Transforming PGHD for Wellness and Biomedical Research

Patient-Generated Data for Wellness/Biomedical Research: Behavioral Sensing to Decision Support
Transforming Patient-Generated Data for
Wellness and Biomedical Research:
From Behavioral Sensing to Decision Support
AMIA 2016 Didactic Panel
Nov 14 15:30 - 17:00 Salon A1
Panelists: Susan Peterson, PhD, MPH, Katherine Kim, PhD, MPH, MBA,
F. Martin-Sanchez, PhD, FACMI, FACHI, Cagatay Demiralp, PhD
Discussant Summary & Panel Moderator: Pei-Yun Sabrina Hsueh, PhD

Didactic Panel - Transforming Patient-Generated Data for Wellness and
Biomedical Research: From Behavioral Sensing to Decision Support
Susan Peterson, PhD, MPH
University of Texas MD Anderson Cancer Center
Katherine Kim, PhD, MPH, MBA
University of California Davis
Fernando Martin-Sanchez, PhD, FACMI, FACHI
Weill Cornell Medicine,
Cagatay Demiralp, PhD
IBM T.J. Watson Research Center
Pei-Yun Sabrina Hsueh, PhD
(Chair/Moderator)
(IBM T.J. Watson Research, USA)

Pei-Yun (Sabrina) Hsueh, PhD
Research Staff Member
IBM Watson Research Health Informatics PIC co-Chair
Behavioral Analytics Lead
Computational Health Behavioral and Decision Science Group
Center for Computational Health
IBM T. J. Watson Research Center
AMIA CPHI WG Secretary
AMIA Consumer and Pervasive Health Informatics Work Group
Opening Remark
Stratification
Identify sub-populations at-risk for
unhealthy behaviors
Personalization
Tailor intervention strategies for
individuals
Engagement
Promote healthy behaviors on a day-to-
day basis

Improving health is not about more medical care:
From Precision Medicine to the use of PGHD for Science of Care
Source: Bipartisan Policy Center,
“F” as in Fat: How Obesity Threatens America’s Future (TFAH/RWJF, Aug. 2013)
6
Medinfo 2013 panel
MIE 2014 workshop
MEDINFO 2015 workshop
HEC/MIE 2016
(IMIA Consumer Health
informatics WG)
AMIA 2016 (AMIA
Consumer and Pervasive
Health Informatics WG
pre-symposium & didactic
panel)

Who is Oscar? Patient is not Just a Patient
• Our agenda for Oscar:
– Medication adherence
– Come to follow-up appointments
– Improved self-monitoring
– Participation in PT
– Nutritious food choices and
increased calories
– Living Will
– Participate in Shared Decision-
Making
• Oscar’s agenda for Oscar:
– Grieving for his wife
– Transportation
– Managing Rx side effects
– Seeing his grandchildren
– Reducing knee pain
5

6
Can data from EHR be the answer?
Capturing Social/Behavioral Determinants from EHR
Institute of Medicine
report (2016)

Health consumer data collection are emerging
with high perceived value, but gaps persist.
67% up from 27% in 2014. More than 48%
and 33% know they can access lab results
and physician notes.
Willing to wear technology
for health tracking
% believe that using
PGHD would be
beneficial
Access to EHR
Scale
> 50%
45%
78%
78% : 18%
The proportion of patients believes in
full access to health records v.s. the
proportion of physicians
Gap
IBM Confidential
Patient Participation
Source:
J.P. Gownder, et al. Forrester research report 2015. PwC Strategy Report 2016.
Catalyst for Payment Reform,, CPR, 2015.
W. Lynch, B. Smith, and M. Slover, Altarum Institute Survey of Consumer Health Care Opinions 2012.
E.O. Lee and E.J. Emanuel,, N Engl J Med 368 (2013), 6-8.
Already wearing
or using apps
21-33% > 90%
% willing to participate
in shared decision
making with clinicians

PGHD evidence is still being collected…
• Some initial evidence points to different directions
– Not effective for improving diet or physical activity behavior for weight management
over 24 months (IDEA trial, JAMA 2016)
• Interestingly, no difference of dietary and physical activity behavior between the standard
intervention and the wearable group; yet outcome is different.
– Not effective for healthcare cost and utilization control over 6 months (PeerJ 2015)
• Interestingly, patients who monitored their health were less likely to attribute health outcomes
to chance than those who didn’t monitor their health
• Some initial success in linking internal motivators
– Improving adherence to medication and blood pressure monitoring (McGillicuddy et
al., 2013)
– Promoting an individual’s sense of autonomy by helping them to focus on their own
reasons for increasing levels of physical activity and exercise (Riiser et al., 2014)
• More research on the horizon….
– Motivational framework (Stanford), sensing making (Columbia U), adaptive
intervention (U Mich, Columbia U, IBM Research), social media for treatment
affordance (Well-Corneil), to name a few, etc.

