This is the slide deck that was presented at Translational Science 2016. Touches upon evidence generation as one of the most desired but expensive process in medical science. Provides examples of how Social Media, medical apps, quantified self movement are leading to patient generated data that can disrupt evidence generation process.

2. Quantified Self, Digital Medicine and SoMe to
support Translational Research
Ashish Atreja, MD, MPH
Chief Technology Innovation & Engagement Officer, Medicine
Asst Professor and Director, Sinai AppLab, Gastroenterology
Icahn School of Medicine at Mount Sinai, NY
www.sinaiapplab.org
© Icahn School of Medicine at Mount Sinai

3. Scientific Core: AppLab
http://sinaiapplab.org

4. Challenge #1
Generation of evidence is expensive
• At least 10 years of
development to bring a
drug the market at a cost
of about $ 2.5 billion
• The cost has grown from
estimated $800 million in
2003
• Further upstream costs
for post-marketing
surveillance and
effectiveness data once
drug is approved

5. Challenge #2
There is a big gap between evidence and
the care we give
1. Patients who are receiving best practices for care
2. Patients who are adherent to medications or lifestyle
interventions
3. Patients who are having uncontrolled symptoms

6. CRC-P
Measure
s
§ IBD 1: Patients Managed With
Corticosteroid Therapy
§ IBD 2: Pharmacologic Management;
Corticosteroid-Sparing Therapies
§ IBD 3: Influenza Vaccination in
Immunosuppressive Therapy
§ IBD 4: Tuberculosis Screening in
Immunosuppressive Therapy
§ IBD 5: Hepatitis B Risk Assessment
in Immunosuppressive Therapy
§ IBD 6: Hepatitis C Risk Assessment
in Immunosuppressive Therapy
Inflammatory Bowel Disease Measures
Hepatitis
C
Measure
s
§ IBD 7: Varicella/HZV Vaccination in
Immunosuppressive Therapy
§ IBD 8: Live Vaccine Avoidance
Counseling in Immunosuppressive
Therapy
§ IBD 9: Assessment of Bone Loss Risk
Due to Corticosteroid Therapy
§ IBD 10: Medication-Related Adverse
Events in IBD
§ IBD 11: Tobacco Status Assessment
and Cessation Counseling
§ IBD 12: Colon Cancer Surveillance in
Patients with IBD
Challenge #3: Providers alone can’t directly impact
population health

7. Speed of Evidence Impacts Entire Translational
Research Continuum
Basic
Biomedical
Discovery
Clinical
Efficacy
Clinical
EffectivenessClinical Practice
T1
What works under
controlled conditions?
(Up to phase III trials)
How can we
change practice?
(Dissemination and
Implementation Research)
What is the effect
on population health?
(Outcomes research)
T2
T3
T4
“Bench” “Bedside”
Community
Practices
Community
Practices
What works in
real world settings?
(e.g., Comparative
Effectiveness
Research)
Savitz et al, Engaging Communities for CER. U Colorado CTSA

8. Opportunities created by patient and
social media generated data to fast
track evidence generation

9. #1 New form of data in post EHR era:
patient generated data

10. 10
Exponential Innovation in Apps, Wearables
and Analytics- Crowdsourcing Quantified Self
50M Wearables shipped
165,000 Apps
Terabytes of new data/second

11. Best Care at Lower Cost: The Path to Continuously Learning Health Care
in America. IOM 2012
Apps Registries Pragmatic trials post marketing tools Wearable
Telemedicine
#2 Convergence of clinical, research and
patient generated data

12. Current Mount Sinai Crohn’s and Colitis
Registry- linked with EHR

13. Report Card showing
Unified View of IBD
Quality
Quality
of
Life
Quality
of
care
Resource
U2liza2on
http://healthpromise.org

15. Dashboard- Patient’s Quality Report

16. Comparison of real world data (1500+
UPMC) with App collected data (Mount Sinai)
Fatigue and Tension as major drivers of poor quality of life in more than 3/4th
of patients with IBD
Registration: ClinicalTrials.gov NCT02322307

