mHealth into the 21st Century: The Progress and Challenges

1. 3rd Annual Telehealth Summit of South Carolina Septmeber 25, 2014 mHealth into the 21st Century: The Progress and Challenges Wendy J. Nilsen, PhD Health Scientist Administrator Office of Behavioral and Social Sciences Research, NIH/ Program Director, Smart and Connected Health, Directorate for Computer & Information Systems, NSF

2. Leveraging the Ubiquity of Wireless

3. Includes any wireless device carried by or on the person that is accepting or transmitting health data/information • Sensors (e.g., implantable miniature sensors and “nanosensors”) • Monitors (e.g., wireless accelerometers, blood pressure & glucose monitors) • Mobile phones

4. Beyond Telemedicine • Portable: Beyond POC Diagnostics • Scalable: Economical to scale • Richer data input: Continuous data sampling • Personal: Patient can receive & input information • Real-time: Data collection and feedback is in real-time using automated analyses and responses

5. Do it right or lose them I think we can safely assume the promise of apps radically revolutionizing our health is heavily inflated. So, then, what good are health apps? Health apps are the equivalent of old school public health advertising. Just as I see an ad when I get on the subway telling me this soft drink has 40 packets of sugar, I whip out my iPhone and see the Livestrong app on my homescreen reminding me that I need to eat well. I don’t really want to use it because it’s such a drag.” Jay Parkinson of Future Well, 2011

6. Moving “Hype” to Productivity

7. Subjective • Concerns • Patient Reported Outcomes mHealth and Connected Health: People, Technology, Process Clinic-based EHR Data Valid, Sporadic Patient-based Health Data Novel, Dense Data Objective • Clinical measures • Laboratory findings • Sensor data Information Exchange Assessment • Diagnosis • Categorical reporting • Prognosis/Trajectory Plan • Treatment planning • Self-care planning • Post treatment • Surveillance Patient & Family Medical Team Hospital System Medical Researcher • Risk modeling • Diagnostic support • Treatment selection • Guideline adherence • Error detection/correction • Situational awareness • Population health • Continuity of care • Identify side effects • Inform discovery Outcomes

8. Continuum of mHealth tools

10. Implantable Biosensors • Problem: Measurement of analytes (glucose, lactate O2 and CO2) that indicate metabolic abnormalities • Solution: Miniaturized wireless implantable biosensor that continuously monitors metabolism for 30 days Diane J. Burgess, University of Connecticut NHLBI, R21HL090458

11. Stress Hormone Detection • Problem: Detection of salivary stress hormones in real-time is expensive and not practical in clinical settings • Solution: Develop wireless salivary biosensors • Salivary α-amylase biosensor • Salivary cortisol biosensor Vivek Shetty, DDS, UCLA, NIDA U01DA023815

12. Adherence Monitoring Problem: Adherence to chronic disease medications is poor. In resource-poor settings, getting people medication is only part of the solution Solution: Wireless medication canisters that signal medication timing, transmit adherence data and allow resources to target the non-compliant Jessica Haberer, Partners Healthcare NIMH K23MH087228

14. Pulmonary Function: Wireless Capnograph Problem: Conventional capnography is hard to do outside of clinical settings Solution: to develop & validate a new wireless capnograph for home-based or mobile use by patients under oxygen therapy Analysis of breathing with the wireless capnograph Information displayed and saved in a user-friendly interface Information and pulmonary patterns evaluated Information sent by individual or nurses to health care professional Feedback provided by health care professional Normal capnograph Hypoventilation Hyperventilation Asthma/COPD capnograph Cardiac Output / Cardiac Arrest Emphysema Hyperventilation CO2 Erica Forzani, Arizona State University

15. Molecular Analysis of Cells Problem:Detection a variety of biologics rapidly and without a laboratory. Solution: A chip based micro NMR unit Smartphone powered analysis: Ca Protein bio-markers, DNA, bacteria and virus drugs Ralph Weissleder, MIT, NIBIB RO1 EB004626

