Published on February 25, 2014
Patients and online networks – characteristics, motivators, behaviour! Ricardo Sousa! Vitanect co-founder and General Manager ! February 2014! 1
Synopsis! • Goal: To share insights about patient online behaviour! • Audience: all those interested in online patient networks! • Date: Feb 2014!
Why do patients go online?! Seek informa-on (on drugs, diseases, diagnosis) Seek treatment (which doctor, which hospital) Seek advice (how to live with, family issues, psychological support)
Who is there to help?! Seek informa-on (on drugs, dWikipedia iseases, WebMD diagnosis) Pa-ent groups Pharma Disease portals Seek treatment (which doctor, sites Hospital and clinic which hospital) Health systems sites Review sites Seek advice (how to live with, family Facebook?? issues, psychological Pa-ent support) Forums Groups
Patient-to-patient support: A gap in online information! • There are no reference websites worldwide! • Facebook is not a great alternative due to privacy concerns! • Missing vertical social network… but: conﬁdentiality ≠ virality?!
Perceived risks (by patients)! • Privacy! – Employers and colleagues! – Insurers, Banks! – Friends, family and neighbours! – Exposing their children! • Fake doctors! • Bad advice! • Business people making money out of them!
Caveat: what do we mean when we say “ online patients”?!
N x N population! • There is no “patients”: thousands of diseases and multiple stages by disease. People are different! – A Parkinson’s disease caregiver has little in common with a schizophrenia caregiver! – An early stage diabetes patient has little in common with an insulin-dependent diabetes patient!
No global village! • There is no “global village” for most patients! – NHS is in England. Germany uses another word and another system. Language, processes, patient experiences are different! – Specialist referral paths are different by country! – Drugs have different names…! – … and sometimes different indications!! – Clinical practice is often very different! – Language is a barrier even inside the same country!
Disease type and stage strongly inﬂuence online behaviour! Acute disease! Searching diagnosis and treatment, occasional! Chronic degenerative disease! Continuous online presence! Accident/Impairment! Event-based online presence, sometimes continuous!
Disease stage and patient concerns/information needs! Pre-care: Prevention/ Wellness! § Wellness solutions! § Selfdiagnosis / disease information! Point-of care: Diagnosis/ Consultation/ Procedure! § Provider search / matching! § Telemedicine! § eDiagnosis! § Remote care / patient-doctor link! Post-care: Medication! Post-care: Management/ Optimization! § Rx fulﬁllment! § Comprehensiv § Adherence! e disease § Vigilance! mgmt! § Follow-up / § Treatment Monitor! optimization! § Alerts! § Follow-up / Monitor! Further reading: IMS, Pa-ent Apps for Improved Healthcare, October 2013 http://ow.ly/tY0Xe and Swan, Emerging PatientDriven Health Care Models: An Examination of Health Social Networks, Consumer Personalized Medicine and Quantiﬁed Self-Tracking, Int J Environ Res Public Health. 2009 February; 6(2): 492–525. http://ow.ly/tYhKG !
Online behaviour depends on the patient type (attitudinal segmentation is needed)! Self-‐managers • Core quan-ﬁed self-‐users • App users • Data-‐tracking pioneers • Con-nuous engagement Find-‐me-‐a-‐ solu-on • Hospital / Doctor seeking • Rx advice seeking • Temporary engagement Give-‐me-‐the-‐ good-‐news • Occasional ac-vity • Low engagement Source: Vitanect research, 2013. For an alternative view on online patient segmentation, see mHealth in an mWorld - How mobile technology is transforming health care, Deloitte 2012 (link - page 6). “Segment ‘Online and Onboard’ corresponds to % of total population)!
Empirical observation – online patient behaviour in social networks!
Search behaviour: What are common searches today?! 4% 2% % of searches 16% Disease informa-on Symptoms Treatments 55% 23% Source: Vitanect internal data, Jan-Feb 2014! Medica-on informa-on Other
Motivations of patients seeking health information online via social health networks! Online survey, N=605 online social health site users. Cluster analysis:! Cluster: main mo3va3on User characteris3cs Acquiring informa-on and support over age 55 years, women, those with lower income, chronic pain, obesity and depression Communica-ng men, those 20–34 years old, those with less educa-on, or an ea-ng disorder Networking mul-ple sclerosis or depression Browsing mul-ple sclerosis Source: Magnezi R1, Grosberg D, Novikov I, Ziv A, Shani M, Freedman LS Characteristics of patients seeking health information online via social health networks versus general Internet sites: a comparative study, Inform Health Soc Care. 2014 Jan 29.! http://informahealthcare.com/doi/abs/10.3109/17538157.2013.879147 !
Patient self-tracking: how much is taking place?! • 70% of doctors report that at least one patient is sharing health-measurement data with them! • Most common methods:! – Hand writing the data! – Printout of the information! • ¾ of physicians agree that self-tracking leads to better outcomes! Source: Manhattan Research’s Taking the Pulse, US 2013, N=2950!
Are patients interested in self-tracking?! • Depends. A majority is not – they are interested in living a normal life. Patient segments:! – Self-managers: they carefully follow their disease, medication, progress. Interested in self-tracking. Minority! – Help-me-if-you-can: they want solutions and seek them. But diagnosis and treatment is the job of the health professionals. Big group! – Give me the good news: they intend to live normally and avoid actions that reminds them they are sick. Disengage. Big group.! • Test – which group do you belong?:! – Do you read the results of your blood tests carefully or do you hand it over to your doctor? ! – Do you forget taking your pills after 3 days?! – A doctor is someone you visited once in your life, or less! Further reading: See some additional data here by @susannahfox !
