Rupa Patel's Ph.D. Dissertation Defense, UW Biomedical & Health Informatics
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Transcript of Rupa Patel's Ph.D. Dissertation Defense, UW Biomedical & Health Informatics
Designing for Use and Acceptance of Tracking Tools in Cancer Care
Rupa PatelBiomedical & Health InformaticsOral Dissertation DefenseAugust 6, 2013
Self-Tracking in Cancer Care
MOTIVATION: Why Track Symptoms in Cancer
Care?
AIM 1: ePRO Tool Use and Symptom Distress
AIM 2: Patient-Driven Self-Tracking
MODEL: Design Considerations for Tracking Tools
CONTRIBUTION & FUTURE WORK2
DIS
SERTA
TIO
N S
UM
MA
RY
MOTIVATION: Why Track Symptoms in Cancer
Care?
AIM 1: ePRO Tool Use and Symptom Distress
AIM 2: Patient-Driven Self-Tracking
MODEL: Design Considerations for Tracking Tools
CONTRIBUTION & FUTURE WORK3
DIS
SERTA
TIO
N S
UM
MA
RY
Self-Tracking in Cancer Care
4
MO
TIV
ATIO
NMarypatient withbreast cancer
Thumb infection
Insomnia
Pain
Unstable glucose level
Weight loss
Fatigue & Nausea
Hives Dry cough
High blood pressureSwelling
5
Symptom Communication
6
Nausea
Fatigue
Pain
Weight loss
MO
TIV
ATIO
N
{ CLINIC VISIT }
7
MO
TIV
ATIO
N
Nausea
Fatigue
Pain
Weight loss
Swelling Neuropathy
Symptom Communication
{ CLINIC VISIT }
8
Thumb infection
Vaginal dryness
Dry cough
Anxiety
..
MO
TIV
ATIO
N
Nausea
Fatigue
Pain
Weight loss
Swelling Neuropathy
Symptom Communication
{ CLINIC VISIT }
• Care based on continuous healing relationships
• Shared knowledge and the free flow of information
• Personalization based on patient needs and values
9
Institute of Medicine (IOM) Report, 2001
MO
TIV
ATIO
N
Communication Needs in Cancer Care
Can Tracking Tools Help?
10
MO
TIV
ATIO
N
Cooking Hacks Website http://www.cooking-hacks.com/index.php/documentation/tutorials/ehealth-biometric-sensor-platform-arduino-raspberry-pi-medical
Researcher/Clinician-Driven
• Patient-Reported Outcome
• Ecological Momentary Assessment
Patient-Driven
• Personal Informatics Self-Tracking
11
MO
TIV
ATIO
N
Tracking Tools Across Fields
PRO
EMA
PInf
Abernethy, AP et al. (2008). Health Serv Res 43(6): 1975-91.
Patient-Reported Outcome Instrument: FACT-G
12
MO
TIV
ATIO
N
Cella et al, Journal of Clinical Oncology, 1993
PRO
ESRA-C2
13
MO
TIV
ATIO
N
Symptom Assessment
Report View
Teaching TipsBerry, ASCO 2012.