17. Chronic Disease Management • Problem:Chronic diseases are difficult and expensive to manage within traditional healthcare settings • Solution: Disease self-management programs for asthma, alcohol dependence and lung cancer • Information provided the user needs it • Intervene remotely with greater frequency than traditional care David Gustafson, University of Wisconsin, NIAAA R01 AA 017192-04

18. Healthcare professional Cardiac Disease Management Problem:Patients with CVD have symptoms that frequently bring them to emergency care where there is limited baseline data Solution: Remote monitoring to create physiological cardiac activity “fingerprints” that alert professionals and patient when there are irregularities based on their own cardiac patterns Center Subject Longitudinal pattern recognition Cell Phone or Computer Connection Subject Adapting parameters Adapting parameters Vladimir Shusterman, PinMed, NHLBI, R43-44 HL0771160, R41HL093953

19. Adverse Event Monitoring Problem: Following at-risk patients for adverse events in low- to medium resource countries is expensive/impractical Solution: Wireless adverse events reporting and database improves patient and community care Queries on demand via Internet Real time data via IVR on cell phones Real time alerts via E-mail Secure database Urban and rural areas Real time alerts via SMS Communication back to the field via cell phones Walter Curiso, MD, University of Peruana FIC R01TW007896

20. Wireless Neurosensing Diagnostic System Problem: Brain implant technology holds enormous potential for physical control restoration and early seizure detection, among others. However, implants are currently restrained by two safety concerns: (a) wired connections to/from the implant, and (b) heat generation. Solution: Tiny fully-passive implants (no battery, no rectifiers, no energy harvesting units), capable of wireless and inconspicuous acquisition of brain signals. PIs: Junseok Chae (Arizona State University), John L. Volakis (The Ohio State University) NSF Grant #1344825

21. Predictive health assessment framework Problem: Identifying relatively rare events based on sparse data or data that arrives after it is useful for adverse events in low- to medium resource countries is expensive/impractical Solution: Sensors and machine learning technologies enable a proactive, timely, person-centered approach to healthcare Mihail Popescu University of Missouri NSF Grant #IIS-1115956

22. Fear about our data • Consumer digital technologies have altered expectations about what will be private • And shifted our thinking about what should be private. • Which data is actually sensitive? • Trade-off between privacy and health care innovation

23. Privacy  Security • Privacy = keeping personal health info from “improper disclosure” • Security = collection of technical and procedural mechanisms in place to protect privacy of health info. Good security should result in privacy • Threats to privacy mostly related to policies that encourage or do not forbid sharing of info. NOT to inadequate security. • Is mobile information EXTRA vulnerable?

24. Breach Notification:500+ Breaches by Type of Breach

25. Breach Notification:500+ Breaches by Location of Breach

26. mHealth: What are the tradeoffs? And why is it worth it? • Digital technologies offer chances to make major advances in health care, prevention and treatment • Precisely because we CAN know so much, and because we can link data to time, event, and context • Real- (or near-) time monitoring and feedback

27. Join our Listserv • mHealth-Training@list.nih.gov Join the electronic mailing list (LISTSERV) for forthcoming announcements by — Sending an e-mail message to listserv@list.nih.gov from the mailing address at which you want to receive announcements. The body of the message should read SUBscribe mHealth-Training [your full name]. The message is case sensitive; so capitalize as indicated! • Don't include the brackets. • The Subject line should be blank • For example, for Robin Smith to subscribe, the message would read • SUBscribe mHealth-Training Robin Smith. You will receive a confirmation of your subscription along with instructions on using the listserv.

28. Thank you! • Wendy Nilsen, NIH Office of Behavioral and Social Sciences Research • 301-496-0979 • nilsenwj@od.nih.gov • Wendy Nilsen, NSF Smart and Connected Health • 703-292-2568 • wnilsen@nsf.gov