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)

Integrated Health Monitoring

Health

Informatics

Precision

Medicine

Participatory health

Quantified

Self

Integrated

Health

Monitoring

Personal Health

Records

Exposome

Genome

Phenome

Big DataConnected Health

Precision

Medicine

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

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)

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.

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

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

The 4 V’s of BIG DATA

Genome

regulation

Microbiome

Epigenome

Exposome

Inter and intra

individual

genetic variation

Phenome levels

Exposome

The exposome has been

defined as the life-long

exposure to environmental

factors of an individual.

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

Health self-monitoring

Quantified Self: The community

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

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)

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/

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Zeo Sleep Manager

Fitbit

Actipressure

iBGStar

Sensaris Senspod uBiome

MoodPanda

23andMe

Variety of self-monitoring devices, sensors and services

22

QS Lab

White Paper

http://www.broadband.unimelb.e

du.au

Second white paper – user guide

Second White Paper – Data integration methods

PHR

Integrated

Analysis

Individual analysis

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

Benefits

If 10% adults USA began a

regular walking program, an

estimated $5.6 Billion in heart

disease could be saved.

Jawbone, California 6.0 Earthquake Aug 2014

Convergence between personalised and

participatory medicine

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.

100 Person Wellness Project

The role of Health Informatics

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.

Health

Informatics

Bioinformatics

Proteomics

and

Metabolomics

Data

Gene

expression

Data

Genomic

Data

Patient

generated

Data

Population

Data

Clinical

Data

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.

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

Researcher

Hospital data

GP, labs, pharmacies data

Researcher-entered

data

Conclusions

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

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>.

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References

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

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

• 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

© Copyright The University of Melbourne 2014

Thank you for your attention!