PRO
Benefits of Patient-Reported Outcome Tools
• Improved health outcomes1,2
• Clinician awareness of symptoms3,4
• Timing of symptom reporting5
14
MO
TIV
ATIO
N
1Velikova, Journal of Clinical Oncology 2004; 2Detmar JAMA 20023Berry, Journal of Clinical Oncology 20114Ruland, JAMIA 20105Cleeland, Journal of Clinical Oncology 2011
PRO
Ecological Momentary Assessment
• Study of human behavior in daily life
• Random or periodic reminders to track
15Stone, Shifman, et al“The Science of Real-time Data Capture” 2007
MO
TIV
ATIO
N
EMA
Benefits of Ecological Momentary Assessment Tools
• Limited recency effects
• Improved event recall
• Capture of mood and context
16
MO
TIV
ATIO
N
Stone, Shifman, et al“The Science of Real-time Data Capture” 2007
EMA
• Patient-initiated tracking
• One generates data for personal insight and action
17
MO
TIV
ATIO
N
Personal InformaticsPInf
Benefits of Personal Informatics Self-Tracking
• Clinic adoption not needed
• Wide selection of apps and devices
• Consumer-facing interface design
18
MO
TIV
ATIO
N
PInf
Barriers to Tracking Tool Use
19
MO
TIV
ATIO
N
Retrospective recall X
Data integration X X
User burden X X X
Interruptions X
Interpretation of meaning
X X
PInfPRO EMA
Donaldson, Quality of Life Research, 2008
Stone, Shifman, et al. “The Science of Real-time Data Capture,”
2007
Li, et al. CHI 2010
U.S. Symptom Tracking Habits
7 in 10 adults track a health indicator• 49% of trackers keep track “in their heads”• 34% of trackers track on paper• 21% of trackers use technology
Pew Study: Tracking For Health, January 2013
Rural patients with cancer or survivors (n=134)• 1 in 3 tracked health issues during treatment• 1 in 11 used technology to track health data
Hermansen-Kobulnicky et al. Support in Cancer, 200920
MO
TIV
ATIO
N
21
How do we design tracking tools that are used and
accepted by both patients and clinicians in standard cancer
care?
MO
TIV
ATIO
N
Self-Tracking in Cancer Care
MOTIVATION: Why Track Symptoms in Cancer
Care?
AIM 1: ePRO Tool Use and Symptom Distress
AIM 2: Patient-Driven Self-Tracking
MODEL: Design Considerations for Tracking Tools
CONTRIBUTION & FUTURE WORK22
DIS
SERTA
TIO
N S
UM
MA
RY
Aim 1 Research Questions
1.1 How often do patients with cancer voluntarily use an ePRO tool?
1.2 Is frequent voluntary use of an ePRO tool associated with a reduction in symptom distress of patients with cancer?
23
AIM
1: e
PR
O T
OO
L USE
Data: Intervention Group (n=372) from Randomized Controlled TrialIntervention• Voluntary access to ESRA-C2 ePRO
Assessment-taking sessions at any time• Access to Teaching Tips/Report Views at Study
Time PointsInclusion criteria• Any cancer• Enrollment prior to treatment start• Adults 18+
24
AIM
1: e
PR
O T
OO
L USE
Berry, ASCO 2012
ESRA-C2
25
AIM
1: e
PR
O T
OO
L USE
Symptom Assessment
Report View
Teaching TipsBerry, ASCO 2012
Symptom Assessments in ESRA-C2Questionnaires• Symptom Distress (SDS15)• Depression (PHQ-9)• Quality of Life (EORCTC-QLQ-30)• Chemotherapy-induced neuropathy (EORCTC-QLQ-
CIPN30)• Skin changes• Fever/chills• Sex-related symptoms• Patient prioritization
77 total questions at study time points30 total Symptom & Quality of Life Issues (SQLI) 26
AIM
1: e
PR
O T
OO
L USE
Berry, ASCO 2012
Data Collection Procedures
27
AIM
1: e
PR
O T
OO
L USE
Study Time Points• Symptom assessment
• Reminder to take assessment
• Clinician receives report
Intervention: Access outside of 4 study time points• Choice of symptom assessments
• Viewing reports
• Viewing teaching tips
Reminder phone call 1 week after enrollment at S1
Consult prior to
treatment
S1 First on-treatmen
t VisitS2
6-8 weeks after
treatment start
S32-4
weeks after
treatment end date
S4
Berry, ASCO 2012
RQ 1.1 Analysis Methods
How often do patients with cancer voluntarily use an ePRO tool?
• Descriptive statistics
• Contingency table / Fisher’s Exact Test
28
AIM
1: e
PR
O T
OO
L USE
Overall Use of ESRA-C2
29
AIM
1: e
PR
O T
OO
L USE
S1
V1.1
V1.2
V1.3
V1.4
V1.5
V1.6 S2
V2.1
V2.2
V2.3 S3
V3.1
V3.2
V3.3 S4
V4.1
0
100
200
300
400
Frequency
of
Sess
ions
S1 S2 S3 S4
Study Time Points Voluntary Sessions
First on-treatmen
t Visit
S2
6-8 weeks after
treatment start
S3 2-4
weeks after
treatment end date
S4
Consult prior to
treatment
S1
S v
Time Points over the Course of the Study
Voluntary Sessions Are Less Likely to Include Completed Symptom Distress (SDS15) Assessments
Fisher’s exact test (p < .001)
30
AIM
1: e
PR
O T
OO
L USE
S
v
31
AIM
1: e
PR
O T
OO
L USE
RQ 1.2 AnalysisIs frequent voluntary use of an ePRO tool associated with a reduction in symptom distress of patients with cancer?
