Personalised and Participatory Medicine Workshop15 may 2012
This document discusses how broadband technologies can enable personalized and participatory medicine. It describes how broadband can help improve youth mental health services and aged care through remote monitoring, telehealth, and electronic health records. The convergence of medicine and digital technologies is creating an information ecosystem that will facilitate more efficient preventative, diagnostic and therapeutic solutions where citizens have access to their genetic and health data. High-capacity broadband networks that transmit large volumes of data will be important for concepts like personalized medicine and participatory health to become feasible.
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Personalised and Participatory Medicine Workshop15 may 2012
- 1. Personalised and Par,cipatory Medicine Workshop, 15 May 2012 Fernando Mar*n-‐Sanchez Ins$tute for a Broadband-‐Enabled Society & Melbourne Medical School
- 2. Introduc$on • Broadband can provide many opportuni$es for the health sector: – Improving youth mental health and aged care services – Monitoring health condi$ons – Enabling shared electronic health records – Telehealth • Convergence with other technologies towards Digitally Enabled Personalized and Par$cipatory Medicine
- 3. Aging Well – Mobile and broadband technologies for ameliora,ng social isola,on in older people – Smart Homes for the Elderly – recent developments in Korea Youth Mental Health − HORYZONS: Online Recovery for Youth Onset Psychosis
- 4. Telehealth – Individual Electronic Health Records – The Telestroke Study – Hap*c Tele-‐Rehabilita/on – Teleden/stry – Virtual visits: Inves*ga*ng the acceptability of webcam consulta*ons for young adults’ sexual health – Wireless broadband monitoring of knee osteoarthri/s – Overcoming geographical barriers for community health – Interpreter mediated cogni*ve assessments using video conferencing soFware – SeeCare IPTV: Personalised Health Literacy Demonstrator – Mobile Augmented Reality – Interpreter mediated cogni/ve assessments using video conferencing soFware – High resolu*on monitoring of atmospheric pollutants to iden*fy their impact on popula*on health – Overcoming geographical barriers for community health – Using video-‐conferencing to pilot an educa*on and clinical support package for rural GPs in Mildura
- 5. Current challenges in Medicine • Need of earlier diagnosis • More personalized therapies • Clinical trials and the development of new drugs need to be faster and more effec$ve • Improve disease classifica$on systems • Risk profiling, disease predic$on and preven$on • Control health system costs • Ci$zens should take more responsibility for the maintenance of their own health.
- 6. The Digitalization ofMedicine • 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$zen level • BUT THIS IS STARTING TO HAPPEN NOW !!!
- 7. Vision • The convergence of medicine and the digital revolu$on will produce an informa,on ecosystem that will facilitate the advent of safer and more efficient preven$ve, diagnos$c and therapeu$c solu$ons. • The ci$zen will have access to her gene,c profile and clinical record, and will monitor and adjust her health using next genera$on sensors and social networks to share this informa$on with peers, clinicians and researchers.
- 8. High-‐capacity Broadband technologies and networks • The availability of ultra-‐high-‐speed, high-‐capacity, ubiquitous, ‘always-‐on’ broadband connec$vity will contribute to the development of an integrated digital infrastructure for medicine, reaching the ci,zen, that will make feasible the concepts of personalized medicine and par$cipatory health. • Ultra high speed broadband networks will be required to transmit enormous volumes of data from pa$ents’ homes to health prac$$oners and vice versa in a $mely manner, and to enable the processing of this deluge of data.
- 9. Collecting genome data • Benchtop Ion Proton™ Sequencer – designed to sequence the en$re human genome in a day for $1,000
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- 11. Collec$ng exposome data • Compilation of exposures experienced over an individual lifetime (Christopher Wild, 2005) • OUTSIDE / INSIDE – Absorbed -- Industrial chemicals, combustion emissions, radiation, response to stress, physical activity levels – heat/cold, noise, food, microbiome • Evaluating Personal Exposures • Phones: Light meters, GPS, Accelerometer • Senspod Monitor (Ozone, carbon monoxide, CO2, NO, Noise and UV) • Arizona State University – Petroleum derived hydrocarbons (Benzene, Toluene)
- 12. Remote pa$ent data monitoring and data collec$on 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
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- 14. Patient Data (sensorsand imaging) Sensors Genomic Phenomic Environmental Integrated Personal EHR Health Record Module 1 Health Profile GWAS Assessment Tables (weighted factors) Modelling Risks Diagnosis Personal Health Profile CDSS Health Profile Module 2 Improvement Trialbanks Networks Risk reduction Decision matrix, protocols Follow-up Personalised Therapy Health Recommendations
- 15. Personalized medicine
- 16. Defini$on • Personalized medicine uses an individual's gene*c (and molecular) profile and individual informa*on about environmental exposures to guide decisions made in regard to (risk profiling) and the preven*on, diagnosis, and treatment of disease. (Adapted from F. Collins, Director NIH)
