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

The Potential

•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.

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

• Dissemination of health information

• Chronic disease management

• Service Access

• Remote treatment

• Disease surveillance

• Prevention and wellness interventions

• Remote Clinical trials

Global

• 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.

Clinic-based

EHR Data

Valid, Sporadic

Patient-based

Health Data

Novel, Dense Data

Information Exchange

Medical Team

Patient &

Family

Hospital

System

Outcomes

Patient Generated

• Concerns

• Patient Reported Outcomes

• Sensor data

• 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

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

mHealth and Connected Health:

People, Technology, Process

Wearable Chemical Sensor System

• 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

http://www.airnow.gov

LUCAS microscope

Computer software automatically interprets images at remote site

A. OZCAN, 1R21EB009222-01

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 •Respiratory rate (RR) counter •Pulse oximeter (Pox)

Field testing: •Verifying outcomes, as well as assessing user interface design, navigation, workflow verification, accessibility testing and provider and patient perceptions regarding feasibility, acceptability and usability

ECG/ACC

ACC

Structure of Data Collecting Software

End-to-end

Encryption of

Sensitive Data

Device Manager

Local

Storage [User Configuration]

[Analyzed Data]

[Raw Data]

Transmitter [Encrypt/Decrypt]

Analyzer [Plug-in

modules]

GPS ACC ECG

Data Collector

Service Manager

Client Application with GUI

Local Socket or IPC

•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

Donna Spruijt-Metz, PHD, USC, NSF

Body Sensor Networks

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

Analysis of breathing

with the wireless

capnograph Information

sent by

individual or

nurses to

health care

professional

Information and pulmonary

patterns evaluated

Hyperventilation 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

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

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

Of Peru

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

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