What are scalable best practices to spread smart health?

What are scalable best practices to
spread smart health?

Chaired by: Jayne Plunkett,
Head of Casualty Reinsurance at Swiss Re,
YGL Class of 2010
Misha Pavel,
Program Director of Smart and
Connected Health at the NSF
Dharma Singh Khalsa,
President of the Alzheimer’s Research
and Prevention Foundation
Josh Wright,
Managing Director of ideas42
What are scalable best practices to spread
smart health?

Misha Pavel,
Program Director of Smart and
Connected Health at the
National Science Foundation
smart health?

4
Smart and Connected Health
Misha Pavel
College of Computer and Information Science
Bouvé College of Health Sciences
Northeastern University
&
Computer & Information Science & Engineering Directorate
Information and Intelligent Systems Division
Any opinion, finding, and conclusions or recommendations expressed in this
material; are those of the author and do not necessarily reflect the views of
the National Science Foundation

Road ahead
I. Healthcare in Crisis
II. Smart & Connected Health
III. Behaviors including Big Data
5
Wactlar H., Pavel M., and Barkis W., "Can Computer Science Save Healthcare?,"
Intelligent Systems, IEEE, vol. 26, pp. 79-83, Sept. 2011.

PART I:
Healthcare in Crisis
Advances in Technology
6

The healthcare crisis – Some troubling statistics
• The cost of healthcare in the U.S. is the highest in the world
(> $8,000 per capita, 16% GDP)
• The U.S. ranked 37th in the 2000 WHO study of healthcare
system performance (8 underlying measures)
• 98,000 deaths per year due to medical errors
• Current individual medical records have an error rate of 20%
• 50% Americans have 1 or more chronic diseases; age of
onset is getting younger
• Medicare and Medicaid costs to be at a staggering 25% of
the U.S. economy by 2050
• 3 lifestyle behaviors (poor diet, lack of exercise, smoking)
cause estimated 1/3rd of U.S. deaths
7

Dependency Ratio: Retired/Working
1950 1960 1970 1980 1990 2000 2010 2020 2030 2040 2050
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
Dependency
Ratio
[Over
64]
/[20-65]
Year
Estimates Projections
Silver Tsunami

A sample of recent strategic visions & activities
Focus on transforming healthcare with technology and innovation

From traditional medicine to smart health
NSF Perspective
EPISODIC, REACTIVE
FOCUS ON DISEASE
PROACTIVE and PREVENTIVE
FOCUS ON WELLBEING
QUALITY OF LIFE
HOSPITAL-CENTRIC PATIENT-CENTRIC, HOME-BASED
FRAGMENTED, LOCAL DATA
INTEROPERABLE, EHR AVAILBLE
ANYWHERE, ANYTIME
NAÏVE,PASSIVE, PATIENTS
EMPOWERED, ENAGAGED,
INFORMED, PARTICIPATING
TRAINING & EXPERIENCE
BASED
MORE EVIDENCE – BASED
DECISION SUPPORT

Quality of Life over Life-Span
0 20 40 60 80 100 120
Age [Years]
Quality
of
Life
Rectangularization
after Fries, 1983
11

Source: Sajal Das, Keith Marzullo
Person
al
Sensing
Public
Sensing
Social
Sensing
People-Centric Sensing
Actions
(controllers)
Percepts
(sensors)
Agent
(Reasoning)
Smart Health
Situation
Awareness:
Humans as
sensors
feed multi-
modal data
streams
Pervasive Computing
Social Informatics
Sense
Identify
Assess
Intervene
Evaluate
Emergency Response
Environment Sensing
The Age of Observation – Smart Sensing,
Reasoning and Decision: BIG DATA

PART II:
Smart & Connected Health (SCH)
Inter-Agency Program
National Institutes of Health
13
NSF Solicitation: NSF-13-543
NIH Notice Number: NOT-OD-13-041

Objectives of the Smart and Connected Health Program
• To fill in research gaps that exist in science and
technology in support of health and wellness
• To advance the fields of health, wellness,
improve quality of care and reduce cost by
leveraging the fundamental science research
Seek improvements in safe, effective, efficient, equitable, and
patient-centered health and wellness services through
innovations in computer and information science, engineering,
social, behavioral and economic science and medical science

