Transcript What is mHealth? - Palmetto Care Connections

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
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 realtime using automated analyses and responses
Do it right or lose them
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
Moving “Hype” to Productivity
mHealth and Connected Health:
People, Technology, Process
Subjective
• Concerns
• Patient Reported Outcomes
Objective
• Clinical measures
• Laboratory findings
• Sensor data
Assessment
• Diagnosis
• Categorical reporting
• Prognosis/Trajectory
Plan
• Treatment planning
• Self-care planning
• Post treatment
• Surveillance
Outcomes
Information Exchange
Clinic-based
EHR Data
Valid, Sporadic
Medical
Team
Hospital
System
• Risk modeling
• Diagnostic support
• Treatment selection
• Guideline adherence
• Error detection/correction
Patient-based
Health Data
Novel, Dense Data
Medical
Researcher
Patient
&
Family
• Situational awareness
• Population health
• Continuity of care
• Identify side effects
• Inform discovery
Continuum of mHealth tools
Global
Treatment
Diagnostic
Measurement
• Sensor sampling in
real time
• Integration with
health data
• POC Diagnostics
• Portable imaging
• Biomarker sensing
• Clinical decision
making
• Chronic disease
management
• Remote Clinical
trials
• Disaster
support/care
• Service Access
• Remote
treatment
• Dissemination of
health
information
• Disease
surveillance
• Medication
tracking and
safety
• Prevention and
wellness
interventions
Measurement
and Assessment
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
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
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
Diagnostics
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 userfriendly interface
Information and pulmonary
patterns evaluated
Information
sent by
individual or
nurses to
health care
professional
Feedback provided by health care
professional
CO2
Normal
capnograph
Asthma/COPD
capnograph
Hyperventilation
Hypoventilation Hyperventilation Cardiac Output / Emphysema
Cardiac Arrest
Erica Forzani, Arizona State University
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
Treatment
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
than traditional care
David Gustafson, University of Wisconsin, NIAAA R01 AA 017192-04
frequency
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
Healthcare
professional
Center
Subject
Subject
Cell
Phone or
Computer Connection
Longitudinal pattern
recognition
Adapting
Adapting
parameters
parameters
Vladimir Shusterman, PinMed, NHLBI, R43-44 HL0771160, R41HL093953
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
Urban and rural areas
Secure
database
Real time
alerts via
E-mail
Real time alerts via SMS
Walter Curiso, MD, University of Peruana
FIC R01TW007896
Communication back to the field via cell
phones
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
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
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
• 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?
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
 [email protected]
▫ Wendy Nilsen, NSF Smart and Connected Health
 703-292-2568
 [email protected]