- 17. Clinical applica$ons of genomic informa$on • Pharmacogene$cs – Personalized Medicine Coali$on -‐ 72 drugs in 2011 • Cys$c fibrosis – successful clinical trial for a specific muta$on • Iden$fica$on of metabolic diseases
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- 19. Self-‐genomics -‐ Clinical annota$on of individual genomes • Prof. Quake -‐ Stanford -‐ -‐ Nature gene$cs paper -‐ $50.000, 1 week, Helicos. Stanford team -‐ • Clinical annotation of genome from “patient Zero” – Drug metabolism – Rare gene$c variants -‐ rare diseases – Common gene$c variants -‐ Risk of complex diseases Ashley et al. The Lancet, Volume 375, Issue 9725, Pages 1525 - 1535, 1 May 2010
- 20. First personal longitudinal OMICS profiling exercise • Combined analysis of genomic, transcriptomic, proteomic, metabolomic and immunological profiles from a single individual (one of the authors-‐ Prof. Michael Snyder), over a 14 month period. More than 3 billion measurements. • He contracted two mild viral infec$ons in the data-‐gathering period, which lem their molecular signature in the analyses. • During one of these infec$ons, his blood glucose levels began to approach those of a diabetes sufferer. Amer changing his diet and exercise habits, glucose level returned to normal. • This study shows that diseases are a product of an individual’s gene$c profile as well as interac$on with the environment and that disease can be treated based on molecular informa$on. (Chen et al, Cell 148, 1293-‐1307 March 16 2012 )
- 21. Par$cipatory Health
- 22. Par$cipatory Health • • From Web 1.0 – Use of internet to find health information to Web 2.0 – web-based communities and services. NHS Social Care Model (NHS) • A survey of 1,060 U.S. adults by the PwC Health Research Institute found that a third of respondents are gravitating toward social media as a place for discussions of health care. • Pew Internet study – 27% of US internet users had tracked health data online • Care management, disease management, supported self-care, promoting better health à Patients empowered, informed and involved in decision making, prevention and learning
- 23. Par$cipatory Health self tracking devices Social web games Participatory Health mobile Internet of things sensors PCEHR
- 24. PCEHR • Quality = patients reviewing their own records - Shared Medical Records • MyHealth@Vanderbilt – information on prescriptions is shared. Knowledge management team – consumers will have convenient e-access to their medical records and genetic profiles to social media & games • Facebook • Lifeline – support line for suicide • Organ donor status • Blood type – app will contact the user
- 25. DIY EHR-‐ The Cathedral and the Bazaar
- 26. Social media as a research tool • We are witnessing a transi$on from research informa$on systems centralized at hospitals and clinical research centres to distributed systems that reach out to the residence of any ci$zen / pa$ent who opts in. • Clinical Research with the pa$ents, not on the pa$ents • Examples – 23andMe – Parkinson’s Disease – PLoS Gene$cs, 2 new gene$c associa$ons – Pa$entsLikeMe – Nature Biotech. Self-‐reported data from 600 pa$ents on the use of lithium for Amyotrophic Lateral Sclerosis (ALS)
- 27. Crowdsourced clinical trials • DIY science, Crowdsourced Health Research Studies, Citizen science, Amateur Scientist, Self- Experimentation • Patients Like Me – 125.000 members. 1000 condition- based communities –25 Papers published in PNAS, Nat Biotech, JMIR, … • 23andme – 23 and we – • Acor, RevolutionHealth, Curetogether, Genomera, Althea Health
- 28. • Self tracking / self quan$fying / self monitoring • The belief that gathering and analysing data can help them improve their lives! • QS’ers doubling every year.– 5524 members, 42 meetup groups • Larry Smarr– 10years quan$fying his body – Weight – physical ac$vity: calories burnt (body media) – Food intake – Sleep (Zeo) – blood chemicals (60 Markers) – cholesterol/triglycerides / Apo B / Ω – 6, Ω – 3/ C-‐reac$ve protein -‐ Ultrasound – (plaque in arteries) – stool test – colonoscopy – DNA – Microbiome • Fitbit – Sleep – Movement • +9000 health apps, each person connected to 140 devices, 9 billion of connected devices now, 24 billion by 2020 • NODE Sensor Environment
- 29. Pa$ent empowerment Current NBN-enabled Driving forces: patient empowerment, networks personalized medicine, social networks EHR – Personally Citizens are able to maintain and control Electronic Controlled EHR their own health information Health Record Gene-disease Personal Citizens ask for genetic analysis of their association genomics DNA through the Internet and receive studies reports on various aspects of their health Clinical trials Crowdsourced The patient voluntarily shares information clinical trials on treatments and evolution of his/her illness with other patients
- 30. Barriers • New regulatory framework (new models of clinical trials) • New informa$cs methods to compile and interpret all the informa$on • Educa$on of pa$ents and health professionals • Ethics, data security and confiden$ality issues • Wide availability of clinical decision support systems at the point-‐of-‐care • New cost-‐effec$veness assessment and financial models of care • Need to prove clinical effec$veness before DTC services are offered.
- 32. Thank you sms@unimelb.edu.au www.healthinforma$cs.unimelb.edu.au Twiuer: @ibeshbir