Rise of e patient fjms


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

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Rise of e patient fjms

  1. 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. 3. 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
  4. 4. Participatory HealthRegina Holliday
  5. 5. 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  !!!  
  6. 6. Participatory Healthà Patients empowered, informed and involved indecision making, prevention and learning self tracking devicesSocial networks games Participatory Health mobile Internet of things sensors PCEHR
  7. 7. 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
  8. 8. Personal Genomics
  9. 9. 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)
  10. 10. Personal Health Records Australian PCEHR
  11. 11. Personal medical image management
  12. 12. Open Notes – Patients reading Doctor’s notes
  13. 13. 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
  14. 14. Patient reported outcomes•  Health services and outcomes research•  Measuring quality of care from the patient perspective NHS PROMs NIH
  15. 15. •  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
  16. 16. Sensors for data collection Environmental sensors Genomic sensors Phenomic sensorsEnvironmental 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
  17. 17. National Broadband Network
  18. 18. Shared decision making
  19. 19. The role ofinformatics
  20. 20. Interpretation of personal genome
  21. 21. Shared decision support systems
  22. 22. Social Media for clinical guideline development
  23. 23. Apps for health - Appatient Stress Glucose ECG Heart rate temperature Diet Saturation Drug reminder LifeWatch V
  24. 24. Participatory Health – Self-Omics (funded by IBES)
  25. 25. Participatory Health – Social media !
  26. 26. Visualising personal health risks profiles(Juhan Sonin, MIT) (Univ. Missouri)
  27. 27. GIS for Personal Health Information disease Acute Spatial LocationExposome Symptoms / Chronic disease EHRBody locationMicrobiomeEpigenome disease Time Acute Genome Volume of data Data Types
  28. 28. BIG DATA – Melbourne April 2013
  29. 29. IssuesPros 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)
  30. 30. Dr. Charles Safran, AMIA
  31. 31. EducationHBIR @ UoM
  32. 32. 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- BiomedicalGraduate informatics engineeringUndergraduate Major in infor- Bachelor matics Bachelor of Science of Biomedicine 34
  33. 33. UoM education strategy in HBI Master of Bio- 5 PhD in Health Informatics6 informatics2 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, engineeringGraduate Information)Undergraduate 4 Honours Major in infor New major in Bachelor matics health informatics Bachelor of Science of Biomedicine Lectures
  34. 34. Thank you for your attention!© Copyright The University of Melbourne 2012