Tech Forum FJMS


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Talk given at Tech Forum 2012 at The University of Melbourne

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Tech Forum FJMS

  1. 1. 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.
  2. 2. Outline• Current challenges in Medicine• Personalised Medicine• Personal Health• The role of Health Informatics• Conclusions
  3. 3. Currentchallenges in medicine
  4. 4. 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.àEmphasis on prevention, not cure
  5. 5. Personalised medicine
  6. 6. 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)
  7. 7. 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
  8. 8. 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  !!!  
  9. 9. Personal HealthRegina Holliday
  10. 10. 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
  11. 11. Professionals (Clinicians and researchers)
  12. 12. GovernmentNIH Australian PCEHR
  13. 13. 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
  14. 14. The role ofhealth informatics
  15. 15. 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
  16. 16. Data integration: Human Phenome Ontology
  17. 17. Data integration: Phenomizer
  18. 18. 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 )
  19. 19. 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)
  20. 20. 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)
  21. 21. Adapted from: Stead et al. 2011, Acad. Med.
  22. 22. From personalized medicine to personal health:Genome supercomputer to enhance interpretation
  23. 23. Interpretation of personal genome
  24. 24. Management of personal health data: Apps forhealth – the ‘Appatient’ Stress Glucose ECG Heart rate temperature Diet Saturation Drug reminder LifeWatch V
  25. 25. 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
  26. 26. Shared decision making
  27. 27. Conclusions
  28. 28. 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
  29. 29. ConclusionsPersonal computing Personal sequencing Personal health?
  30. 30. 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)
  31. 31. Thank you for your attention!© Copyright The University of Melbourne 2012