NSF Directorates Participating in SCH
15
Office of the
Director
Engineering (ENG)
Geosciences (GEO)
Mathematical and Physical Sciences (MPS)
Budget, Finance
Award
Management
Computer & Information Science and Engineering
(CISE)
Biological Sciences (BIO)
Diversity and
Inclusion
Social, Behavioral and Economic Sciences (EBS)
Education and Human Resources (EHR)
General
Counsel
Information &
Resource
Management
Legislative &
Public Affairs
National
Science Board
Office of
Inspector
General
Cyber-
infrastructure
Integrative
Activities
International
Science and
Engineering
Polar programs

NIH Institutes Officially Participating in SCH
OBSSR
NCI
NIBIB
NIA
NHGRI
NICHD
National Human
Genome Research
Institute

Family
Caregiver
Coach
Clinician
Devices
User
Interfaces
Inference
Assessment
Patient-centered framework for health, wellness and
precision medicine (including behavioral assessment)
Payers Employers Legal
Environment Privacy
Self-care
Patient
Physical Function
Cognitive Function
Chronic Disease
Socialization
Physio Sensors
Activity Sensors
Mobile Sensors
EHR, PHR
Mobile Health
NIT: Networks, DB, API Software, EHR, PHR

ECG
EEG
Pulmonary
Function
Gait
Balance
Step Size
Blood
Pressure
SpO2
Posture
Step
Height
GPS
Performance
Early Detection
Prediction
Inference
Datamining
Training
Health Information
Coaching
Chronic Care
Social Networks
Decision Support
Population
Statistics
Epidemiology
Evidence
Mobile Health
18
Training
Health Information
Coaching
Chronic Care
Social Networks
Wactlar H., Pavel M., and Barkis W., "Can Computer Science Save
Healthcare?," Intelligent Systems, IEEE, vol. 26, pp. 79-83, Sept. 2011.

Smart and Connected Health Research Areas
• Integration of EHR, pharma and clinical data
• Access to information, data harmonization
• Semantic representation, fusion,
Digital Health
Information
Infrastructure
Informatics and
Infrastructure
• Datamining and machine learning
• Inference, cognitive decision support system
• Bring raw image data to clinical practice
Data to Knowledge
to Decision
Reasoning under
uncertainty
• Systems for empowering patient
• Models of readiness to change
• State assessment from images video
Empowered
Individuals
Energized, enabled,
educated
• Assistive technologies embodying computational
intelligence
• Medical devices, co-robots, cognitive orthotics, rehab
coaches
Sensors, Devices, and
Robotics
Sensor-based
actuation

Challenge: Extraction of Knowledge and Meaning
Harmonizing/Coherence: Source-Invariant Decisions
NIT (ICT) Network Layer, Databases, EHR, PHR, XHR
Decisions
Transform
Decisions Decisions
Transform Transform
Transform
Heterogeneous Sources/Sensors
Adaptation, Calibration & Fusion
Transform
Transform
Transform

PART III
Focus on Behaviors
Big Data
Any opinion, finding, and conclusions or recommendations expressed in
this material are those of the author and do not necessarily reflect the
views of the National Science Foundation
21

Causes of Premature Mortality
22
30%
5%
15%
40%
10%
Behavioral
Social
Circumstances
Environmental
Exposure
Genetic
Medical Care
Deficiency
McGinnis JM, Russo PG, Knickman, JR. Health Affairs, April 2002.

Changing habits and
lifestyle is difficult
23

ECG
EEG
Pulmonary
Function
Gait
Balance
Step Size
Blood
Pressure
SpO2
Posture
Step
Height
GPS
Performance
Early Detection
Prediction
Inference
Datamining
Training
Health Information
Coaching
Chronic Care
Social Networks
Decision Support
Population
Statistics
Epidemiology
Evidence
Mobile Health
24
Training
Health Information
Coaching
Chronic Care
Social Networks
Wactlar H., Pavel M., and Barkis W., "Can Computer Science Save
Healthcare?," Intelligent Systems, IEEE, vol. 26, pp. 79-83, Sept. 2011.

