O slideshow foi denunciado.
Utilizamos seu perfil e dados de atividades no LinkedIn para personalizar e exibir anúncios mais relevantes. Altere suas preferências de anúncios quando desejar.
Designing for Use and
Acceptance of Tracking
Tools in Cancer Care
Rupa Patel
Biomedical & Health Informatics
Oral Disserta...
Self-Tracking in Cancer Care
MOTIVATION: Why Track Symptoms in Cancer Care?
AIM 1: ePRO Tool Use and Symptom Distress
AIM ...
MOTIVATION: Why Track Symptoms in Cancer Care?
AIM 1: ePRO Tool Use and Symptom Distress
AIM 2: Patient-Driven Self-Tracki...
4
MOTIVATION
Mary
patient with
breast
cancer
Thumb infection
Insomnia
Pain
Unstable glucose level
Weight loss
Fatigue & Nausea
Hives Dry cough
High blood pressure
Swel...
Symptom Communication
6
Nausea
Fatigue
Pain
Weight loss
MOTIVATION
{ CLINIC VISIT }
7
MOTIVATION
Nausea
Fatigue
Pain
Weight loss
Swelling Neuropathy
Symptom Communication
{ CLINIC VISIT }
8
Thumb infection
Vaginal dryness
Dry cough
Anxiety
..
MOTIVATION
Nausea
Fatigue
Pain
Weight loss
Swelling Neuropathy
Symp...
• Care based on continuous
healing relationships
• Shared knowledge and the free
flow of information
• Personalization bas...
Can Tracking Tools Help?
10
MOTIVATION
Cooking Hacks Website http://www.cooking-hacks.com/index.php/documentation/tutorial...
Researcher/Clinician-Driven
• Patient-Reported Outcome
• Ecological Momentary Assessment
Patient-Driven
• Personal Informa...
Abernethy, AP et al.
(2008). Health Serv
Res 43(6): 1975-91.
Patient-Reported Outcome
Instrument: FACT-G
12
MOTIVATION
Cel...
ESRA-C2
13
MOTIVATION
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 ...
Ecological MomentaryAssessment
• Study of human behavior
in daily life
• Random or periodic
reminders to track
15
Stone, S...
Benefits of Ecological Momentary
Assessment Tools
• Limited recency effects
• Improved event recall
• Capture of mood and ...
• Patient-initiated tracking
• One generates data for personal insight and
action
17
MOTIVATION
Personal InformaticsPInf
Benefits of Personal Informatics
Self-Tracking
• Clinic adoption not needed
• Wide selection of apps and devices
• Consume...
Barriers to Tracking Tool Use
19
MOTIVATION
Retrospective recall X
Data integration X X
User burden X X X
Interruptions X
...
U.S. Symptom Tracking Habits
7 in 10 adults track a health indicator
• 49% of trackers keep track “in their heads”
• 34% o...
21
How do we design tracking tools
that are used and accepted by both
patients and clinicians in standard
cancer care?
MOT...
Self-Tracking in Cancer Care
MOTIVATION: Why Track Symptoms in Cancer Care?
AIM 1: ePRO Tool Use and Symptom Distress
AIM ...
Aim 1 Research Questions
1.1 How often do patients with cancer
voluntarily use an ePRO tool?
1.2 Is frequent voluntary use...
Data: Intervention Group (n=372) from
Randomized Controlled Trial
Intervention
• Voluntary access to ESRA-C2 ePRO Assessme...
ESRA-C2
25
AIM1:ePROTOOLUSE
Symptom Assessment
Report View
Teaching TipsBerry, ASCO 2012
Symptom Assessments in ESRA-C2
Questionnaires
• Symptom Distress (SDS15)
• Depression (PHQ-9)
• Quality of Life (EORCTC-QL...
Data Collection Procedures
27
AIM1:ePROTOOLUSE
Study Time Points
• Symptom assessment
• Reminder to take assessment
• Clin...
RQ 1.1 Analysis Methods
How often do patients with cancer voluntarily use
an ePRO tool?
• Descriptive statistics
• Conting...
Overall Use of ESRA-C2
29
AIM1:ePROTOOLUSE
0
100
200
300
400
S1
V1.1
V1.2
V1.3
V1.4
V1.5
V1.6
S2
V2.1
V2.2
V2.3
S3
V3.1
V3...