Agenda
• 15:30-16:30 Presentations
– Opening remark
– Prof. Peterson: Using Patient-generated Health Data for Assessment and
Intervention in Cancer Survivors
– Prof. Kim: Leveraging Patient-generated Health Data for Person-centered
Care Coordination
– Prof. Martin-Sanchez: Evidence generation from Patient-generated Health
Data for informing biomedical research
– Dr. Demiralp : PGHD & Visualization
– Dr. Hsueh (Summary): EMERGING HEALTHCARE LANDSCAPE SHIFT WITH PATIENT-
GENERATED DATA
• 16:30-17:00 Panel discussion/audience Q&A
– Highlighting the opportunities (use cases)
– Identifying the bottlenecks and barriers of using patient generated health data
– Potential solution to overcome the barriers identified

Using Patient-generated Health Data for
Assessment and Intervention
in Cancer Survivors
Susan K. Peterson, PhD, MPH
Professor, Department of Behavioral Science
The University of Texas MD Anderson Cancer Center
Houston, Texas
AMIA 2016 Panel
Nov 14 15:30 - 17:00

Susan K. Peterson, PhD, MPH
Professor of Behavioral Science
Director, Patient-Reported Outcomes, Survey & Population Research Shared Resource
The University of Texas MD Anderson Cancer Center
Research interests
• Development and evaluation of e-Health interventions for populations at
risk for hereditary cancer and cancer survivors, including sensor-based and
mobile technology applications for behavioral assessment and intervention
• Psychosocial and behavioral outcomes of cancer genetic and genomic
testing in cancer survivors and families, including:
- Decision-making about testing & receiving genetic test results
- Psychological and behavioral impact of testing on quality of life and
related factors

Health information technology
priorities for oncology
• Systems approach for greater and more rapid access
to patient clinical information
• Better understand therapeutic responses, side effects, quality of life,
general health status
• Identify trends important to prevention and survivorship
• Synthesize and distill large amounts of data
• Identify earlier opportunities to manage side effects, complications, long-
term survivorship outcomes
• Personalized, relevant guidance driven by real-time
data for smarter management decisions by patients,
physicians
• Support activated and empowered individuals
Blueprint for Transforming Clinical and
Translational Cancer Research, ASCO, 2011
President’s Cancer Panel, NCI, 2014-2015

CYCORE: CYberinfrastructure for cancer
COmparative effectiveness Research
RC2 CA148263, R01 CA177914, R01 CA177996
System features:
• Patient-accessible platforms for rapid, direct data
collection
• Remote patient monitoring and management, away
from clinic setting
• Sensors, ecological momentary assessment
(EMA), video interface
• Interfaces accessible to patients, clinicians,
researchers
• Ability to receive feedback, track data
(historical and real-time)
Patrick, Transl Beh Med, 2011; Hirsch, Ca J, 2011

U.S. cancer incidence & deaths
All Cancers 1,665,540 585,720
Lung 224,210 163,660
Breast 235,030 40,430
Prostate 233,000 29,480
Colorectal 144,040 51,260
Head & Neck 55,070 11,490
(e.g., oropharynx, nasopharynx, larynx, oral cavity, salivary)
American Cancer Society 2016
New cases Expected Deaths
Incidence of HPV-positive oropharyngeal cancers has > doubled in past 20
years, due to increasing incidence of HPV in U.S. population

Head & neck cancer treatment
• Nasopharynx
• Oropharynx
• Hypopharynx
• Larynx
• Oral cavity
• Thyroid
• Sinonasal
• Salivary
• Skin
Curative radiation therapy (RT)
With/without chemotherapy
Curative surgery
With/without radiation therapy
With/without chemotherapy
RT typically lasts 5 days/week for 6-7 weeks

