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  1. 1. An Introduction into Social Media, Web-Based Interventions and Technologies for Participatory Health Prof Fernando Martin-Sanchez, Professor Elizabeth Murray, Professor Jane Gunn & Dr Sylvia Kauer General Practice and Primary Health Care Academic Centre & Health and Biomedical Informatics Centre
  2. 2. Welcome! twitter #SMePCAus
  3. 3. Program Time Session 9am Registration 9.15 Intro and Background 9.45 Intro into Participatory Health Research 10.15 Social Media 10.45 Morning Tea 11am Self-monitoring 11.30 Web-based interventions 12.15 Lunch 1.15 Workshop: Implementing IT in clinical Practice 3pm Afternoon tea 3.15-4pm Panel Discussion
  4. 4. Survey findings Who’s here today? Community health centre / organisations Universities Medicare Locals Hospital Clinical Practice 0 2 4 6 8 10 12 14 16 Main interest for today? Internet interventions Social media Participatory health Other 0 2 4 6 8 10 12 14
  5. 5. Survey findings Confidence from 1 (not at all) to 5 (very) 5 4 3 2 1
  6. 6.  82% use internet in Australia  80% of users search for health information online INTERNET USE
  7. 7. 130% mobile phone ownership Smartphone estimates from 60 – 84% 45,000 apps on Apple App Store in fitness, health and medical category MOBILE PHONE USE
  8. 8. Web 2.0 Social Media Selfmonitoring Participatory health Internet intervention s
  9. 9. One-way communication Two-way communication Icons licenced under Creative Commons:
  10. 10. Web 2.0 Social Media
  11. 11. Social Media 4/Social_Media_Landscape_2013.jpg accessed 7/2/14. Licenced by Creative Commons. Attribution-NoDerivs 2.0 Generic (CC BY-ND 2.0)
  12. 12. Social media potentials for clinical practice • Keep up to date • Share knowledge with other professionals • Improve quality of care • Need caution – E.g., Legal disclaimers on online messages – Privacy issues – Connectivity issues
  13. 13. Web 2.0 Social Media Selfmonitoring
  14. 14. Self-monitoring devices
  15. 15. Web 2.0 Social Media Selfmonitoring Internet intervention s
  16. 16. Web-based interventions
  17. 17. Web 2.0 Social Media Selfmonitoring Participatory health Internet intervention s
  18. 18. Patient-centred care Less GP acceptability Censor Dismiss Internet-sourced information Redirect Refer Research later Reflect Reframe Research together Greater GP acceptability Acknowledge Reinforce Relationship Model Doctor-centred care Patient-centred care Sourced from Dr Kelvin Lau‟s Masters Thesis Department of General Practice, 2013
  19. 19. Virtual clinics Screening Electronic medical records Electronic prescriptions Dr Google – sourcing information from the web OPPORTUNITIES
  20. 20. Sourcing information from the web „Do no harm‟ Privacy issues CHALLENGES
  21. 21. • Don‟t have the answers but: – The capabilities are exciting and full of potential – People are using it and we can‟t ignore that • What we can do? – Test it ourselves and our patients in clinics – Assess the potential risks vs benefits – Always with a critical eye
  22. 22. Introduction into Participatory Health Prof Fernando Martin-Sanchez General Practice and Primary Health Care Academic Centre & Health and Biomedical Informatics Centre
  23. 23. The Digitalization of Medicine • Digital revolution in other domains (banking, insurance, leisure, government,…) • The incorporation 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 revolution has not yet reached medicine at the patient/citizen level • BUT THIS IS STARTING TO HAPPEN NOW !!!
  24. 24. Consumer engagement • 75% of patients want to view their medical records online • 76% for lab test results • 65% - appointments by email • 62% - online communication with theur primary physician • Physicians are adopting IT • Mobile devices and social media Source: Optum Institute, June 2012
  25. 25. Participatory Health Regina Holliday The Society for Participatory Medicine defines participatory medicine as a movement in which networked patients shift from being mere passengers to responsible drivers of their health, and in which medical care providers encourage and value them as full partners.