Examples from Oregon Center for Aging
and Technology (ORCATECH)
25
Home Health

Hayes, ORCATECH 2007
26
Bedroom
Bathroom
Living Room
Front Door
Kitchen
Sensor Events
Private Home
BIG BEHAVIORAL DATA

Challenges for closing the loop
Continuous, Unobtrusive Monitoring of Activities
Physiology and Genomic
BIG DATA
Computational Predictive Models
Phenotyping
Including Behavioral (Behavioral Markers)
Prevention, Early Detection, Rehabilitation, Maintenance,

Monitoring and assessment of gait
28
• Unobtrusive assessment of everyday speed of walking
• Modeling sensors and human gait
Daniel Austin
Stuart Hagler

Example: Relating Speed of Walking to Cognitive Function
06/07 11/08 03/10
40
50
60
70
80
90
100
110
120
Time
Evolution of the gait velocity PDF for home 196 (dir=0).
Velocity
(cm/s)
0.005
0.01
0.015
0.02
0.025
0.03
0.035
0.04
CDR = 0.5
29
Daniel Austin, OHSU

Gait
Sensors
Multiscale Modeling: From sensors to brain function
should include behavioral and cognitive factors
• Unobtrusive
measurement of gait
characteristics
• Model relationship
between the sensory
inputs and gait
characteristics
• Infer sensory-motor,
perceptual and
cognitive functions
Cognition
Perception
Sensory
Motor
Inference
of Gait Parameters
Cognition
Perception
Sensory
Motor
Inference
of Brain Function
30

Example: Monitoring Sleep with load cells under the
bedposts
31

Sleep and Physiological Measurements using
Load Cells Technology
• Strain gauge transducers
• Monitoring quality of sleep
• Monitoring sleep hygiene
• Monitoring weight
21:00 00:00 03:00 06:00
Time (hour)
Total
Force
(N)

Apnea and Movement Detection
33
5. Z. Beattie, C. Hagen, M. Pavel, and T. Hayes, “Unobtrusive Monitoring of Sleep Apnea," SLEEP
2011 Abstract 25th Anniversary Meeting of the Associated Professional Sleep Societies, LLC,
Minneapolis, Minnesota, Jun 11 – Jun 15, 2011.

Cognitive Assessment with Computer Interactions
Example: Computer games
(with embedded inference algorithms)

Example: Working Memory
35
Design Objectives
• Address key
cognitive functions
• Self-motivating
• Incorporate a
model of
underlying
memory processes

Memory Model: Survival Analysis
36
0 5 10 15
0
0.5
1
Subject 1020, N = 8687
Probability
of
Correct
Intervening Number of Events
0 5 10 15 20 25
0
0.5
1
Probability
of
Correct
Intervening Time [sec]
   
1
b
t
a
M t F t e
 
 
 
  

Collaborators and Support Teams
OHSU Team UCB Team
• Holly Jimison
• Tamara Hayes
• Jeff Kaye
• Jennifer Marcoe
• Krystal Klein, Post-doc
• Stuart Hagler,
• Daniel Austin
• Zephy McKanna
• Steve Williamson
• Tracy Zitleberger
• Nicole Larimer
• Don Young
• Yves Vimegnon,
• Jon Yeargers
• Devin Williams
• Ruzena Bajcsy, PI
• Edmund Seto, Co-I
• Gregorij Kurillo, Senior
Researcher
• Ferda Olfi, PhD Student
• Štěpán Obdržálek, Post-doc
37
3/18/2022
Oregon Research Center for
Aging and Technology

Take Home Messages
38
I. Healthcare is in crisis
II. Smart & Connected Health is focused on
developing technology-based solutions that can
help making healthcare preventive and focused on
quality of life
III. Focus on behaviors using monitoring as well as
cognitive and physical exercise can have a huge
impact on improving quality of life
IV. Analytics and computational modeling will play an
essential component of the emerging technology

• Copyrighted material used under Fair Use. If you are the copyright
holder and believe your material has been used unfairly, or if you have
any suggestions, feedback, or support, please contact:
ciseitsupport@nsf.gov
• Except where otherwise indicated, permission is granted to copy,
distribute, and/or modify all images in this document under the terms of
the GNU Free Documentation license, Version 1.2 or any later version
published by the Free Software Foundation; with no Invariant Sections,
no Front-Cover Texts, and no Back-Cover Texts. A copy of the license
is included in the section entitled “GNU Free Documentation license”
(http://commons.wikimedia.org/wiki/Commons:GNU_Free_Documentati
on_License)
• The inclusion of a logo does not express or imply the endorsement by
NSF of the entities' products, services or enterprises.
Credits
40

Q&A
10 minutes
(Included in session
recording)

Dharma Singh Khalsa,
President of the Alzheimer’s
Research and Prevention
Foundation
smart health?