Challenges in managing treatment-
related side effects
• Significant side effects from RT + chemo for HNC
• Mucositis resulting in swallowing problems,
pain, altered eating/drinking capabilities
• Adherence to complex self-care regimen necessary
to reduce long-term morbidity
– 67% non-adherent to swallowing exercises during RT (Shinn,
2010)
• Climate for malnutrition and dehydration
• Physiological decline can rapidly occur
between routine visits
• Increased ER visits, inpatient admissions,
costs

Standard clinical care during
radiation therapy
• Weekly visits with radiation oncologist
• Weight, temperature, pulse
• Blood pressure (sitting & standing)
– Orthostatic Hypotension: Decrease of 20 mmHg
systolic or 10 mmHg diastolic on standing. Usually
accompanied by an increase in heart rate
– Assess nutrition and hydration
• IV fluids if dehydrated; GI consult for feeding tube
– Pain and symptom assessment
• Pain control, supportive care
Limitations
- Ability to assess patient only once/week in clinic
- Rapid physiological changes can occur between visits
- Home assessment historically limited to self-report

Opportunity: Using patient-generated
data to prevent dehydration during RT
• Objective, frequent data collection on key physiological
and behavioral outcomes
• Weight, hydration, BP, pulse, swallowing
• Identify patients at high risk, reduce hospitalization, ER
visits
• Identify need for IV hydration early
• Better nutritional and pain support
• Support adherence to self-care
• Provide decision support for clinician to optimize
chances for rapid intervention, support

Monitoring dehydration risk in HNC
patients during RT
Blood pressure
+ pulse
Data transmission to
home-based hub
Scale to monitor
weight loss
Interface with CI
Data available to researchers, clinicians
Patient-reported
outcomes
(symptoms,
nutrition, fluid
intake/output)
via phone/tablet
app

CYCORE web-based interface:
Daily weight, BP, pulse
Enables clinician/researcher to view data, track trends in weight, pulse, BP
Orthostatic
hypotension + Pulse
Dehydration risk
Early
identification
via CYCORE
Earlier IV
fluid therapy
Improved outcome: reduction in ER visits, hospitalizations

CYCORE web-based interface:
Patient-reported outcomes
Enables clinician/researcher to view data on daily patient-
reported symptoms and medications

Preliminary data
• 48/50 patients completed feasibility study; 98% of
assessments completed
• 60% of patients had at least one dehydration-related
event during study
» Orthostatic hypotension: ↓ systolic BP + ↑ HR 10
points, sitting vs. standing readings
• Symptoms associated with dehydration-related
events
– Nausea (p=0.004), vomiting (p=0.004) swallowing difficulty
(p=0.004)
• High level of patient and clinician support and
satisfaction
Peterson, Shinn, et al., JNCI Monogr 2013

HNC patients and dehydration risk:
Satisfaction and usability ratings
Peterson, JNCI Monogr, 2013
Participants’ responses to post-study evaluation questionnaire regarding usability and
acceptability of mobile devices for home monitoring of dehydration risk (Range: 0 = “not at
all” to 10 = “extremely”), n=48
0
1
2
3
4
5
6
7
8
9
10
Helpfulness
of baseline
training
session
Clarity of
printed
instructions
you used at
home
Ease of
device use
at home
Confidence
in ability to
use device
at home
Usefulness
of automatic
data
provision to
doctor
Importance
of seeing
device
reading at
home
Concern
about the
data privacy
Satisfaction
with use of
each device
BP Device
Weight Scale
Phone: Daily
Symptoms
Surveys
Home Health Hub

Sensor-assisted prevention of dehydration
in head and neck cancer patients
Aims:
1. Evaluate efficacy of sensor-based home monitoring in
reducing the prevalence of hospitalization and emergency
room visits related to dehydration in HNC patients undergoing
RT
- Compare hospital and ER admissions in CYCORE-
assigned patients vs. usual care
2. Evaluate the efficacy of sensor-based home monitoring in
reducing costs related to treating dehydration in HNC patients
undergoing RT
1R01CA177914-01 NCI/NIH (Pis: Peterson, Shinn, Beadle, Garden)