  26. 26. 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
  27. 27. Devices • • • • • • Wearables Sensors DTC lab tests Smartphones iPods Tablets
  28. 28. • • • • • • • • Society for Participatory Medicine E-patients Health 2.0 Quantified Self Medicine X Patients included Medicine 2.0
  29. 29. 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
  30. 30. Participatory health I. II. III. IV. V. VI. VII. VIII. IX. X. Personal genome services Personal diagnostic testing Personal medical image management Personal sensing and monitoring Personal health records Patient reading doctor‟s notes Patient initiating clinical trials Patient reporting outcomes Patient accessing health information Shared decision making Collecting data Participatory health Exchanging information
  31. 31. Personal Genomics • 23andMe: assess your disease risk
  32. 32. 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)
  33. 33. Personal medical image management accessed 7/2/14
  34. 34. Quantified self • Self tracking / self quantifying / self monitoring • The belief that gathering and analysing data can help them improve their lives! • Groups 112, Members 17,893, Cities 89, Countries 31 • Quantified Self is a collaboration of users and tool makers who share an interest in self knowledge through self-tracking. • There are three main “branches” to our work. – The Quantified Self blog and community site. – Show and Tell meetings (Meetup groups) - Melbourne – Quantified Self Conferences (US and Europe
  35. 35. Personal Health Records • Do it yourself electronic health records & e-patient community research on Crohn‟s disease:
  36. 36. Open Notes – Patients reading Doctor’s notes
  37. 37. 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
  38. 38. Patient reported outcomes • Health services and outcomes research • Measuring quality of care from the patient perspective • Promis • pcornet – national patient-centred clinical research network • pcori • NHS PROMs
  39. 39. Patient accessing/organizing health information accessed 7/2/14 accessed 7/2/14
  40. 40. Shared decision making
  41. 41. Visualising personal health risks profiles (Juhan Sonin, MIT) (Univ. Missouri)
  42. 42. The Blue Button and Blue Button + The Blue Button is a symbol for patients to view online and download their own personal health records. Meaningful use – V/D/T View/ Download/ Transmit. Blue Button+ extends the Blue Button concept to include a standardized data format and additional functionality
  43. 43. • Patient apps for Improved Health Care by the IMS Institute • imshealth/Global/Content/Corporate/IM S%20Health%20Institute/Reports/Patie nt_Apps/IIHI_Patient_Apps_Report.pdf
  44. 44. Creating and prescribing personalised apps • Such as
  45. 45. Tensions Clinicians‟ resistance to change Patient advocates
  46. 46. MEDICINE PARTICIPATORY HEALTH Provider-centric Patient or Consumer-centric Curative Proactive Passive role of the patient Active Clinical decision making Shared decision making Electronic medical record Patient Health Record
  47. 47. Evolution Shenkin B, Warner D. Giving the patient his medical record: a proposal to improve the system. NEJM, 1973
  48. 48. Benefits • • • • • • • • • Better outcomes Lower costs Better patient experience Motivation Deepening understanding of their health Self-improvement Risk profiling Prevention Shift terciary  secondary  primary  home care • Data donors for research
  49. 49. Issues • • • • • • • • • Privacy Security Education Cyberchondria Equity Regulation, accreditation Role of the clinician Infrastructure needs Therapeutic gap (ethics)
  50. 50. Dr. Charles Safran, AMIA
  51. 51. Thank you for your attention! © Copyright The University of Melbourne 2012
  52. 52. Social Media. Creating a professional presence, staying up-to-date and dissemination Prof Fernando Martin-Sanchez General Practice and Primary Health Care Academic Centre & Health and Biomedical Informatics Centre
  53. 53. Creating a professional presence
  54. 54.
  55. 55. Staying up-to-date
  56. 56. RSS • RSS (Rich Site Summary) is a format for delivering regularly changing web content. • Many news-related sites, weblogs and other online publishers syndicate their content as an RSS Feed to whoever wants it. For examples see
  57. 57. Staying up-to-date – RSS feeds aggregator
  58. 58. Netvibes
  59. 59. Content curation – Scoop it
  60. 60. Content curation- Scoop it
  61. 61. Disseminating information
  62. 62. Self-monitoring Prof Fernando Martin-Sanchez General Practice and Primary Health Care Academic Centre & Health and Biomedical Informatics Centre
  63. 63. Quantified Self: The concept
  64. 64. Quantified Self: The community
  65. 65. New market Global annual wearable device unit shipments crossing the 100 million milestone in 2014, and reaching 300 million units five years from now. Gartner‟s hype cycle illustrates this ( 515) Corporate health plans – 13 Mill
  66. 66. The Quantified Self community • Quantified Self is a collaboration of users and tool makers who share an interest in self knowledge through self-tracking. • We exchange information about our personal projects, the tools we use, tips we‟ve gleaned, lessons we‟ve learned. We blog, meet face to face, and collaborate online. There are three main “branches” to our work. – The Quantified Self blog and community site. – Show and Tell meetings (Meetup groups) Melbourne – Quantified Self Conferences (US and Europe)
