Participatory OpenmHealth


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Presented by Deborah Estrin at the OECD-NSF Workshop Building a Smarter Health and Wellness Future, Feb. 15, 2011

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Participatory OpenmHealth

  1. 1. Participatory mHealth: an opportunity for innovation in healthcare, wellness, research Deborah Estrin,, with collaborators from CENS, UCLA and UCSF (Ida Sim) Patient self-care innovation happens outside the traditional enterprise and clinical workflows but it can still contribute to, and be, evidence-basedMonday, July 25, 2011 1
  2. 2. Why mobile/mHealth? •3 lifestyle behaviors (poor diet, lack of exercise, smoking) cause 1/3rd of US deaths; 50% Americans have 1 or more chronic diseases; age of onset getting younger. •mHealth apps allow care support/data collection 24x7--chronic disease prevention/management/research as part of daily life •affordability/adoptability could support groundswell of medical discovery, evidence-based practice about treatment/prevention vision: support individuals, communities, clinicians to continuously improve patient-centered, personalized, health and healthcare mobile devices offer proximity, pervasiveness, programmability, personalization complementary to Internet interventionsMonday, July 25, 2011 2
  3. 3. Mobile devices can extend interventions and research beyond the reach of traditional clinical care 168 hours a week...1440 minutes a day...(but not necessarily 365 days a year) our actions our self report personal data repository experience sampling streams context and activity traces Photo: Marshall Astor, WWW aggregate measures, trends, patterns event detection visualization processingMonday, July 25, 2011 3
  4. 4. Whose mHealth? • A woman who is pre-diabetic tracks how eating/exercise habits affect weight and fatigue; also explores effective, comfortable blood pressure Rx dosage. • A young man who is struggling to find a treatment plan for depression believes medication dose is ineffective; doctor blames poor sleep habits/non-adherence. Patient self-monitoring includes medication reminder/verifications, sleep survey, activity traces, to guide adjustments in care plan, discussion of root causes. • A middle-aged woman who does not respond well to medication for psoriasis monitors diet, stress, environmental factors; initiates data campaign via social networking site for psoriasis sufferers. Each volunteer runs mHealth app for 2-months to create large data set to mine for patterns that precede flare-ups. • A group of high schoolers with asthma map their inhaler use and make a case for shifting Track practice to an alternate location farther from the freewayMonday, July 25, 2011 4
  5. 5. Integrated personal data streams create Living Records { UI SKETCH } Jane Doe Messages (1) Create ODL prev month May 2009 next month Automatically prompted, geocoded, uploaded, Interventions May 1 May 31 analyzed: Drug Prescribed Patient prescribed new daily medication. START DATE: May 26, 2009 Weight in pounds Measurements and trends for the month. AMOUNT: 200 mg - physiological (weight, BP, glucose...) FREQUENCY: One pill twice per day, once in the160 morning and once in the evening. - patient reporting (medication, PREV. AMT.: 150 mg symptoms, stress factors) PREV. FREQ.: Same NOTES: Enter observations here. - activity (location traces, exercise, sleep)150 May 1 May 31 Blood Pressure Measurements and trends for the month. - contextual, environmental, {moves up and down w/ scrolling, meta data230 SYSTOLIC changes depending on what is selected on left} DIASTOLIC social factors 0 Technical challenge to extract relevant features, trends, patterns, anomalies May 1 May 31 Map Traces and stationary times with points of interest highlighted Gym Fast Food Park switch to full screen view map Processed/filtered personal data streams would become part of emerging PHR/EHRs (complementary not duplicative) 5 Monday, July 25, 2011 5
