On October 23rd, 2014, we updated our
By continuing to use LinkedIn’s SlideShare service, you agree to the revised terms, so please take a few minutes to review them.
From Personalised Medicine to Personal Health 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.
Outline• Current challenges in Medicine• Personalised Medicine• Personal Health• The role of Health Informatics• Conclusions
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 Preventive• Risk profiling, disease prediction and medicine prevention• Control health system costs Personal• Citizens should take more responsibility for Health the maintenance of their own health.àEmphasis on prevention, not cure
Definition• Personalized medicine uses an individuals 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)
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
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 !!!
Personal HealthRegina Holliday
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
Professionals (Clinicians and researchers)
GovernmentNIH Australian PCEHR
Personal (Participatory) Health - Technologiesà Patients empowered, informed and involved indecision making, prevention and learning self tracking devicesSocial networks games Participatory Health mobile Internet of things sensors PCEHR
The role ofhealth informatics
Data collection from sensors Environmental sensors Genomic sensors Phenomic sensorsEnvironmental risk factors Genome biomarkers (DNA sequence,(pollution, radiation, toxic agents, …) Phenome variation, regulation) Genome Exposome Physiological, biochemical parameters (cholesterol, temperature, glucose, heart rate…) Integrated personal health record
Data integration: Human Phenome Ontology
Data integration: Phenomizer
Data Interpretation: 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.• 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 )
Data Interpretation: Comprehensive molecular information analysis• genomic DNA copy number Comprehensive arrays molecular• DNA portraits of methylation human breast• exome tumours sequencing The Cancer• microRNA Genome Atlas sequencing Nature 490, 61–• reverse-phase 70 protein arrays (04 October 2012)
Data interpretation: 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)
Adapted from: Stead et al. 2011, Acad. Med.
From personalized medicine to personal health:Genome supercomputer to enhance interpretation
Interpretation of personal genome
Management of personal health data: Apps forhealth – the ‘Appatient’ Stress Glucose ECG Heart rate temperature Diet Saturation Drug reminder LifeWatch V
Self tracking / self quantifying / self monitoring• The belief that gathering and analysing data can help them improve their lives!• QS’ers doubling every year.– 6000 members, 50 meet-up groups
Shared decision making
Conclusions• Similarities – Need of system approaches – Integration of multiple sources of data – Advances in analytical technologies – Big data / data driven• Differences Personalised medicine Personal Health o Clinician-focus o Patient-centred o Focus molecular data o Focus environmental o Curing o Prevention
ConclusionsPersonal computing Personal sequencing Personal health?
ConclusionsPros 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)