Alzheimer’s Prevention 2013:
From Drugs to Lifestyle
Dharma Singh Khalsa, M.D.
President/Medical Director
Alzheimer’s Research and Prevention Foundation
www.alzheimersprevention.org

5.4
million people
have
Alzheimer’s
>$150
billion dollars
in annual costs
>10
million unpaid
caregivers
6th
leading cause
of death
#1
worry
a new case
every
6833
seconds
2013
Alzheimer’s Disease
Facts and Figures

Maintain a sharp brain with age
Boomers’ #1 Fear
Getting Alzheimer’s
Boomers’ #1 Goal

FOR IMMEDIATE RELEASE
May 15, 2012
Contact: HHS Press Office
(202) 690-6343
News Release
Obama administration presents national plan to fight
Alzheimer’s disease (NAPA)
HHS Secretary Sebelius outlines research funding, tools for health
care providers, awareness campaign and new website
Health and Human Services Secretary Kathleen Sebelius today
released an ambitious national plan to fight Alzheimer’s disease. The
plan was called for in the National Alzheimer’s Project Act (NAPA),
which President Obama signed into law in January 2011. The National
Plan to Address Alzheimer’s Disease sets forth five goals, including
the development of effective prevention and treatment
approaches for Alzheimer’s disease and related dementias by
2025.

Until 2012, Lifestyle Included in
National Discussion
2013: Drugs and Genetics
We need to re-introduce
lifestyle into the
conversation

The Four Pillars of
Alzheimer’s Prevention
1. Diet and Brain Specific Nutrients
2. Stress Management
3. Exercise
4. Spiritual Wellbeing

Why Yoga/Meditation Should be
Part of the Conversation
1. Stress is a risk factor for memory loss
2. Meditation lowers stress and improves brain function
3. KK is faster and easier and memory specific compared to other
meditation techniques:
a. The Relaxation Response – 20 min/ twice a day
b. TM – 20 min/twice a day
c. Mindfulness – 47 min average
d. Kirtan Kriya (KK) – 12 min

The Benefits of KK in 12 Min/Day
1. Can be easily learned and practiced at home
with a CD
2.Strengthens the brain, like going to the gym
strengthens the body
3.Improves attention, concentration, focus and
memory
4. Better mood, less depression and anxiety
5. More mental and physical energy
6. Enhanced Genetic Health
Demonstration on Sept 27th

1. FINGER Study
2. UCLA- KK & MCI
3. UWVA-KK & MCI
Ongoing ARPF Research
4. UofAz- KK + virtual
balance training vs bt,
associated with cog
dysfunction

And In The End…
Spirit and Wisdom
To Be Continued on Sept
27th….

Josh Wright,
Managing Director of ideas42
smart health?

Using Behavioral Economics
To Improve Health
2013 SmartBrains Summit
September 20th – 8:30-10:30 What are scalable best practices to spread smart health?
Josh Wright
josh@ideas42.org

WHAT IS IDEAS42?
©2013
ideas42 57

58
FOUNDED BY VISIONARY ACADEMICS…
©2013
ideas42
Sendhil Mullainathan,
Harvard University
Antoinette Schoar, MIT
Sloan
Eldar Shafir, Princeton
University

59
…A BEHAVIORAL IDEAS LAB AND CONSULTING
FIRM (501c3) WITH AN AMBITIOUS GOAL
©2013
ideas42 59
Academics Theory
Amazing understanding
about human behavior
from Behavioral
Psychology &
Behavioral Economics
academic research
Need
To
Apply
Real
World
To Solve Hard Problems in:
-Consumer Finance
-Economic Opportunity
-Health
-Education
-Energy Consumption
To Help
Millions
of
People

REPRESENTATION LEADS TO SOLUTION 63
©2012 ideas42

BEHAVIORAL MODEL
A
B
Decision Actions
Yes
No
Outcome
Yes
No
• We decide yes if benefits > costs
• Action naturally follows from
decision
©2012 ideas42
68

69
A
B
Decision Actions Outcome
Failed to choose, didn’t
consider at all
???
Ye
s
No
Process
changes
decision
Yes
No
???
Yes
No
???
Yes
No
BEHAVIORAL MODEL
©2013 ideas42

401(k) PSYCHOLOGY
Out of
every 100
surveyed
employees
68 self-report
saving too little 24 plan to
raise
savings rate
in next 2
months
3 actually follow through over the next four months
©2012 ideas42
75

CAUTION: DIAGNOSIS SHOULD DRIVE DESIGN
• Defaults work in increasing 401(k) savings
• Can we apply to savings in general?
• Field experiment tested with low-income
population receiving EITC.
• Defaulted to placing 10% into savings bonds.
©2012 ideas42
77