  67. 67. QS Lab
  68. 68. White Paper
  69. 69. Classification of self-quantification systems • Capture data directly from the user (Primary or Secondary) • Sensor Location (Mobile or Fixed) • Involve skin pricking (In-contact or On-body) • Data type (Environmental or Touchless) • Location of data integration (Software-based or Hardwarebased integration) • Location of data visualisation(Standalone, etc.) 75
  70. 70. What to measure (adapted by Manal Almalki from WHO ICF) International Classification of Functioning, Disability and Health (ICF)
  71. 71. Second white paper – user guide
  72. 72. Self-monitoring Devices – information flow iBGStar Glucose Meter integrated with iPhone Aortic stenosis integrated with iPhone iRhythm TRANQS Beta myVitali TicTrac Beta Zeo sleep monitor Individual analysis Integrated Analysis FitBit
  73. 73. Devices compatible with MicroSoft HealthVault • • • • • • • • • Bayer Diabetes Care Carematix: Blood Glucose Meter CareMatix: Weight scale FitBit One: Activity + sleep tracker FitBit Ultra: Activity + sleep tracker FitBit Zip: Activity tracker Omron: Blood pressure Medapp: Pulse oximeter Microlike: Blood pressure
  74. 74. Self-Omics • QS as an interface to the Human Body • How much information? • People-as-sensors • Making the personal public • From population surveillance to individual surveillance Infography: Institute for Health Technology Transformation
  75. 75. Benefits
  76. 76. Health eHeart Study – A Digital Framingham Heart Study? • One million people Major Health Study will get its data from personal tech – Kecia • Monitor heart health in real time using smartphone Lynn (March 19, 2013) • • If 10% adults USA began a regular walking program, an • estimated $5.6 Billion in heart disease could be saved. • apps, sensors, and other devices Information to be collected includes blood pressure, diet, and sleep habits Warning signs for various heart conditions No doctor's visit is required in order to participate! FHS collects data from its participants every two years during a physical checkup, leaving gaps that Health eHeart's real-time data collection can help fill.
  77. 77. Self-monitoring • Project MUM-Size – Study of very obese pregnant women – risk of complications due to anesthesia during labor – Using fitbit and social media support by research midwives in the intervention group to prevent weight gain during pregnancy – Detection of constrains (Aria scale not suitable for pregnant women, limit of 140 Kgs of weight)
  78. 78. Availability of new sensors for data collection Exposome Genome Phenome Environmental risk factors (pollution, radiation, toxic agents, …) Biomarkers (DNA sequence, Epigenetics) Anatomy, Physiological, biochemical parameters (cholesterol, temperature, glucose, heart rate…) Social media / Integrated personal health record / Personal Health Systems
  79. 79. References • Almalki, M, Martin-Sanchez, F & Gray, K 2013, 'Self-Quantification: The Informatics of Personal Data Management for Health and Fitness‟, Institute for a BroadbandEnabled Society (IBES). • Almalki, M, Martin-Sanchez, F & Gray, K 2013. The Use of Self-Quantification Systems: Big Data Prospects and Challenges. Proceedings of HISA BIG DATA 2013 conference. Accepted for publication at BMC Health Information Science and Systems 85
  80. 80. Workshop Implementing IT in clinical practice Prof Jane Gunn PhD Candidates Marianne Webb, Mark Merolli & Manal Almalki General Practice and Primary Health Care Academic Centre & Health and Biomedical Informatics Centre
  81. 81. 1.15 – 1.45pm Interview with Phd Students: Marianne Webb, Manal Almalki & Mark Merolli 1.45 – 2.45pm Small Group work 2.45-3.00pm Feedback WORKSHOP
  82. 82. Online psychosocial screening tool for general practice MARIANNE WEBB
  83. 83. Towards a comprehensive personal health information selfquantification system MANAL ALMALKI
  84. 84. Social media for chronic pain management MARK MEROLLI
  85. 85. Mark Merolli - PhD
  86. 86. iInternet intervention self monitoring social media Readings • Mansfield et al (2010) Social Media and the Medical Profession: A guide to online professionalism for medical practitioners and medical students. Sourced from: • Greene et al. (2011) Online social networking by patients with diabetes: a qualitative evaluation of communication with Facebook. J Gen Intern Med. 26(3): 287-92 • Lupton (2013) Understanding the human machine. IEEE Tech Soc Mag. Winder issue • Morris & Aguilera (2012). Mobile, social, and wearable computing and the evolution of psychological practice. Prof Psy: Res Pract. 43(6): 622-6 • Whitehouse et al. (2013) Co-creation with TickiT: designing and evaulating a clinical ehealth platform for youth. JMIR Res Protoc. 2(2): e42. • Murray et al. (2012) Widening access to treatment for alcohol misuse: description and formative evaluation of an innovative web-based service in one primary care trust. Alcohol Alcohol. 47(6): 697-70
  87. 87. 45 minutes What are the top 3-6 uses for this technology in clinical practice? Choose one example and make a plan for how you would introduce its use What are the challenges?
  88. 88. Sharing examples of using technology in clinical practice FEEDBACK
  89. 89. Slides will be available at: slideshare/SMeGPAus Connect with us @GPPHCAC #SMePCAus SMeGPAus General Practice and Primary Health Care Academic Centre & Health and Biomedical Informatics Centre