Empirical results: health-information seeking behaviour and social networking use! Online survey, N=1,745 online health information users. Results:! Factor Used online health rankings/reviews or health social networks Has chronic disease? Twice as likely [OR 2.09, P<.001). Formal educa-on Lower odds for less formal educa-on (OR 0.49, P<.001) Male / Female Lower odds for male (OR 0.71, P<.001) Income 1.5x as likely when higher incomes (OR 1.49, P=.05) Age Older respondents were less likely to use SNS (OR 0.96, P<.001 Regular health care provider 1.9x odds for users with a regular provider (OR 1.89, P<.001) Source: Thackeray R1, Crookston BT, West JH. Correlates of health-related social media use among adults. IJ Med Internet Res. 2013 Jan 30 http://www.jmir.org/2013/1/e21/ !
Are online patient populations comparable with populations in the clinical practice?! • Comparison between patientslikeme MS population (N=10,255) and MS center (N=4,039)! – Online population is younger (45 vs 48) ! – Higher % of females (80% vs 75%)! – Higher % of high education (26% completed college vs 12%)! – Good correlations between patient-reported MSRS composite and physician-derived measures! Source: Bove R, Secor E, Healy BC, Musallam A, Vaughan T, et al. (2013) Evaluation of an Online Platform for Multiple Sclerosis Research: Patient Description, Validation of Severity Scale, and Exploration of BMI Effects on Disease Course. PLoS ONE 8(3): e59707. doi:10.1371/journal.pone.0059707!
Emotion and data! • Disease is emotional. It is not a game, a contest, a cool new “space”: Emotional message is important! • The average patient is not a 25 year-old hispter! • Medicine and healthcare are often scientiﬁc areas with precise language!
Some empirical evidence on emotion and online behaviour – emotions matter! Online survey, N=525 posts by 116 participants in a cancer social network. Results:! Message content Result Higher word count More likely to receive a reply (OR 1.3 P=0.001) Fewer 2nd person pronouns (you, your, etc) More likely to receive a reply (OR 0.92 P=0.04) High level of posi-ve emo-on Less likely to receive a reply (OR 0.94, P=0.03) Topics with higher likelihood Self-‐disclosure (p < 0.001) of a reply Medical experiences (p = 0.01) Rela-onship issues (p = 0.05) Introductory posts (p < 0.01). Source: Lewallen AC1, Owen JE, Bantum EO, Stanton AL.. (2014) How language affects peer responsiveness in an online cancer support group: implications for treatment design and facilitation. http://www.ncbi.nlm.nih.gov/pubmed/24519856 !
Some empirical evidence on emotion and online behaviour – emotions matter, and can have detrimental impact for some patients! Online survey, N=18,064 posts by 236 patients in a breast cancer social network. Results:! Type of pa3ent Impact of giving and receiving emo3onal support in CMSS groups With higher emo-onal communica-on competence Posi-ve eﬀects on emo-onal well-‐being With lower emo-onal communica-on competence Detrimental impacts on emo-onal well-‐being Source: Yoo W1, Namkoong K, Choi M, Shah DV, Tsang S, Hong Y, Aguilar M, Gustafson DH.. (2014) Giving and Receiving Emotional Support Online: Communication Competence as a Moderator of Psychosocial Beneﬁts for Women with Breast Cancer, omput Human Behav. 2014 Jan;30:13-22. http://www.ncbi.nlm.nih.gov/pubmed/24058261 !
Behaviour modiﬁcation – are we close to affecting outcomes with online/mobile applications?! • Systematic review of 2,040 studies (2014) assessing the current level of evidence regarding the effectiveness of online social network health behaviour interventions.! – 10 studies met inclusion criteria! – 9 out of 10 reported signiﬁcant improvements in some aspect of health behaviour change! – Effect sizes for behaviour change in general were small in magnitude and statistically non-signiﬁcant! Source: Maher CA1, Lewis LK, Ferrar K, Marshall S, De Bourdeaudhuij I, Vandelanotte C. (2014) Are Health Behavior Change Interventions That Use Online Social Networks Effective? A Systematic Review. J Med Internet Res. 2014 Feb 14;16(2):e40.!
Takeaways! • Don’t treat patients as a single entity – there is wide variability! – Geography! – Disease, disease type and stage! – Patient behavioural segment! • Takeaways on current behaviour! – – – – Information and support seeking is main motivation. Gap is real.! Women more than men, younger, better educated! Self-tracking is still done by a minority! Online populations are comparable to ofﬂine, slight bias to young, more educated! – Emotional content matters, but impact differs by user! – Early stage, little evidence on behaviour modiﬁcation claims!
Questions?! firstname.lastname@example.org! ! About Vitanect! ! Vitanect is an online social network for patients and caregivers, focusing on healthcare research! ! Visit us at www.vitanect.com !
Calcification Inhibitors in CKD and Dialysis Patients
Isolation and lack of engagement in social networks are strong predictors of ... diabetic patients, ... Health and Social Behavior 36 ...
... Daniel B. Characteristics of Socially Isolated Patients with Coronary ... Lisa F. Social Networks and Health: ... Health and Social Behavior ...
... social networks theories ... Health Behavior Change,” American Journal of Health Promotion, 12 (1), ... Theoretical Models in Social Marketing
The online version of Social Networks at ScienceDirect.com, ... Social Networks ... We examine social capital effects on productivity among diverse ...
The determinants of health ... the person’s individual characteristics and behaviours. ... Social support networks ...
Journal of Medical Internet Research ... Substantial research has focused on patients’ health ... Trials of online social network-delivered behavioral ...
... and environmental influences on behavior. Behavior change, ... Social psychology; Sociology of health and illness; Tropical disease; Preventive healthcare:
Primary Care Behavioral Health ... and mental health providers, the patients’ needs are ... of health and illness, Social and ...