Voluntary SessionsAre Less Likely to
Include Completed Symptom Distress
(SDS15)Assessments
Fisher‟sexact test (p < .001)
3...
31
AIM1:ePROTOOLUSE
RQ 1.2 Analysis
Is frequent voluntary use of an ePRO tool
associated with a reduction in symptom
distr...
32
AIM1:ePROTOOLUSE
RQ 1.2 Analysis
Is frequent voluntary use of an ePRO tool
associated with a reduction in symptom
distr...
Symptom Distress, by Use Group
AIM1:ePROTOOLUSE
20
22
24
26
28
30
S1 S2 S3 S4
No Use (n=123)
1 Use (n=92)
≥2 Uses (n=74)
S...
Aim 1 Summary
34
AIM1:ePROTOOLUSE
• Low overall voluntary use of ePRO tool
• Frequent users had lower end-of-study
symptom...
Limitations
• No data on acceptability of features
• Varied length of treatment
• Focus on a general symptom measure
35
AI...
Self-Tracking in Cancer Care
MOTIVATION: Why Track Symptoms in Cancer Care?
AIM 1: ePRO Tool Use and Symptom Distress
AIM ...
Aim 2 Research Questions
2.1 What are barriers to self-tracking during
cancer care?
2.2 How does actual use of tracking to...
Data Collection Methods
“In-the-Wild” Field Study
(n=15)
• home & clinic
observations
• interviews
• questionnaires
38
AIM...
HealthWeaver “Check-in” Entry
39
Web Mobile
AIM2:PATIENTTRACKING
HealthWeaver Graphing
40
AIM2:PATIENTTRACKING
Open Coding Analysis Themes
• Health issues & metrics
• E.g., nausea, anxiety
• Tracking behavior
• E.g., sporadically in ...
Findings: Tracking with cancer
Barriers “in the Wild”
• Limited clinical guidance
• Fragmentation of data
• Time & energy ...
Barrier: Limited Clinical Guidance
• Patients use memory to recall symptoms
• Clinicians recommend few metrics to track
43...
Barrier: Fragmentation of Data
• Paper, MS Office
used to track
• Difficult to reflect
• Data unified by just 1
participan...
HealthWeaver Tracking Usage
45
Metrics required for
study
Default metrics in
HealthWeaver
Average metrics
tracked = 8.8
(n...
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...
Benefit: Communication Support with
Clinicians
P17: “I was able to show [my doctor] that my hip was
getting worse over tim...
Benefit: Psychosocial Comfort
P23: “…[documenting] something good that
happened, any new news, and good news, might
be hel...
Design Implications
• Provide pre-populated metrics
• Provide customizable metrics
• Facilitate reflection and communicati...
Aim 2 Summary
• High use of personal informatics tracking tool
• Unexpected benefits of self-tracking
• Design implication...
Self-Tracking in Cancer Care
MOTIVATION: Why Track Symptoms in Cancer Care?
AIM 1: ePRO Tool Use and Symptom Distress
AIM ...
52
How do we design tracking tools
that are used and accepted by both
patients and clinicians in standard
cancer care?
CON...
Why are Tracking Tools Not Actually
Used in Standard Cancer Care?
“The approaches that are being used to develop
eHealth t...
Theories Informing Use and
Acceptance of Tracking Tools
• Technology Acceptance Model (TAM)
• Derivations of TAM
• Persona...
Technology Acceptance Model (TAM)
55
Davis 1989
CONCEPTUALMODEL
Continued Use Model
CONCEPTUALMODEL
56
Kim & Malhotra 2005
Unified Theory of Acceptance and
Use of Technology (UTAUT)
CONCEPTUALMODEL
57
Venkatesh 2003
Issues with TAM and its Derivations
• Changing facilitators affect continued use
• Focuses on environment and user
conditi...
Stage-Based Model of Personal
Informatics Systems
59
CONCEPTUALMODEL
Li et al, CHI 2010
Issues with the Stage-based Model
• Missing properties of tracking tools
• No clinician representation
60
CONCEPTUALMODEL
61
CONCEPTUALMODEL
Mary
patient with
breast
cancer
TRACKING TOOL
Dimensions
• Modality
• General vs. Condition-
specific
• Manual vs. Automatic
• Universal vs.
