The POEM Study:  Finding Value in Social Networking for Health
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The POEM Study: Finding Value in Social Networking for Health

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This presentation (and discussion) will outline the results of a recent national VHA study on the effectiveness of the PatientsLikeMe social networking experience in a cohort of patients with a ...

This presentation (and discussion) will outline the results of a recent national VHA study on the effectiveness of the PatientsLikeMe social networking experience in a cohort of patients with a chronic health condition (epilepsy). The study captured patient usage data and tested the impact on accepted and validated metrics of self-management. This talk will detail these results, while reflecting on the broader implications on the potential value of these findings. The discussion will address the current incentive structure in the healthcare system which does not naturally support this type of healthcare 'intervention', and will posit alternative solutions to 'valuing' these types of patient engagement mechanisms.

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The POEM Study: Finding Value in Social Networking for Health Presentation Transcript

  • 1. The POEM Study: Finding Value in Social Networking for Health John D. Hixson MD UCSF and San Francisco VAMC
  • 2. Outline • Background on Epilepsy and theVA • Study Design and Conduct • Results • Discussion of Outcomes andValue
  • 3. Epilepsy and U.S.Veterans
  • 4. The POEM Study • Policy for • Optimal • Epilepsy • Management
  • 5. Step 1: Create/update and share your health profile Step 2: Find support from others like you and compare experiences Step 3: Learn from aggregated communityTreatment and Symptom Reports Step 4: Track health information to assist in improved treatment conversation with health team Step 5: Play an integral part in your own health care Engagement Cycle
  • 6. Profile Page
  • 7. Self-TrackingTools
  • 8. Self-TrackingTools
  • 9. Open Forums
  • 10. PRO Surveys
  • 11. Hypothesis The use of an online social media and disease management platform forVeterans will result in improved longitudinal epilepsy care as measured by validated patient self-efficacy metrics
  • 12. Methods • Inclusion/exclusion criteria – U.S.Veteran – Diagnosed with epilepsy or seizure disorder – No prior PLM usage • Recruitment strategies – Patient letters/emails, phone calls, flyers, local and national digital advertising • Outcome data collected at baseline and 6 weeks, usage data collected continuously • Incentive for completion
  • 13. Landing Page
  • 14. Patient Sign-up
  • 15. Patient Consent
  • 16. Patient-Reported Outcomes
  • 17. Participate for SixWeeks
  • 18. Outcomes Measures • Epilepsy Self-Efficacy Scale (ESES) • Epilepsy Self-Management Scale (ESMS) “I keep track of the side effects of my seizure medication” “I have a support group of people who have epilepsy” “When my seizure medication is running out, I take less medication at each time”
  • 19. ESMS
  • 20. ESES
  • 21. Secondary Outcomes • ESMS Subscales – Information management – Medication management – Safety management – Seizure management – Lifestyle management • ESES Subscales – Medication self-efficacy – General self-efficacy – Seizure management self-efficacy
  • 22. Others Measures • Demographic information – Age, sex, race, years of education, health status • PLM Utilization – Logins, forum posts, comments, new friends • PLM Impact – Seizure understanding, management and care
  • 23. Results • 249 eligible participants (453 total) – 92 full study completers • Average age: 50.2 years • 81% male • 75 % non-Hispanic white • 19% self-report working full-time • 30% self-report either fair or poor health status