One-way between-group ANOVA
31
AIM
1: e
PR
O T
OO
L USEDependent Variable
• SDS15 score• Range: 15 - 60
Independent Variable• Voluntary Use• 3 levels: 0, 1, ≥2 uses
32
AIM
1: e
PR
O T
OO
L USE
RQ 1.2 AnalysisIs frequent voluntary use of an ePRO tool associated with a reduction in symptom distress of patients with cancer?
One-way between-group ANOVA
Frequent users (≥2 uses) had significantly lower end-of-study symptom distress scores than those with just 1 use (p < .05)
32
AIM
1: e
PR
O T
OO
L USEDependent Variable
• SDS15 score• Range: 15 - 60
Independent Variable• Voluntary Use• 3 levels: 0, 1, ≥2 uses
Symptom Distress, by Use GroupA
IM 1
: ePR
O T
OO
L USE
S1 S2 S3 S420
21
22
23
24
25
26
27
28
29
30
No Use (n=123) 1 Use (n=92) ≥2 Uses (n=74)
Sym
pto
m D
istr
ess
(SD
S15
Sco
re)
S1 v S2 v S3 v S4
Study Time Points
Voluntary Sessions
S
v
Time Points over the Course of the Study
33
Aim 1 Summary
34
AIM
1: e
PR
O T
OO
L USE
• Low overall voluntary use of ePRO tool
• Frequent users had lower end-of-study symptom distress than those with 1 use
• Future work to identify reasons for RCT effect
Limitations
• No data on acceptability of features
• Varied length of treatment
• Focus on a general symptom measure
35
AIM
1: e
PR
O T
OO
L USE
Self-Tracking in Cancer Care
MOTIVATION: Why Track Symptoms in Cancer
Care?
AIM 1: ePRO Tool Use and Symptom Distress
AIM 2: Patient-Driven Self-Tracking
MODEL: Design Considerations for Tracking Tools
CONTRIBUTION & FUTURE WORK36
DIS
SERTA
TIO
N S
UM
MA
RY
Aim 2 Research Questions
2.1 What are barriers to self-tracking during cancer care?
2.2 How does actual use of tracking tools benefit patients?
37
AIM
2: PA
TIE
NT T
RA
CK
ING
Data Collection Methods
“In-the-Wild” Field Study (n=15)• home & clinic
observations• interviews• questionnaires
38
AIM
2: PA
TIE
NT T
RA
CK
ING
“Technology Probe” Study (n=10)• tool use logs• clinic observations• interviews• questionnaires
Inclusion criteria: Women with breast cancer
Unruh et al, CHI 2010 Klasnja et al, CHI 2010
HealthWeaver “Check-in” Entry
39
Web Mobile
AIM
2: PA
TIE
NT T
RA
CK
ING
HealthWeaver Graphing
40
AIM
2: PA
TIE
NT T
RA
CK
ING
Open Coding Analysis Themes
• Health issues & metrics• E.g., nausea, anxiety
• Tracking behavior• E.g., sporadically in notebooks
• Barriers to self-tracking in the wild
• Benefits of self-tracking with HealthWeaver
41
AIM
2: PA
TIE
NT T
RA
CK
ING
Findings: Tracking with cancer
Barriers “in the Wild”• Limited clinical guidance• Fragmentation of data• Time & energy burden
Benefits with HealthWeaver• Augmented memory• Psychosocial comfort• Communication support with clinicians
42
AIM
2: PA
TIE
NT T
RA
CK
ING
Patel et al., AMIA 2012
Barrier: Limited Clinical Guidance• Patients use memory to recall symptoms
• Clinicians recommend few metrics to track
43
P8’s drain log
AIM
2: PA
TIE
NT T
RA
CK
ING
Barrier: Fragmentation of Data
• Paper, MS Office used to track
• Difficult to reflect
• Data unified by just 1 participant
44P9’s notebook
AIM
2: PA
TIE
NT T
RA
CK
ING
HealthWeaver Tracking Usage
45
Metrics required for study
Default metrics in HealthWeaver
Average metrics tracked = 8.8(n=10)
AIM
2: PA
TIE
NT T
RA
CK
ING
Benefit: Augmenting Patterns
P19: “So I was able to look back and see, I wasn’t feeling this bad, what’s going on now?”