Personalized
...
TRACKING TOOL
Dimensions
• Structure of Data
• Clinical Relevance
• Completeness
• Type of Vocabulary
• Actual vs. Estimat...
TRACKING TOOL
Patient
Priorities
PATIENT
Dimensions
• Symptom Distress
• Behavioral Intention
• Comfort with
Technology
DA...
TRACKING TOOL
Patient
Priorities
PATIENT
Dimensions
• Symptom Distress
• Behavioral Intention
• Comfort with
Technology
DA...
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 ...
Contribution to Health Informatics
• Uses a larger sample of voluntary ePRO tool
use than prior studies
• Supports converg...
Contribution to Human-Computer
Interaction
• Provides tracking tool design considerations
for people with serious illnesse...
Future Work
• Validate model
• Interviews with patients and clinicians
• Surveys
• Design new tracking tools for cancer ca...
Thank You!
Committee
Wanda Pratt, PhD
Donna Berry, RN, PhD
Paul Gorman, MD
Tom Payne, MD
Beth Devine, PharmD, PhD
Particip...
Pedja Klasnja
Andrea Hartzler
Eun Kyoung Choe
Sharbani Roy
Lauren Wilcox-Patterson
Leila Zelnick
Nadia Akhtar
Rachel Hanis...
Questions? Rupa Patel rupatel@uw.edu
Regina Holliday, Artist & Patient Advocate, Washington, DC
RQ2
Is frequent voluntary use of an ePRO tool
associated with a reduction in symptom distress
of patients with cancer?
76
...
“Self-tracking” defined
Awareness of bodily symptoms and their impact on
daily activities and cognitive processes that is
...
RQ2
78
AIM1:ePROTOOLUSE
Frequent users‟ symptom distress was almost
significantly higher in voluntary uses between T2 and ...
TRACKING TOOL
Dimensions
• Modality
• General vs. Condition-specific
• Manual vs. Automatic
• Universal vs. Personalized
•...
Próximos SlideShares
Carregando em…5
×

Rupa Patel's Ph.D. Dissertation Defense, UW Biomedical & Health Informatics

Patients with cancer experience many unanticipated symptoms and struggle to communicate them to clinicians during treatment. They contend with a variety of symptoms at home—issues stemming from cancer progression, treatment regimens, and co-morbidities. Although many patients rely on clinic visits to get help with managing these symptoms, clinicians often underestimate the intensity of patients' symptoms or miss them altogether.

A proliferation of mobile and sensor-based tools, which enable self-tracking, leads us to consider how to approach their design to support cancer symptom management. However, tracking tools are not widely used and accepted in cancer care. To further study use of tracking tools, I analyzed the use of two different types of manual tracking tools: (1) ESRA-C2, an electronic Patient-Reported Outcome (ePRO) tool deployed to 372 people with cancer; and (2) HealthWeaver, a personal informatics tool deployed as a technology probe to 10 women with breast cancer. Also, I analyzed the “in-the-wild” self-tracking practices of the 10 women before they used HealthWeaver, as well as 15 other women with breast cancer. Results showed that patients who voluntarily used the ePRO tool the most frequently had relatively low symptom distress. In addition, although patients’ tracking behaviors “in the wild” were fragmented and sporadic, these behaviors with a personal informatics tool were more consistent. Participants also used tracked data to see patterns among symptoms, feel psychosocial comfort, and improve symptom communication with clinicians. Given these considerations, I describe a new conceptual model that has implications for patients, clinicians, and tool developers. If patients and clinicians accept and integrate tracking tools into cancer symptom management away from the clinic, we can move closer to continuous healing relationships that are the cornerstone of effective care.