Eligibility
- Bilateral RT for:
oropharyngeal,
hypopharyngeal,
nasopharyngeal,
laryngeal, salivary gland,
thyroid, oral cavity,
unknown primary HNC
with cervical metastasis
- Age > 18 yrs; English
proficient; Zubrod <2
- No prior dysphagia
CYCORE
Head & Neck Cancer RCT
September 2016

CYCORE
(n=127)
Standard care
(n=127)
Age, M (range) 62 (26-79) 61 (36-79)
Female, % 22% 22%
Race – White %
Black
Hispanic
Asian
Other
86%
1
7
4
1
79%
9
9
1
3
Married % 84% 84%
< HS graduate
Some college/other
> bachelor’s degree
17%
27
56
20%
25
54
CYCORE Head & Neck Cancer RCT
Participants’ Demographic Characteristics

Using patient-generated health data:
Barriers and solutions
Barriers
• Need to balance utility of information vs. clinician, workflow, and
time burden
• Ability to collect, administer, and communicate PGHD ≠ clinical
goals or needs
• When and how to integrate PGHD into EHRs
Possible solutions
• Involve multidisciplinary stakeholder teams to identify use cases
w/ clear clinical need & technology solutions
• Collect, administer, and communicate PGHD for the right patient,
the right problem, the right time
• Optimize process automation and usable system interfaces to
integrate PGHD into EHRs
Patrick, Transl Beh Med (2013); Harle, JAMIA (2016)

Collaborators
MD Anderson
– Karen Basen-Engquist, PhD, MPH
– Eileen Shinn, PhD
– Beth Beadle, MD, PhD
– Adam Garden, MD
– Sanjay Shete, PhD
– Chan Shen, PhD
– Alex Prokhorov, MD, PhD
– Stephanie Martch, MS, RD, LD
UC San Diego/Calit2
– Kevin Patrick, MD
– Emilia Farcas, PhD
– Fred Raab, MS
– Chaitanya Baru, PhD
– Ingolf Krueger, PhD
– Viswanath Nandigam, MS
– Kai Lin, PhD
– Yan Yan, MS
Univ. of Alabama-Birmingham
– Wendy Denmark-Wahnefried, PhD

Leveraging Patient-generated
Health Data for Person-centered
Care Coordination
Session S45 Didactic Panel
Transforming Patient-generated Data for Wellness
and Biomedical Research: From Behavioral Sensing
to Decision Support
Nov 14, 2016, 3:30-5:00
Katherine K. Kim, PhD, MPH, MBA
kathykim@ucdavis.edu

33
Challenges for
individuals with
complex chronic and
co-morbid conditions

34

Care Coordination Challenge
35 35
HOSPITALS
INPATIENT
COMMUNITY
SERVICES
PERSON
CLINIC
S/
OFFICE
S
HOSPICE CARE
OUTPATIENT
CARE
CAREGIVERS
RESPITE CARE
SOCIAL SERVICES
HOME CARE
PRIMA
RY
CARE
SPECIAL
IST
SPECIAL
IST
SPECIAL
IST
PRIMARY CARE
SPECIALI
ST
SPECIAL
IST
FAMILY
Many touch points, multiple transitions, unclear accountability

HOSPITALS
INPATIENT
HOME CARE
PRIMARY
CARE
SPECIALIS
T
SPECIALIS
T
COMMUNITY
SERVICES
PATIENT
CLINIC
S/
OFFICE
S
HOSPICE CARE
OUTPATIENT
CARE
CAREGIVER
S
SPECIALIS
T
RESPITE CARE
SOCIAL SERVICES
FAMILYPRIMARY CARE
SPECIALIS
T
SPECIALIS
T
Many portals with incomplete information

Community-Wide Care Coordination: A
Framework in Process
“Care coordination is the deliberate
synchronization of activities and information
to improve health outcomes by ensuring that
care recipients’ and families’ needs and
preferences for healthcare and community
services are met over time.”
(National Quality Forum, 2014)
37

Personal Health Network (PHN) for
Chemotherapy Care Coordination
• 2-arm feasibility RCT: care coordination with and
without PHN
• 60 participants
• User-centered design
• Evaluation:
- Health technology acceptance and use
- ED and inpatient utilization
- Symptom severity
- Workflow efficiency
38

Personal Health Network (PHN)
• Shared care plan
• Patient education & information at the “point of need”
• Person-generated data: symptoms, PROs, needs
• Communication
• HIPAA compliant mobile application
- Tiatros platform
- iOS application
- Browser application
39