46
memory
support
AIM
2: PA
TIE
NT T
RA
CK
ING
Benefit: Communication Support with Clinicians
P17: “I was able to show [my doctor] that my hip was getting worse over time and that she should take it a little more seriously, [given] the fact I had it for day after day after day and I could show her what was going on.”
47
AIM
2: PA
TIE
NT T
RA
CK
ING
Patient priorities & data
Benefit: Psychosocial Comfort
P23: “…[documenting] something good that happened, any new news, and good news, might be helpful to go back and remember that there have been improvements.”
48
AIM
2: PA
TIE
NT T
RA
CK
ING
Design Implications
• Provide pre-populated metrics
• Provide customizable metrics
• Facilitate reflection and communication with clinicians
• Support patient ownership of tracking process
49Patel et al, AMIA 2012
AIM
2: PA
TIE
NT T
RA
CK
ING
Aim 2 Summary
• High use of personal informatics tracking tool
• Unexpected benefits of self-tracking
• Design implications drawn from benefits and barriers
50
AIM
2: PA
TIE
NT T
RA
CK
ING
Self-Tracking in Cancer Care
MOTIVATION: Why Track Symptoms in Cancer
Care?
AIM 1: ePRO Tool Use and Symptom Distress
AIM 2: Patient-Driven Self-Tracking
MODEL: Design Considerations for Tracking Tools
CONTRIBUTION & FUTURE WORK51
DIS
SERTA
TIO
N S
UM
MA
RY
52
How do we design tracking tools that are used and
accepted by both patients and clinicians in standard cancer
care?
CO
NC
EPTU
AL M
OD
EL
Why are Tracking Tools Not Actually Used in Standard Cancer Care?
“The approaches that are being used to develop eHealth technologies are not productive enough to create technologies that are meaningful, manageable, and sustainable.”
- Julia van Gemert-Pijnen
University of Twente, Netherlands
53
CO
NC
EPTU
AL M
OD
EL
Theories Informing Use and Acceptance of Tracking Tools
• Technology Acceptance Model (TAM)
• Derivations of TAM
• Personal Informatics Stage-Based
Model
54
CO
NC
EPTU
AL M
OD
EL
Technology Acceptance Model (TAM)
55
Davis 1989
CO
NC
EPTU
AL M
OD
EL
Continued Use ModelC
ON
CEPTU
AL M
OD
EL
56
Kim & Malhotra 2005
Unified Theory of Acceptance and Use of Technology (UTAUT)
CO
NC
EPTU
AL M
OD
EL
57
Venkatesh 2003
Issues with TAM and its Derivations
• Changing facilitators affect continued
use
• Focuses on environment and user
conditions, not technology design
58
CO
NC
EPTU
AL M
OD
EL
Stage-Based Model of Personal Informatics Systems
59
CO
NC
EPTU
AL M
OD
EL
Li et al, CHI 2010
Issues with the Stage-based Model
• Missing properties of tracking tools
• No clinician representation
60
CO
NC
EPTU
AL M
OD
EL
61
CO
NC
EPTU
AL M
OD
EL
Marypatient withbreast cancer
TRACKING TOOL
Dimensions• Modality• General vs.
Condition-specific• Manual vs.
Automatic• Universal vs.
Personalized• Integration with EHR
TRACKING TOOL
Dimensions• Structure of Data• Clinical Relevance• Completeness• Type of Vocabulary• Actual vs.
