(image
processing
,
genomics)
MD Masters at MDHS Master Master Master Master
(Public Health, …) of IS of IT of of
Bio- Biomedical
Graduate
informatics engineering
Undergraduate
Major
in infor-
Bachelor matics Bachelor
of Science of Biomedicine
34

35. UoM education strategy in HBI
Master of Bio-
5 PhD in Health Informatics6 informatics
2
Lectures
1 2 New 3 7 Subjects
(image
subjects New stream
processing,
Subjects or
HBI eHBIs & genomics)
on Health stream
Content eHBIm IT
MD Masters at MDHS Master Master New Masters Master
(Public Health, Nursing) of IS of IT (Cancer, of Biomedical
Ageing, engineering
Graduate
Information)
Undergraduate 4 Honours
Major
in infor New major in
Bachelor matics
health
informatics
Bachelor
of Science of Biomedicine
Lectures

36. Thank you for your attention!
© Copyright The University of Melbourne 2012