40
Table 1: Baseline Socio-Demographic and Health Characteristics
Care Coordination
Plus PHN
Care Coordination
Alone p
n=35 n=26
Age in Years (SD) 59.00 (11.12) 59.17 (9.18) 0.95
Female, % 75 75 1.00
White non-Hispanic, % 89 73 0.12
Completed Education, % 0.68
Employment, % 0.55
Married/Partnered, % 58 79 0.19
Family Income, % 0.24
Health Status, % 0.32
Cancer Stage, % 0.62
I 14 8
II 37 44
III 26 16
IV 23 32
Treatment Plan*
Chemotherapy 100 96 0.24
Radiation 31 34 0.79
Other 20 8 0.18
* Treatment Plan variables do not add up to 100%; respondents could check more than
one.

41
Table 2: Health Technology Acceptance and Use (HTAU) at Baseline (n=33;
Intervention Group Only)
Construct and Items Score*
Mean SD
Price Value (3 PV Items) 4.40 0.12
Facilitating Conditions (4 FC Items) 4.48 0.48
Effort Expectancy (4 EE Items) 3.86 0.11
Social Influence (5 SI Items) 3.13 0.10
Performance Expectancy (8 PE Items) 3.10 0.40
Hedonic Motivation (3 HM Items) 3.30 0.30
Behavioral Intention (3 BI Items) 3.41 0.23
Habit (3 HT Items) 2.37 0.08
* Each item rated from 0 (not at all) to 6 (a great deal)

Implications
• One of the first examples of a technology-enabled
care coordination intervention in oncology
• Early evaluation of usability has allowed for
refinements and PHN v.2 to be rolled out to the
same participants.
• Equal attention to person-generated and clinical
data allows potential for person-centered care.
• Transparency supports collaboration: Data
generated by patient and coordinator is open to
each PHN’s members.
• Remaining challenges: connectivity, integration with
the EHR for seamless adoption.
42

Acknowledgements
UC Davis Collaborative Care Coordination Research Group (3CRG): Betty
Irene Moore School of Nursing, School of Medicine, Comprehensive Cancer
Center
• Jill Joseph
• Janice Bell
• Andra Davis
• Sarah Reed
• Rick Bold
• David Copenhaver
• Tom Semrad
• Victoria Ngo
• Robin Whitney
• Joy Morgan
• Wendy Wait
• Chelsie Antonio-Gonzales
• Ronald Grummer
• Thuy Le
Funding
• McKesson Foundation #201401953 (PI
Joseph)
• NIBIB/Boston University Center for the
Future of Technology in Cancer Care,
#U54-EB015403-04 (PI Kim, Joseph)
• Oncology Nursing Society
• UC Davis Center for Health Policy
Research (PI Bell)
• UC Davis Academic Senate grant for New
Research Initiatives and Collaborative
Interdisciplinary Research (PI Bell)
• Gordon and Betty Moore Foundation
grant to Betty Irene Moore School of
Nursing

Summary
• Opportunities for person-generated data
- Equal attention to person-generated and clinical data enables
person-centered care.
- Transparency may be a prerequisite for collaborative care
models
• Identify bottlenecks and barriers for using person-generated
data
- Ubiquitous persistent connectivity
- Need enterprise level robustness and functionality with ease
and footprint of mobile app
• Potential mechanisms for overcoming
- Innovations in application design for performance, scalability,
integration
44

Thank You
Katherine Kim
kathykim@ucdavis.edu
45

Fernando Martin-Sanchez PhD, FACHI, FACMI
• Professor - Division of Health Informatics, Dept. of Healthcare
Policy and Research
• Environmental and Participatory Health Informatics (ENaPHI)
Research Group
• PhD in Informatics, PhD in Medicine, MSc in Knowledge
Engineering, MSc in Molecular Biology, BSc in Biochemistry
• Joined Weill Cornell in December 2015
– Professor and Chair of Health Informatics, Melbourne
Medical School (2011-2015)
– Director, Health and Biomedical Informatics Research
Centre, (HaBIC) the University of Melbourne (2013-2015)
– Head of Dept. Medical Bioinformatics. National Institute of
Health Carlos III of Spain. (1998-2010)
• Research interests: biomedical data integration, participatory
health informatics, exposome informatics, precision medicine
Email: fem2008@med.cornell.edu
Twitter: @fermarsan