  • Entre para ver os comentários

Rupa Patel's Ph.D. Dissertation Defense, UW Biomedical & Health Informatics

  1. 1. Designing for Use and Acceptance of Tracking Tools in Cancer Care Rupa Patel Biomedical & Health Informatics Oral Dissertation Defense August 6, 2013
  2. 2. 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 WORK 2 DISSERTATIONSUMMARY
  3. 3. 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 WORK 3 DISSERTATIONSUMMARY Self-Tracking in Cancer Care
  4. 4. 4 MOTIVATION Mary patient with breast cancer
  5. 5. Thumb infection Insomnia Pain Unstable glucose level Weight loss Fatigue & Nausea Hives Dry cough High blood pressure Swelling 5
  6. 6. Symptom Communication 6 Nausea Fatigue Pain Weight loss MOTIVATION { CLINIC VISIT }
  7. 7. 7 MOTIVATION Nausea Fatigue Pain Weight loss Swelling Neuropathy Symptom Communication { CLINIC VISIT }
  8. 8. 8 Thumb infection Vaginal dryness Dry cough Anxiety .. MOTIVATION Nausea Fatigue Pain Weight loss Swelling Neuropathy Symptom Communication { CLINIC VISIT }
  9. 9. • 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 MOTIVATION Communication Needs in Cancer Care
  10. 10. Can Tracking Tools Help? 10 MOTIVATION Cooking Hacks Website http://www.cooking-hacks.com/index.php/documentation/tutorials/ehealth- biometric-sensor-platform-arduino-raspberry-pi-medical
  11. 11. Researcher/Clinician-Driven • Patient-Reported Outcome • Ecological Momentary Assessment Patient-Driven • Personal Informatics Self-Tracking 11 MOTIVATION Tracking Tools Across Fields PRO EMA PInf
  12. 12. Abernethy, AP et al. (2008). Health Serv Res 43(6): 1975-91. Patient-Reported Outcome Instrument: FACT-G 12 MOTIVATION Cella et al, Journal of Clinical Oncology, 1993 PRO
  13. 13. ESRA-C2 13 MOTIVATION Symptom Assessment Report View Teaching TipsBerry, ASCO 2012. PRO
  14. 14. Benefits of Patient-Reported Outcome Tools • Improved health outcomes1,2 • Clinician awareness of symptoms3,4 • Timing of symptom reporting5 14 MOTIVATION 1Velikova, Journal of Clinical Oncology 2004; 2Detmar JAMA 2002 3Berry, Journal of Clinical Oncology 2011 4Ruland, JAMIA 2010 5Cleeland, Journal of Clinical Oncology 2011 PRO
  15. 15. Ecological MomentaryAssessment • Study of human behavior in daily life • Random or periodic reminders to track 15 Stone, Shifman, et al “The Science of Real-time Data Capture” 2007 MOTIVATION EMA
  16. 16. Benefits of Ecological Momentary Assessment Tools • Limited recency effects • Improved event recall • Capture of mood and context 16 MOTIVATION Stone, Shifman, et al “The Science of Real-time Data Capture” 2007 EMA
  17. 17. • Patient-initiated tracking • One generates data for personal insight and action 17 MOTIVATION Personal InformaticsPInf
  18. 18. Benefits of Personal Informatics Self-Tracking • Clinic adoption not needed • Wide selection of apps and devices • Consumer-facing interface design 18 MOTIVATION PInf
  19. 19. Barriers to Tracking Tool Use 19 MOTIVATION 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
  20. 20. 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, 2009 20 MOTIVATION
  21. 21. 21 How do we design tracking tools that are used and accepted by both patients and clinicians in standard cancer care? MOTIVATION
  22. 22. 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 WORK 22 DISSERTATIONSUMMARY
  23. 23. 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 AIM1:ePROTOOLUSE
  24. 24. Data: Intervention Group (n=372) from Randomized Controlled Trial Intervention • Voluntary access to ESRA-C2 ePRO Assessment- taking sessions at any time • Access to Teaching Tips/Report Views at Study Time Points Inclusion criteria • Any cancer • Enrollment prior to treatment start • Adults 18+ 24 AIM1:ePROTOOLUSE Berry, ASCO 2012
  25. 25. ESRA-C2 25 AIM1:ePROTOOLUSE Symptom Assessment Report View Teaching TipsBerry, ASCO 2012
  26. 26. Symptom Assessments in ESRA-C2 Questionnaires • 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 points 30 total Symptom & Quality of Life Issues (SQLI) 26 AIM1:ePROTOOLUSE Berry, ASCO 2012