  • 24. 0.0 1.0 2.0 3.0 4.0 5.0 6.0 Information Lifestyle Medication Safety Seizure Total Mean Category Epilepsy Self-Management Scale Baseline
  • 25. 0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 Medication General Seizure Total Mean Category Epilepsy Self-Efficacy Scale
  • 26. Results • Self-Management Score Improvement – marginal improvement (3.7  3.9, p .01) • Self-Efficacy Score Improvement – marginal improvement, (7.4 7.8, p .03) • ESMS Information Management – 17% improvement, (2.4  2.8, p .001) **Includes ALL available data points
  • 27. Utilization Statistics – Logins: Median 5 (range: 0-80) – Post to forum: 15% – Left profile comment: 10% – Sent private message: 30% – Met new person with epilepsy: 26% • of these, 10% considered ‘friends’
  • 28. What Now? • Despite using a ‘validated’ outcome: – Considered a ‘soft’ measure – No direct path to implementation – Limited incentive structure • Contrast to typical pharma or device studies – Historically uses a ‘hard’ measure – Very clear path to implementation – Known incentive structure, regardless of true utility
  • 29. Typical Study • Adjunctive therapy, on multiple AEDs • 10-20 seizures/month • No seizure-free periods longer than 21 days • 3-6 months trial • Outcomes: % seizure reduction, 50% responder rate
  • 30. RealWorldValue • Socialization • Driving • Return toWorking Status • Healthcare Utilization – Emergency Services use – Pharmacy – Psychological support services
  • 31. Who HasThe Power? • Policy makers • Payors • Employers that ‘self-insure’
  • 32. Conclusions • Digital health platforms can and should be evaluated with academic rigor • Clinicians and investigators should not shy from recruiting the ‘non-tracker’ • Trials with typical patients will yield valuable and practical lessons • Policymakers and stakeholders need to seriously consider alternative metrics of health ‘success’
  • 33. Additional Slides
  • 34. Pilot-Tested Outcomes • Epilepsy Self-Efficacy Scale (ESES) • Epilepsy Self-Management Scale (ESMS) • Quality of Life in Epilepsy QoLIE-10P • Modified Medication Adherence Scale • Jacoby Stigma Scale • PAM (Patient Activation Measure) • Hospital Anxiety & Depression Scale (HADS) • Patient satisfaction with and feedback on PLM
  • 35. Explanatory vs. Pragmatic • Participants • Experimental intervention • Comparison intervention • Follow up intensity • Primary outcome • Compliance • Outcome analysis
  • 36. Interim Data Plan – Goal -To determine whether study should be stopped due to: • clear evidence of benefit or harm • futility to detect benefit or harm – Statistical tests • Paired t-tests,Wilcoxon sign-rank tests • Missing data (N/A, prefer not to answer) – Mean likert scores (all available data) – Extrapolated scores (<20% missing)
  • 37. Recruitment Data – Recruitment: 412 sign-ups • 190 excluded – 10 opt out, 56 did not consent, 64 not veterans, 18 no epilepsy, 26 did not complete survey, 16 ‘sockpuppets’ • 232 enrolled (56%) • Retention: 232 enrolled – 201 invited to follow-up, 31 not yet invited – 75 completed (37%) – Completers higher education (84% vs. 70% completed HS, p=0.03)
  • 38. Primary Outcomes N Time 1 Time 2 P-1 P-2 ESMS, mean likert 75 3.7 3.9 .03 .04 ESMS, extrapolated 67 140 144 .006 .006 ESES, mean likert 75 7.5 7.8 .07 .04 ESES, extrapolated 64 243 255 .03 .01 P-1: Paired t-test; P-2:Wilcoxon sign-rank Evidence of improvement in both ESMS and ESES
  • 39. Secondary Outcomes N Time 1 Time 2 P-1 P-2 ESMS, information (1) 75 2.5 2.8 .001 .002 ESMS, information (2) 56 20.2 22.6 .002 .002 ESMS, lifestyle (1) 75 3.4 3.5 .04 .04 ESMS, lifestyle (2) 69 20.0 20.9 .01 .02 ESMS, seizure (2) 59 25.5 26.4 .04 .03 ESES, general (1) 75 6.4 6.8 .03 .03 ESES, seizure (1) 75 7.6 7.8 .10 .06 ESES, seizure (2) 66 58.9 61.6 .04 .03 1: mean likert; 2: extrapolated; P-1: Paired t-test; P-2:Wilcoxon sign-rank Other subscales – no evidence of change