ENaPHI@
WeillCornellMedicine
Participatory Technologies
(Digital Health)
QS, mHealth, SoMe, DTC …
Biomedical
Research
Healthcare &
Prevention
Environmental
Health
Informatics
Participatory
Health
Informatics
EXPOSOME
• Ontologies
• Resources
• Expotyping
EVIDENCE
GENERATION
• Therapeutic affordances
of social media
• Essential characteristics
of SQS
Precision Medicine Informatics

Availability of devices, sensors, apps, DTC services and Social Networks
Wearables
Sensors
DTC lab tests
Apps

• Digital health (participatory) technologies
– smartphone apps,
– personal sensing devices,
– direct-to-consumer e-services
– social media
• Health Informatics is moving into new territories, beyond provider-generated
clinical data - PGHD (Patient-generated health data)
1. Monitoring of individual environmental health risk factors. Exposome
2. Participatory Health
• These new sources of individual big and small data (continuous,
comprehensive and personalized) pose great challenges for Health Informatics
and will require new approaches to data collection, storage, standardisation,
integration, analysis and visualisation.
accessible and
affordable for
individuals

51
• Gregory Abowd (2011) predicted “within 5 years, the
majority of clinically relevant data...will be collected outside
of clinical settings.”
• PGHD—health-related data created, recorded, or gathered
by or from individuals to help address a health concern
• Individuals are responsible for recording data and decide how to
share it (Personal Health Data)
Potential Benefits Challenges
•Personalized/ preventative medicine
•Reduction of unnecessary patient visits
and/or hospital admissions
•Easy and continuous monitoring
•A cheap treatment for many chronic diseases
•Verifying measurement validity
•Lack of standardization (interoperability)
•Specific challenges for clinicians
•Specific challenges for patients
•Financial and technical
•Legal
Mark Liber, 11

Health
Informatics
Bioinformatics
Proteomics
and
Metabolomics
Data
Gene
expression
Data
Genomic
Data
Patient
generated
Data
Population
Data
Clinical
Data

A different way to recruit research participants
• For a recent breast cancer study, epidemiologist Kathryn H. Schmitz
of the University of Pennsylvania sent out 60,000 letters—and
netted 351 women. Walking each participant through the
paperwork took 30 minutes or more. Such inefficient methods of
finding test subjects have been the norm for medical research.
• Apple, working with Stanford University School of Medicine,
developed MyHeart Counts, an app for monitoring cardiac health.
Within the first 24 hours, 10,000 participants signed up for the
study.
• Kelton and Makovsky Health -fifth annual “Pulse of Online Health”
found that 66 percent of Americans would use a mobile application
to manage health-related issues.
• The patient’s voice has largely been missing from most of the
design and the focus of clinical studies (Ken Mandl, Harvard).
• Citizen science, participatory health

Genotype * Expotype  Phenotype
Coriell Personalised Medicine Collaborative
Marc Rubin,
Nature 2015

Genome / GenotypeExposome / Expotype
Phenome / Phenotype
Biomarkers (DNA sequence,
Epigenetics)
Environmental risk factors
(pollution, radiation, toxic agents, …)
Anatomy, Physiological, biochemical parameters
(cholesterol, temperature, glucose, heart rate…)
Social media / Personal health record / EMRs / Research Repository

Software
Apple Google
Cornell
Tech
Samsung
DB
smartphone HealthKit Google Fit S Health
Apps for
researchers ResearchKit Study Kit
(Baseline)
Research
Stack
Apps for
consumers
CareKit
OHMAGE-omh

EHR integration with HealthKit
• 2014 - Ochsner Health System in Louisiana
• 2015 - Deaconess Health System in Indiana
integrates Fitbit into EHR portal
• 2015 - Duke is using HealthKit to get patient-
generated data into the EHR.
• 2015 - Cerner with Validic
• 2015 - EPIC MyChart at Cornell Medicine has full
HealthKit functionality

mPower
• First six months of data from the app. mPower, a
Parkinson's-focused app, one of the first five Apple
ResearchKit studies
• Of the 12,000 mPower study participants, about 9,500
participants chose to share their data with all
researchers.
• mPower stands for "mobile Parkinson’s observatory for
worldwide, evidence-based research".
• The mPower app aims to help users track their symptoms
using activities including a memory game, finger tapping,
speaking, and walking. The app will also collect data from
wearable devices

