NIH: Technologies to Realize the Promise
of Reducing Health Disparities
Wendy Nilsen, PhD
NIH Office of Behavioral & Social Sciences Research
NSF Smart and Connected Health (CISE)
Digital Divide
mHealth 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
•mHealth technologies can
expand health into the real
world.
•Generate user-friendly tools
for enhancing health.
•Change the questions we
ask.
•Scale to entire populations
• Facilitate more efficient and
representative clinical trials.
The Potential
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
• Dissemination of
health information
• Chronic disease
management
• Service Access
• Remote treatment
• Disease
surveillance
• Prevention and
wellness
interventions
• Remote Clinical
trials
• Service Access
• Remote
treatment
• Dissemination of
health
information
• Disease
surveillance
• Medication
tracking and
safety
• Disaster
support/care
• Prevention and
wellness
interventions
Rationale for Reducing Disparities
•
•
•
•
•
•
•
Demographics
Intimacy/Customizability
Consumer Technology and New Expertise
Flexibility/Real time
Centralization of communication
Reducing the burden of data transmission
Representativeness in clinical research
Who uses Mobile?
EVERYONE
http://www.pewinternet.org/
Seniors and Cell Phone Adoption
http://www.pewinternet.org/2014/04/03/older-adults-and-technology-use/
Customizability/Intimacy
My language, my apps right from the start.
Consumer Technology and New
Expertise
Consumer technology
provides opportunities
for engagement that
rival unhealthy
competition
Can’t health be
enjoyable or desired?
Flexibility/Real time
• Flexibility of delivery:
▫ On my schedule
▫ When I want it
• Real time information
▫ Support/information when and
where they are needed
▫ Information/Support that develops
with my needs
• Integrated into my life
Centralization of communication
• Mobile devices can be a health “hub”
• Communication with care team
▫ Photos
▫ To ask or do lists
▫ Messaging
• Interventions and information programs
▫ Along side of other self-tracked information
Reducing the Burden of Data
Representativeness of Clinical
Research
Green LA, Miller RS, Reed FM, Iverson DC, Barley GE. How
Representative of Typical Practice are Practice-Based Research
Networks? Arch Fam Med, 1993; 2:939-949.
mHealth and Connected Health:
People, Technology, Process
Patient Generated
• Concerns
• Patient Reported Outcomes
• Sensor data
Clinic generated
• Clinical measures
• Laboratory findings
• Sensor data
Assessment
• Diagnosis
• Categorical reporting
• Prognosis/Trajectory
Plan
• Treatment planning
• Self-care planning
• Care coordination
• 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
Wearable Chemical Sensor System
http://www.airnow.gov
• Problem: Chemical exposure varies by
context, need personal exposure
• Solution: Selective detection of VOCs
(hydrocarbon and acid vapors)





Sensitive: ppb – ppm
Real-time: sec. – min.
Spatially resolved
Wearable: cell phone size
Cell phone based interface
Nongjian Tao, Arizona State University,
NIEHS, U01 ES016064
LUCAS
microscope
Childhood Pneumonia
Problem: Children die of pneumonia around the world because of lack of professionals
to accurately diagnosis and treat
Solution: mPneumonia: A suite of tools designed for hospital-based clinicians in India
including a smart phone or tablet with a:
•Integrated Management of Neonatal and Childhood Illnesses (IMNCI) algorithm
A. OZCAN, 1R21EB009222-01
•Respiratory rate (RR) counter
•Pulse oximeter (Pox)
Field testing:
• Verifying outcomes, as well as assessing
user interface design, navigation, workflow
Computer software
verification, accessibility testing and
automatically
provider and patient perceptions regarding
interprets images at
feasibility, acceptability and usability
remote site
Body Sensor Networks
•Problem: Overweight and Obesity among urban, minority youth
•Solution: KNOWME networks personalized tracking & feedback in Real-Time
 Immediate access to data allows nimble reactions to events, environments,
& behavior
 User interface for health professionals, children & families
 User initiated data (SMS, speech notes, images/videos)
 Real-time, personalized, adaptive interventions to correct energy balance
End-to-end
Encryption of
Sensitive
DataApplication with GUI
Client
ECG/ACC
Local Socket or IPC
ACC
Analyzer
[Plug-in
modules]
Service Manager
Local
Storage
Transmitter
[User Configuration]
[Analyzed Data]
[Raw Data]
[Encrypt/Decrypt]
Data Collector
Donna Spruijt-Metz, PHD,
USC, NSF
Device Manager
GPS
ACC
ECG
Structure of Data Collecting Software
Chronic Disease Management
• Problem: Chronic diseases are difficult and expensive
to manage within traditional healthcare settings
• Solution: CHESS: 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
▫ Real-time management
▫ More efficient triage
▫ Reduces acute care
David Gustafson, University of Wisconsin, NIAAA R01 AA 017192-04
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
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
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
Of Peru
Real time alerts via SMS
Walter Curiso, MD, University of Peruana
FIC R01TW007896
Communication back to the field via cell
phones
For more information contact:
• Wendy Nilsen, PhD
▫
▫
▫
▫
▫
▫
Office of Behavioral and Social Sciences Research
Nilsenwj@od.nih.gov
301-496-0979
Smart and Connected Health, NSF
wnilsen@nsf.gov
703-292-2568