Rise of e patient fjms
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The rise of the 'ePatient': how it is affecting clinical practice and research The document discusses how engaged patients, or "ePatients", who actively gather their own health data and conduct their own research are affecting clinical practice and research. It describes how ePatients are empowered through personal health records, diagnostic testing, genomic data, and self-monitoring devices. This shift towards participatory health challenges traditional clinician-led models and will require changes in areas like privacy, education, and how data is integrated into care.
Rise of e patient fjms
- 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 a鍖ected 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 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 physicians notes ≒ Patient-initiated clinical trials ≒ Patient reported outcomes measurement ≒ Sensors for Self-monitoring and self-quantifying ≒ Shared decision making
- 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 Doctors notes
- 14. Crowdsourced clinical trials ≒ Clinical Research with the patients, not on the patients ≒ Examples 23andMe Parkinsons 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! ≒ QSers 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 / 立 6, 立 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
- 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 secondary ≒ Role of the clinician primary home care ≒ Infrastructure needs ≒ Data donors for research ≒ Therapeutic gap (ethics)
- 32. Dr. Charles Safran, AMIA
- 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! 息 Copyright The University of Melbourne 2012