  27. 27. Data Collection Procedures 27 AIM1:ePROTOOLUSE 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- treatment Visit S2 6-8 weeks after treatment start S3 2-4 weeks after treatment end date S4 Berry, ASCO 2012
  28. 28. 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 AIM1:ePROTOOLUSE
  29. 29. Overall Use of ESRA-C2 29 AIM1:ePROTOOLUSE 0 100 200 300 400 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 FrequencyofSessions S1 S2 S3 S4 Study Time Points Voluntary Sessions First on- treatment 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
  30. 30. Voluntary SessionsAre Less Likely to Include Completed Symptom Distress (SDS15)Assessments Fisher‟sexact test (p < .001) 30 Frequency of Sessions with Complete SDS15 Frequency of Sessions without Complete SDS15 % of Complete SDS15 from All Sessions Study Assessment-Taking Sessions 1016 89 91.9% Voluntary Assessment-Taking Sessions 135 43 75.8% AIM1:ePROTOOLUSE S v
  31. 31. 31 AIM1:ePROTOOLUSE RQ 1.2 Analysis Is frequent voluntary use of an ePRO tool associated with a reduction in symptom distress of patients with cancer? One-way between-group ANOVA 31 AIM1:ePROTOOLUSE Dependent Variable • SDS15 score • Range: 15 - 60 Independent Variable • Voluntary Use • 3 levels: 0, 1, ≥2 uses
  32. 32. 32 AIM1:ePROTOOLUSE RQ 1.2 Analysis Is 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 AIM1:ePROTOOLUSE Dependent Variable • SDS15 score • Range: 15 - 60 Independent Variable • Voluntary Use • 3 levels: 0, 1, ≥2 uses
  33. 33. Symptom Distress, by Use Group AIM1:ePROTOOLUSE 20 22 24 26 28 30 S1 S2 S3 S4 No Use (n=123) 1 Use (n=92) ≥2 Uses (n=74) SymptomDistress(SDS15Score) S1 v S2 v S3 v S4 Study Time Points Voluntary Sessions S v Time Points over the Course of the Study 33
  34. 34. Aim 1 Summary 34 AIM1:ePROTOOLUSE • 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
  35. 35. Limitations • No data on acceptability of features • Varied length of treatment • Focus on a general symptom measure 35 AIM1:ePROTOOLUSE
  36. 36. 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 WORK 36 DISSERTATIONSUMMARY
  37. 37. 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 AIM2:PATIENTTRACKING
  38. 38. Data Collection Methods “In-the-Wild” Field Study (n=15) • home & clinic observations • interviews • questionnaires 38 AIM2:PATIENTTRACKING “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
  39. 39. HealthWeaver “Check-in” Entry 39 Web Mobile AIM2:PATIENTTRACKING
  40. 40. HealthWeaver Graphing 40 AIM2:PATIENTTRACKING
  41. 41. 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 AIM2:PATIENTTRACKING
  42. 42. 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 AIM2:PATIENTTRACKING Patel et al., AMIA 2012
  43. 43. Barrier: Limited Clinical Guidance • Patients use memory to recall symptoms • Clinicians recommend few metrics to track 43 P8‟s drain log AIM2:PATIENTTRACKING
  44. 44. Barrier: Fragmentation of Data • Paper, MS Office used to track • Difficult to reflect • Data unified by just 1 participant 44 P9‟s notebook AIM2:PATIENTTRACKING
  45. 45. HealthWeaver Tracking Usage 45 Metrics required for study Default metrics in HealthWeaver Average metrics tracked = 8.8 (n=10) AIM2:PATIENTTRACKING
  46. 46. 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 AIM2:PATIENTTRACKING
  47. 47. 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 AIM2:PATIENTTRACKING Patient priorities & data
  48. 48. 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 AIM2:PATIENTTRACKING
  49. 49. 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 AIM2:PATIENTTRACKING
  50. 50. Aim 2 Summary • High use of personal informatics tracking tool • Unexpected benefits of self-tracking • Design implications drawn from benefits and barriers 50 AIM2:PATIENTTRACKING
  51. 51. 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 WORK 51 DISSERTATIONSUMMARY
  52. 52. 52 How do we design tracking tools that are used and accepted by both patients and clinicians in standard cancer care? CONCEPTUALMODEL
