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MHealth into the 21st Century: The Progress and Challenges Wendy J. Nilsen, PhD Health Scientist Administrator Office of Behavioral and Social Sciences.

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Presentation on theme: "MHealth into the 21st Century: The Progress and Challenges Wendy J. Nilsen, PhD Health Scientist Administrator Office of Behavioral and Social Sciences."— Presentation transcript:

1 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 3 rd Annual Telehealth Summit of South Carolina Septmeber 25, 2014

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 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 5 Do it right or lose them

6 Moving “Hype” to Productivity

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

8 Continuum of mHealth tools Measurement Sensor sampling in real time Integration with health data Diagnostic POC Diagnostics Portable imaging Biomarker sensing Clinical decision making Treatment Chronic disease management Remote Clinical trials Disaster support/care Global Service Access Remote treatment Dissemination of health information Disease surveillance Medication tracking and safety Prevention and wellness interventions

9 Measurement and Assessment

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 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 Stress Hormone Detection Vivek Shetty, DDS, UCLA, NIDA U01DA023815

12 Adherence Monitoring Jessica Haberer, Partners Healthcare NIMH K23MH087228 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

13 Diagnostics

14 Analysis of breathing with the wireless capnograph Information sent by individual or nurses to health care professional Information and pulmonary patterns evaluated Hyperventilation Cardiac Output / Cardiac Arrest Normal capnograph Asthma/COPD capnograph Emphysema Hypoventilation CO 2 Information displayed and saved in a user- friendly interface Pulmonary Function: Wireless Capnograph Feedback provided by health care professional Erica Forzani, Arizona State University 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

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

16 Treatment

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 Longitudinal pattern recognition Subject Center Cell Phone or Computer Connection Adapting parameters Subject Healthcare professional Adapting parameters 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 Vladimir Shusterman, PinMed, NHLBI, R43-44 HL0771160, R41HL093953 Cardiac Disease Management

19 Walter Curiso, MD, University of Peruana FIC R01TW007896 Real time data via IVR on cell phones Secure database Queries on demand via Internet Real time alerts via E-mail Real time alerts via SMS Communication back to the field via cell phones Urban and rural areas 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

20 Wireless Neurosensing Diagnostic System PIs: Junseok Chae (Arizona State University), John L. Volakis (The Ohio State University) NSF Grant #1344825 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.

21 Mihail Popescu University of Missouri NSF Grant # IIS-1115956 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

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

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 Join the electronic mailing list (LISTSERV) for forthcoming announcements by — Sending an e-mail message to 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  ▫ Wendy Nilsen, NSF Smart and Connected Health  703-292-2568  28

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