Jawbone and Bay Area Earthquake
Sept 2014
https://jawbone.com/blog/napa-earthquake-effect-on-sleep/

ENTRAIN app

Health eHeart

Google Baseline

100K Wellness Project

N-of-1 studies
• https://www.thegua
rdian.com/science/
2016/jul/03/citizen-
science-how-
internet-changing-
amateur-
research?CMP=sha
re_btn_fb

US Precision Medicine Initiative Cohort Program

Summary: Use of PGHD
Challenges / Limitations Possible solutions
Verifying validity of data Reproducibility exercises
Lack of standardization (interoperability) Standard reporting guidelines and
templates
How to decide which data to use More research is needed
Lack of integration with clinical
workflow
User centric design, involving clinicians
Insufficient training More emphasis in health professions
curricula and outreach to society at
large
Equity and access (economic barriers
and digital gap)
Proper cost-efficiency evaluation and
sponsorship by health insurers or
providers

Data
Visualization
70
Çağatay Demiralp @serravis
IBM Research

IBM T. J. Watson
Stanford University
University of Washington
Microsoft Research
Cambridge
Brown University
Çağatay Demiralp
Current
Previous
Data Visualization . Visual Analytics . HCI

IBM T. J. Watson
Stanford University
University of Washington
Microsoft Research
Cambridge
Brown University
Çağatay Demiralp
Current
Previous
Data Visualization . Visual Analytics . HCI
Extend the theoretical and perceptual foundations of
data visualization
Develop and automate interactive visual analysis tools Í
Current focus

What is
visualization?

What is visualization?
• “Transformation of the symbolic into the geometric.”
[McCormick et al. 1987]
• “... finding the artificial memory that best supports our
natural means of perception.” [Bertin 1967]
• “The use of computer-generated, interactive, visual
representations of data to amplify cognition.”
[Card, Mackinlay, & Shneiderman 1999]

Why create
visualizations?

E.J. Marey’s sphygmograph [from Braun 83]

In 1854 John Snow plotted the
position of each cholera case
on a map [from Tufte 83]

Used map to hypothesize that pump on
Broad St. was the cause

34
72x
————
0
30
60
90
120
Mental Paper & Pencil
Time(Secs)68
2380+————
2448

New York weather [from NY Times 1981]

Bones in
hand
Double helix [Watson and
Crick 53]

Crimean War Deaths by Florence Nightingale,1856

Crimean War Deaths by Florence Nightingale,1856
“to affect thro’ the eyes
what we fail to convey to
the public through their
word-proof ears”

Why do we create visualizations?
• Record information
• Blueprints, photographs, seismographs, …
• Analyze data to support reasoning
• Develop and assess hypotheses
• Discover errors in data
• Expand memory
• Find patterns
• Communicate information to others
• Share and persuade
• Collaborate and revise

PGHD &
Visualization

Usual story
Focus on data
collection
Lack of tools for
sense making

How effective
are these?

What do they mean?

Challenges
Little known about effectiveness
Behavior change, decision making, risk
assessment, diverse users & tasks
Difficult to interpret & operationalize
Noisy, sparse and heterogenous data

Challenges
Little known about
effectiveness
Behavior change,
decision making,
risk assessment, etc.
Difficult to interpret &
operationalize
Noisy, sparse and
heterogenous data
Solutions
Systematically evaluate
Derive general principles
Communicate risk &
uncertainty
PGHD-VisKit
Integrative tools
What-if scenarios
Reproducible analysis
Automation

DISCUSSANT SUMMARY
EMERGING HEALTHCARE LANDSCAPE SHIFT WITH
PATIENT-GENERATED DATA

It is not about Big Data
SOURCE: Barbara J. Sowada, A Call to Be Whole:
The Fundamentals of Health Care Reform, July 30, 2003, Praeger.
IBM Watson // ©2015 IBM Corporation
NOISY, LARGE VOLUME,
UNCONTROLLED
Need minimum description
& quality/validity study

9
6
Can data from EHR be the answer?
Capturing Social/Behavioral Determinants from EHR
Institute of Medicine
report (2016)