• Defaults work in increasing 401(k) savings
• Can we apply to savings in general?
• Field experiment tested with low-income
population receiving EITC.
• Defaulted to placing 10% into savings bonds.
0%
20%
40%
60%
80%
100%
Opt In Opt Out
78
©2012 ideas42
CAUTION: DIAGNOSIS SHOULD DRIVE DESIGN

EITC PSYCHOLOGY
0%
25%
50%
75%
100%
Did not trust the
government
Did not feel
comfortable
buying bonds
Did not like
bonds because
wanted more
liquidity
Did not have a
baseline to
compare to
bonds interest
rate
Had specific plan
for how they
were going to
spend refund
©2012 ideas42
79

PROJECT EXAMPLE – OREGON MEDICAID
 Oregon had to limit the number of people who could receive
Medicaid because of budget constraints.
 Budget resources situation improved, Oregon could add more
people to Medicaid program, but not everyone that is eligible.
 Asked people to submit request to be put on wait list and to be
selected at random for Medicaid.
 People who are selected at random are asked by mail to fill out
full application, and eligible applicants receive healthcare.
80
©2013 ideas42

81
Mailed
Offer
100
Do not
Complete
App.
40
60
Complete
App.
App. rejected
– Not filled
out properly
or ineligible
30
30
App.
approved
©2013 ideas42

82
Mailed
Offer
100
Do not
Complete
App.
40
60
Complete
App.
App. rejected
– Not filled
out properly
or ineligible
30
30
App.
approved
Primary focus –
increase uptake
Secondary focus
– increase proper
fill outs
©2013 ideas42

DIAGNOSIS - 3 STATES OF ENGAGEMENT
1. How can we increase
the opening of
envelopes?
Opening Understanding Taking Action
2. How can we help
customers understand
the letter?
3. How can we help
customers quickly take
action?
 There are 3 states of engagement with the customer when sending a letter:
 Opening the envelope
 Understanding the letter and application
 Taking Action, e.g., properly fill out application
83
BUT WE COULD NOT CHANGE THE APPLICATION ITSELF?
©2013 ideas42

DIAGNOSIS - TIMING DRIVES
RECEPTIVENESS AND COGNITIVE
DEMANDS
84
(LMI)
Results
Experiment
• Mall shoppers participated in an
experiment
• Shoppers were randomly assigned to one
of two groups. One group was asked to
think through an easy financial situation,
and the other was challenged with a hard
financial situation
• Afterwards, all the shoppers received a
simple cognition test
Cognitive Score and Financial
Stress
• The cognition scores of lower income
shoppers who were asked to think about
the hard financial scenario were
significantly lower than those asked to
think about the easy financial situation.
• Shoppers with above median income
showed very little variation in their
cognition scores in the difficult financial
situation
©2013 ideas42

DESIGN – FOUR ELEMENTS
• Timing – Try to have some items hit during 1st week of
the month, when financial cognition demands are
lowest
• Personalized/Humanized Post Card 7 days prior to
application from the State
• Second Application in a large blue envelope
• Reminder post card with personalization and number
to call
89
©2013 ideas42

DESIGN – PERSONALIZED POST CARD
91
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!
!!!!!!!!!!!!!!!!!!
!
!
!
!
!
Center&
for&
Outcomes&
Research&
and&
Education&
5211!NE!Glisan!St.!
Portland,!OR!97213!
!
Address!Service!Requested!
PRESORTED
FIRST CLASS MAIL
US POSTAGE
PAID
PORTLAND OR
PERMIT NO. 5510
Recipient Name
Street Address
Address 2
City, ST
ZIP Code
©2013 ideas42

92
before& &
You’ve'won'the'lottery'you'entered FREE&
HEALTH&
CARE Oregon&
Health&
Plan
Call&
toll&
free&
1=877=215=0686&
or&
email&
me&
at& 'with&
any&
questions—&
I&
am&
here&
to&
help!&
&
&
[RA&
circles&
photo&
and&
handwrites&
signature]
DESIGN – PERSONALIZED POST CARD
©2013 ideas42

DESIGN – SECOND LARGE BLUE ENVELOPE
93
• Different from most other envelopes, but color is familiar
• Calming
©2013 ideas42

DESIGN – REMINDER
94
-0.40
-0.35
-0.30
-0.25
-0.20
-0.15
-0.10
-0.05
0.00
Number of Late Periods Number of Late Periods if Ever Late
Behavioral treatments compared to control
One
relationship
manager
Different
relationship
managers
Reminders
©2013 ideas42

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