17. Creating a comprehensive “research profile”
for each patient
17
EHR Registry
Endoscopy
records
• Lab
• Demographics
• Medications
• Hospitalization
s
• Family Hx
• Disease
activity
• Omics data
App
• Longitudinal
• QOL
• Symptoms
• Treat to
target
• Text mining
• Safety
• Efficacy
Intervention (clinical or
research)

18. #3 New form of care and research engagement
• Apple Research Kit
• Electronic consent that enables
people without direct, in-person
contact
• Within 24 hours 11,000
participants enrolled

19. Social Media as source for research data
• “scrape” a website
• “data grants”
• scripted API queries
Courtesy: Nick Genes, MD, Ph.D

20. Which platform is best?
• Twitter
• public by default, semi-anonymous users
• smaller than FB but users share much more
• text-based with links, categories (#)
» replies lead to conversations, corpora
» RT’s spread message, suggest agreement
• location via GPS, bio
Courtesy: Nick Genes, MD, Ph.D

21. Which platform is best?
• Facebook
• largest platform, tied to demographic info
• private by default
• consent user-by-user
» unless you’re FB, or make a devious app
» responsible FB researchers can set up their own
community/group: anthropology
Courtesy: Nick Genes, MD, Ph.D

26. Envisioning e-research in year 2020
ü Generating hypothesis (e-hypothesis)
ü Identifying feasibility of conducting trials
based internet cohorts (e-feasibility)
ü Recruiting eligible patients directly through
patient powered networks or social media
(e-recruiting)
ü e-consent and e-randomization through
apps and telemedicine
ü Tracking post market data through app (e-
PRO) and e-research visits
ü Increasing effectiveness of intervention
through apps (e-optimization)
@ Fraction of Cost and Time

27. After 500 pilots, we know almost nothing about the likely uptake, best
strategies for engagement, efficacy, or effectiveness of these initiatives
- World Bank
Tsai et al. PLOS Medicine. Scaling up mHealth: Where is the evidence?27
Bottleneck: Developing Digital Medicine as a
Scientific Discipline

28. https://peerj.com/articles/1554/

29. Date of download: 1/4/2016
Copyright © 2016 American Medical Association.
All rights reserved.
From: Effect of Lifestyle-Focused Text Messaging on Risk Factor Modification in Patients With Coronary Heart
Disease: A Randomized Clinical Trial
JAMA. 2015;314(12):1255-1263. doi:10.1001/jama.2015.10945
Enrollment of Participants in the TEXT-ME Randomized Clinical TrialLDL-C indicates low-density lipoprotein cholesterol.
Figure Legend:
At 6 months, levels of LDL-C were significantly
lower in intervention participants (mean
difference, −5 mg/dL with reductions in systolic
blood pressure (−7.6 mm Hg) and BMI (−1.3),,
and a significant reduction in smoking (26% vs
44%; relative risk, 0.61 [95% CI, 0.48 to 0.76]; P
< .001).
The majority reported the text-message program
to be useful (91%), easy to understand (97%),
and appropriate in frequency (86%).

30. Evidence-Based Digital Medicine
(EBDM)
RIGOR
Evidence-based Medicine
INNOVATION
Digital Technologies

31. 31
Payers
Startups
Others
Societies
Grants
agency
Angels
VCs
Pharma

32. 32
Building Consortium to support EBDM
1. Share information about ongoing single site pilots
2. Standardize existing governance and regulatory policies
3. Support multi-site digital medicine pilots
Powered by
Community
Forum
Weekly
Webinars
One-one
messaging

33. National Registry
of Digital
Medicine Pilots
Unique Assets for Curating Evidence
http://nodehealth.org

34. Conclusions
• Collection of data is expensive
• Translational researchers should look
into “new data” generated by patients
through digital medicine and social
media
• Digital medicine is fast becoming a
scientific discipline and researchers
need to be part of evidence generation

35. 35
Are We Ready to Disrupt Translational
Research?
Questions?
ashish.atreja@mssm.edu
http://nodehealth.org