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: jfeder@brodeur.com

ameier@email.unc.edu

Thank you!

Andrea Meier, Bret Shaw, Judy Feder, & Eulàlia Puig Abril