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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 Research, NIH/
Program Director, Smart and Connected Health, Directorate for Computer & Information Systems, NSF
3rd Annual Telehealth Summit of South CarolinaSeptmeber 25, 2014
Leveraging the Ubiquity of Wireless
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
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
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
5Do it right or lose them
Moving “Hype” to Productivity
Clinic-based EHR Data
Valid, Sporadic
Patient-basedHealth Data
Novel, Dense Data
Information Exchange
MedicalTeam
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
Continuum of mHealth tools
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
• 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
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
Analysis of breathing with the wireless capnograph
Informationsent by individual ornurses to health careprofessional
Information and pulmonary patterns evaluated
HyperventilationHyperventilation
Cardiac Output /Cardiac Arrest
Normal capnograph
Asthma/COPD capnograph
EmphysemaHypoventilation
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
Molecular Analysis of CellsProblem: 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
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
Longitudinal pattern recognition
Subject Center
CellCellPhone or Phone or
Computer ConnectionComputer 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 dataSolution: 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
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
Real time alerts via SMS
Communication back to the field via cell phones
Urban and rural areas
Adverse Event MonitoringProblem: 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
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.
Mihail Popescu University of Missouri
NSF Grant #IIS-1115956
Predictive health assessment frameworkProblem: 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
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
Privacy Security
Breach Notification:500+ Breaches by Type of Breach
Breach Notification:500+ Breaches by Location of Breach
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
Join our Listserv• [email protected]
Join the electronic mailing list (LISTSERV) for forthcoming announcements by — Sending an e-mail message to [email protected] 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.
Thank you!
▫Wendy Nilsen, NIH Office of Behavioral and Social Sciences Research 301-496-0979 [email protected]
▫Wendy Nilsen, NSF Smart and Connected Health 703-292-2568 [email protected]
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