Download - E-patients Communities and Chronic Illness
E-patients Communities and Chronic Illness:
Needs assessment and design implications of breast cancer, multiple sclerosis and Marfan syndrome health e-communities
Toronto, 2009
Exploratory Study: Aims
Understand e-health information and support seeking behavior among diverse types of chronic disease patients
Assess patients’ preferences for Web 2.0 resources
Inform design of future web-based support communities
Toronto, 2009
Method Interactive web-based survey (May-June 2008) Recruitment targets: approximately 12 e-health
communities, 9 of which responded to our initial request
Convenience sample: Members of 3 e-health communities: metastatic breast cancer (n=62), multiple sclerosis (n=31), and Marfan syndrome (n=35)
154 unique starts/ 127 completed (82%) Analyses
Whole group descriptive analyses Planned between group comparisons
Toronto, 2009
Survey Topics Participant characteristics
Age, gender, education, employment status,, support network characteristics
Self reports of chronic condition(s) and health self ratings
Recent Internet searches for information on own chronic condition(s) in past 30 days
Ease or difficulty finding information on Treatments, information from experts, health-related support,
and other relevant types of information
Activities patients would like to be able to do online to enhance their coping with chronic condition(s)
Willingness to have personal information shared
Preferences for different kinds of web functionality
Toronto, 2009
Convenience sample tapped a wide range of chronic illness experiences
Relatively common--rare diseases Risk factors: environmental--heritable Ages of onset: birth--later adulthood Systems affected Expected life spans Sources of uncertainty
Difficulties in diagnosing Patterns of disease progression
Toronto, 2009
Sample characteristics
Characteristics%
(N=127)
Female 96%
Adults: 40-59 y,o. 62%
Euro-American 96%
Education: High school diploma or higher
98%
Employment
Full time 31.0%
Part time 9.5%
Unemployed 29.4%
Retired/Disabled 31.0%
Health status
Self rating: Fair 54.4%
Chronic disease co-morbidity 39.3% >1 diagnosis
Toronto, 2009
Commonalities in e-health information and support seeking experiences
Few significant differences in: Health information seeking
experiences• Overall, Marfan patients reported somewhat more difficulty
finding the information they needed
E-health community support Desire to find true patient peers Interest in sharing “patient wisdom” with
broader healthcare community
Toronto, 2009
Results:Health information seekingN=127
Item (n=number who searched) n
Fairly/Very Easy to
Find%
Fairly/Very Hard
to Find%
Current treatments 111 77.5% 22.5 %
Treatment side effects 111 74.8% 25.2 %
Managing multiple chronic conditions
66 57.6% 42.4%
Recommendations for health care providers
53 28.3% 71.7%
Clinical trials 51 62.7% 37.3%
Toronto, 2009
Results: Searching for different types of online health information and resources (N=127)
Item (n=number who searched) n
Fairly/VeryEasy to Find
%
Fairly/VeryHard to Find
%
Comprehensive health info websites
110 84% 16%
Scientific articles in online journals
84 90% 10%
News articles 60 69% 31%
Products and services 54 57% 44%
Health insurance 38 21% 79%
Doctors’ presentations on the Web
33 43% 57%
Toronto, 2009
Social Contexts of e-Health Seeking:Sources of Support (N=127)
Other sources: neighbors, coworkers, in-home health care providers, unspecified others
Toronto, 2009
Results: Searching for OnlineSocial Support (N=127)
Item (n=number who searched)* n
Fairly/VeryEasy to Find
%
Fairly/VeryHard to Find
%
HeCs for my chronic condition 105 81% 19%
People going through same experiences 103 19% 81%
HeCs for my combination of multiple chronic conditions
46 65% 35%
People coping with depression and other chronic conditions
46 65% 35%
Toronto, 2009
Interest in Apomediated Activities(N=127)
Activity(n=number of respondents) n
Alreadydoing
%Interested
%
Unsure orUninterested
%
Share knowledge with a broader e-health community
110 83% 9% 8%
Buy products 110 17% 42% 41%
Write or contribute to a blog
112 15% 15% 70%
Create personal health profile
110 14% 32% 55%
Create detailed ehealth record
110 13% 40% 48%
Rate HC providers 110 7% 59% 34%
Toronto, 2009
Limitations
Small, convenience samplePilot surveySources of bias: gender, race, educationParticipants were all members of e-health
communitiesDid not probe on existing social networks,
Twitter, other social media sites
Toronto, 2009
Conclusions
Health seeking behavior was similar across diverse chronic disease groups
Patients in all groups want more specific, individualized and timely information
Most patients were interested in participating in apomediated activities, but fewer were doing them
Toronto, 2009
Verbatims:Participants value what they can learn from each other
Patients want: “sites that are collecting and publicizing patient recommendations for improvement of care”… “patient recommendations for doctors”… “data on underreported side effects.”
Patients value: “I have found the unedited, uncensored and non-statistical (e.g. anecdotal info available on [my HeC] to be as helpful or more helpful than the general sites (e.g, WebMD) because it is first hand, individual and specific.
Patients know the difference: “There is a glut of inspirational sites [with illness stories]. I would like to see sites that are collecting and publicizing patient recommendations for improving care…and other advocacy.”
Toronto, 2009
Patients recognize limitations, risks of “patient wisdom” ande-health resources
“I don’t believe privacy could be protected [with health record data] but I would still be willing to participate.”
“You will have major issues of selection bias. People who post are very different from those who don’t.”
Toronto, 2009
Implications for health e-community designs
Information sharing opportunities with “true patient peers”
Searchable, rich personal profiles
Presence functionality to detect members “like me”
Integration of social networks with PHR/EMR
Enablers of recruitment and formation of subgroups (e.g., combinations of chronic conditions)
Toronto, 2009
Implications for health e-community designs
Improved access to current research findings
Public access to peer-reviewed literature
Multimedia formats for communicating health information (i.e., YouTube videos, interactive webinars)
Access to research experts (webinars, Q&A)
Integrated research toolsHeC member-initiated researchClinical research studies
Toronto, 2009
Implications for health e-community designs
Information about and access to relevant products, services, and treatments
HCP rating systemsClinical trials Contextual e-advertising
Toronto, 2009
Questions, comments or feedback?
All welcome!Email: [email protected]
Thank you!
Andrea Meier, Bret Shaw, Judy Feder, & Eulàlia Puig Abril