Estimated• Timing of Capture• Private vs. Shared
DATA
TRACKING TOOL
Patient Priorities
PATIENTDimensions• Symptom Distress• Behavioral
Intention• Comfort with
Technology
DATA
TRACKING TOOL
Patient Priorities
PATIENTDimensions• Symptom Distress• Behavioral
Intention• Comfort with
Technology
DATA
CLINICIANDimensions• Specialization• Behavioral
Intention• Comfort with
Technology
Clinician Priorities
TRACKING TOOL
Patient Priorities
DATA
Clinician Priorities
PATIENT CLINICIAN
TRACKING TOOL
Patient Priorities
DATA
Clinician Priorities
ACCEPTANCE
PATIENT CLINICIAN
TRACKING TOOL
Patient Priorities
DATA
Clinician Priorities
ACCEPTANCE
ACCEPTANCE
PATIENT CLINICIAN
Self-Tracking in Cancer Care
MOTIVATION: Why Track Symptoms in Cancer
Care?
AIM 1: ePRO Tool Use and Symptom Distress
AIM 2: Patient-Driven Self-Tracking
MODEL: Design Considerations for Tracking Tools
CONTRIBUTION & FUTURE WORK69
DIS
SERTA
TIO
N S
UM
MA
RY
Contribution to Health Informatics
• Uses a larger sample of voluntary ePRO tool use than prior studies
• Supports convergence of multiple types of tracking tools
• Considers how to integrate patient-driven tracking tools into healthcare
• Introduces a new model that has implications for future tracking tool design
70
CO
NTR
IBU
TIO
N
Contribution to Human-Computer Interaction
• Provides tracking tool design considerations for people with serious illnesses like cancer
71
CO
NTR
IBU
TIO
N
Future Work
• Validate model
• Interviews with patients and clinicians
• Surveys
• Design new tracking tools for cancer care
72
FUTU
RE W
OR
K
Thank You!
CommitteeWanda Pratt, PhDDonna Berry, RN, PhDPaul Gorman, MDTom Payne, MDBeth Devine, PharmD, PhD
Participants in studiesNIH R01 GrantsNLM Informatics Fellowship
73
AC
KN
OW
LED
GEM
EN
TS
Pedja KlasnjaAndrea HartzlerEun Kyoung ChoeSharbani RoyLauren Wilcox-PattersonLeila ZelnickNadia AkhtarRachel HanischLaurence RohmerSarah MennickenBas de VeerSameer HalaiJared BauerAlan Au
Persona images:Courtesy of Limeade
74
Deepa, Alpa, Payal, NeelamDasha & AlisherMichelleAishaShannonMary CzDUBMSR summer interns ‘12, ‘13FHCRC Communicating for the CureBHI-2008, BHIstudent @ UWTito’s asado crewSoccer friendsHoldem @ homeFellow NLM fellowsWISH colleagues
iMed lab
AC
KN
OW
LED
GEM
EN
TS
Questions? Rupa Patel [email protected]
Regina Holliday, Artist & Patient Advocate, Washington, DC
RQ2Is frequent voluntary use of an ePRO tool associated with a reduction in symptom distress of patients with cancer?
76
AIM
1: V
OLU
NTA
RY U
SE
“Self-tracking” defined
Awareness of bodily symptoms and their
impact on daily activities and cognitive
processes that is captured either through
measurement or observations and self-report
77
RELA
TED
WO
RK
RQ2
78
AIM
1: e
PR
O T
OO
L USE
Frequent users’ symptom distress was almost significantly higher in voluntary uses between T2 and T3 study time points (p < .07)
TRACKING TOOL
Dimensions• Modality• General vs. Condition-
specific• Manual vs. Automatic• Universal vs. Personalized• Integration with EHR
Dimensions• Structure of Data• Clinical Relevance• Completeness• Type of Vocabulary• Actual vs. Estimated• Timing of Capture• Private vs. Shared
Patient Priorities
DATAClinician Priorities
ACCEPTANCE
ACCEPTANCE
PATIENTDimensions• Symptom Distress• Behavioral Intention• Comfort with
Technology
CLINICIANDimensions• Specialization• Behavioral Intention• Comfort with
Technology