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

E-mail

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

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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

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