  53. 53. 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 CONCEPTUALMODEL
  54. 54. Theories Informing Use and Acceptance of Tracking Tools • Technology Acceptance Model (TAM) • Derivations of TAM • Personal Informatics Stage-Based Model 54 CONCEPTUALMODEL
  55. 55. Technology Acceptance Model (TAM) 55 Davis 1989 CONCEPTUALMODEL
  56. 56. Continued Use Model CONCEPTUALMODEL 56 Kim & Malhotra 2005
  57. 57. Unified Theory of Acceptance and Use of Technology (UTAUT) CONCEPTUALMODEL 57 Venkatesh 2003
  58. 58. Issues with TAM and its Derivations • Changing facilitators affect continued use • Focuses on environment and user conditions, not technology design 58 CONCEPTUALMODEL
  59. 59. Stage-Based Model of Personal Informatics Systems 59 CONCEPTUALMODEL Li et al, CHI 2010
  60. 60. Issues with the Stage-based Model • Missing properties of tracking tools • No clinician representation 60 CONCEPTUALMODEL
  61. 61. 61 CONCEPTUALMODEL Mary patient with breast cancer
  62. 62. TRACKING TOOL Dimensions • Modality • General vs. Condition- specific • Manual vs. Automatic • Universal vs. Personalized • Integration with EHR
  63. 63. TRACKING TOOL Dimensions • Structure of Data • Clinical Relevance • Completeness • Type of Vocabulary • Actual vs. Estimated • Timing of Capture • Private vs. Shared DATA
  64. 64. TRACKING TOOL Patient Priorities PATIENT Dimensions • Symptom Distress • Behavioral Intention • Comfort with Technology DATA
  65. 65. TRACKING TOOL Patient Priorities PATIENT Dimensions • Symptom Distress • Behavioral Intention • Comfort with Technology DATA CLINICIAN Dimensions • Specialization • Behavioral Intention • Comfort with Technology Clinician Priorities
  66. 66. TRACKING TOOL Patient Priorities DATA Clinician Priorities PATIENT CLINICIAN
  67. 67. TRACKING TOOL Patient Priorities DATA Clinician Priorities ACCEPTANCE PATIENT CLINICIAN
  68. 68. TRACKING TOOL Patient Priorities DATA Clinician Priorities ACCEPTANCE ACCEPTANCE PATIENT CLINICIAN
  69. 69. 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 WORK 69 DISSERTATIONSUMMARY
  70. 70. 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 CONTRIBUTION
  71. 71. Contribution to Human-Computer Interaction • Provides tracking tool design considerations for people with serious illnesses like cancer 71 CONTRIBUTION
  72. 72. Future Work • Validate model • Interviews with patients and clinicians • Surveys • Design new tracking tools for cancer care 72 FUTUREWORK
  73. 73. Thank You! Committee Wanda Pratt, PhD Donna Berry, RN, PhD Paul Gorman, MD Tom Payne, MD Beth Devine, PharmD, PhD Participants in studies NIH R01 Grants NLM Informatics Fellowship 73 ACKNOWLEDGEMENTS
  74. 74. Pedja Klasnja Andrea Hartzler Eun Kyoung Choe Sharbani Roy Lauren Wilcox-Patterson Leila Zelnick Nadia Akhtar Rachel Hanisch Laurence Rohmer Sarah Mennicken Bas de Veer Sameer Halai Jared Bauer Alan Au Persona images: Courtesy of Limeade 74 Deepa, Alpa, Payal, Neelam Dasha & Alisher Michelle Aisha Shannon Mary Cz DUB MSR summer interns „12, „13 FHCRC Communicating for the Cure BHI-2008, BHIstudent @ UW Tito‟s asado crew Soccer friends Holdem @ home Fellow NLM fellows WISH colleagues iMed lab ACKNOWLEDGEMENTS
  75. 75. Questions? Rupa Patel rupatel@uw.edu Regina Holliday, Artist & Patient Advocate, Washington, DC
  76. 76. RQ2 Is frequent voluntary use of an ePRO tool associated with a reduction in symptom distress of patients with cancer? 76 AIM1:VOLUNTARYUSE
  77. 77. “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 RELATEDWORK
  78. 78. RQ2 78 AIM1:ePROTOOLUSE Frequent users‟ symptom distress was almost significantly higher in voluntary uses between T2 and T3 study time points (p < .07)
  79. 79. 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 DATA Clinician Priorities ACCEPTANCE ACCEPTANCE PATIENT Dimensions • Symptom Distress • Behavioral Intention • Comfort with Technology CLINICIAN Dimensions • Specialization • Behavioral Intention • Comfort with Technology

×