  6. 6. Why focus on open architecture ? broad applicability (diseases, demographics), heterogeneous/‘dual’ use (treatment, engagement, evidence), evolving methodologies, need for innovation ecosystem Stovepipe Architecture Open mHealth Architecture Patient/Caregivers Patient/Caregivers Its not just a mobile app: Analysis/ visualization/ • authoring prompts, feedback Re-usable health triggers A P P L I C A T I O N S data and knowledge services Processing • Individual feedback, Storage Standardized personal data tailoring vaults and health Data transport specific data exchange protocols • analysis and Data capture visualization Mobile platforms Mobile platforms • Personal data vaults iPhone/Android/ iPhone/Android/ Feature Phones Feature Phones 2Monday, July 25, 2011 6
  7. 7. An open modular system is critical to foster rapid and meaningful exploration and innovation developers and data users: mobile app users: clinicians, data analysts, etc. patients and healthy individuals self report data dash- scripting reminders feedback APPLICATION & boards tools & & LAYER automate triggers messaging d measures extract trends, feedback using ANALYSIS anomalies, data and social LAYER correlations media import / export standardized data semantics identity management EHR/PHR, DATA data security, privacy social media SERVICES configuration Personal Data Vault Swiernik, Estrin, Sim, et al 7Monday, July 25, 2011 7
  8. 8. Open architectures enable privacy to be architected as well: Personal Data Vault: allow participants to retain control over their raw data Mobile App Personal Third Party Data Vault Services - Data Capture / Upload - Analytics for Personal (Prompted, Automated) - User Identity and Data Streams Authentication - Reminders - Interface to Clinical Care - Long-term Data Plan, Personnel - Feedback, Incentives Management - Integration with EHR/PHRs - Cross Patient Aggregation Well defined interfaces allow Patient defined selective Well defined interfaces allow mobile functions to be mixed, sharing with Open mHealth analytics functions to be mixed, matched, and shared Server function matched, shared, compared vault + filters = granular, assisted control over what/when you send to whom, what data says about you, whether you reveal who you are or share anonymously, ...Monday, July 25, 2011 8
  9. 9. Closing remarks “If you can’t go to the field with the sensor you want...go with the sensor you have!” “The power of the Internet, the reach of the phone (Voxiva)” Humans are in this loop--so HCI, privacy, visualization, bias, are part of research agenda, and end to end systems that users can exercise are part of the process It takes a healthy research ecosystem to bring information technology innovations to meaningful societal use--Open architectures and platforms are a key building block. 24Monday, July 25, 2011 9
  10. 10. Acknowledgments: Collaborators and Sponsors Collaborators Technology faculty, PIs: Jeff Burke, Deborah Estrin, Mark Hansen, Ramesh Govindan, Martin Lukac, Nithya Ramanathan, Mani Srivastava Application/domain expert faculty/PIs (Health science, Education, Ecology): Jacqueline Casillas, Patricia Ganz, Jeff Goldman, Eric Graham, Jerry Kang, Jenny Kim, Jane Margolis, Maria Teresa Ochoa, Mary Jane Rotheram-Borus, Ida Sim (UCSF), , Dallas Swendeman, Michael Swiernik Students, staff: Staff: Betta Dawson, Mo Monibi, Joshua Selsky, Eric Yuen, Ruth West, Graduate students: Amelia Acker, Faisal Alquaddoomi, Peter Capone-Newton, Patrick Crutcher, Hossein Falaki, Brent Flagstaff, John Hicks, Donnie Kim, Keith Mayoral, Min Mun, Sasank Reddy, Jean Ryoo, Vids Samanta, Katie Shilton, Masanao Yajima, Nathan Yau, Undergraduate students: Jameel Al-Azeez, Joey Degges, Gleb Denisov, Cameron Ketcham, Ashley Jin, Chenyang Xia Sponsors and Partners/Collaborators UCLA centers: CENS, REMAP, Global center for families and children, Health Sciences Federal funding: NSF: NETS-FIND Program, OIA, Ethics, BPC; NIH, NOAA Corporate funding: Google, Intel, MSR, Nokia, T-Mobile, Cisco, Sun (RIP) Foundations/NGOs: The California Endowment, Project Surya, Woodrow Wilson CenterMonday, July 25, 2011 10