HETEROGENEOUS SOURCES OF PGHD POSE
CHALLENG TO HIT: PATIENT-CENTEREDNESS
EHR Administrative Data/Claims
PHR
Mobile PHR/
Ecological Momentary
Assessment
Patient-Reported
Outcome
Ref: Wu, A. W., et al. (Journal of Clinical Epidemiology 2013)

PGHD evidence vary
• Some initial evidence points to different directions
– Not effective for improving diet or physical activity behavior for weight management
over 24 months (IDEA trial, JAMA 2016)
• Interestingly, no difference of dietary and physical activity behavior between the standard
intervention and the wearable group; yet outcome is different.
– Not effective for (PeerJ 2015)
• Interestingly, patients who monitored their health were less likely to attribute health outcomes
to chance than those who didn’t monitor their health
• Some initial success in linking internal motivators
– Improving adherence to medication and blood pressure monitoring (McGillicuddy et
al., 2013)
– Promoting an individual’s sense of autonomy by helping them to focus on their own
reasons for increasing levels of physical activity and exercise (Riiser et al., 2014)
• More research on the horizon….
– Motivational framework (Stanford), sensing making (Columbia U), adaptive
intervention (U Mich, Columbia & IBM Research), social media for treatment
affordance (Well-Corneil), to name a few, etc.

Who is Oscar? Patient is not Just a Patient
• Our agenda for Oscar:
– Medication adherence
– Come to follow-up appointments
– Improved self-monitoring
– Participation in PT
– Nutritious food choices and
increased calories
– Living Will
– Participate in Shared Decision-
Making
• Oscar’s agenda for Oscar:
– Grieving for his wife
– Transportation
– Managing Rx side effects
– Seeing his grandchildren
– Reducing knee pain
100

More Patient-centered Comparative
Effectiveness Research are needed
Provide outcome
indicators.
Provide quality
assessment and
improvement
measures.
Serve as a basis for
population health
campaign tools.
Improve Clinical
Care & Quality
Collect evidence for
comparing
intervention options
with similar efficacy.
Drive patient-
centered CER and
identify
personalization
factors.
Comparative
Effectiveness

Integrating with Best Practice in Clinical Flow
Includes medication,
access, utilization, self-care.
Inferred from the assigned
interventions (enrollment,
goal, tasks).
Disease Burden
Includes job family,
lifestyle.
Inferred from
patient profiling.
Life Demand
Ref: Mayo Clinic: Minimally Disruptive Medicine: Kerunit model.
Instrument for Patient Capacity Assessment (ICAN)
What are you doing when you are not sitting here with me?
Where do you find the most joy of your life?
What’s on your mind today?
Are these areas of your life a source of satisfaction, burden, or both?
What are the things that your doctors or clinic have asked you to do to care for your health?
Do you feel that they are a help, a burden, or both?

What do you need to know about
these individuals?
103

Improving health is not about more medical care:
From Precision Medicine to the use of PGHD for Science of Care
Source: Bipartisan Policy Center,
“F” as in Fat: How Obesity Threatens America’s Future (TFAH/RWJF, Aug. 2013)
6
Medinfo 2013 panel
MIE 2014, MEDINFO 2015 workshop
HEC/MIE 2016
(IMIA Consumer Health informatics WG)
AMIA 2016 (AMIA Consumer and Pervasive
Health Informatics WG pre-symposium &
didactic panel)

Key Questions for PGHD transformation
• What is the opportunity area going forward for PGHD
transformation?
• What is our definition of PGHD? What are the likely sources?
• What the key dimension of PGHD for value evaluation of data
transformation? What are the barriers? More technical or
social?
• What are the likely action items to be suggested to the
community to further the discussion about transforming
PGHD in biomedical and wellness research? Is there a filed
difference to be addressed here?

Key Questions for PGHD transformation (2)
• How can PGHD contribute to successful provider-patient
communications, risk reduction, and increase in early
interventions?
• Can PGHD support shared decision making or help calibrate
just-in-time intervention to patient’s values?
• Do the providers’ and patients’ beliefs and support of PGHD
and approaches affect patient usage?
• Can dynamically configured healthcare IT help improve
healthcare quality and patient behavior using a scalable
technology-enabled platform?

Thank You
Merci
Grazie
Gracias
Obrigado
Danke
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