are we ready for disruption in translational research through digital medicine?
TRANSCRIPT
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
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
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
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
Measures
§ 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
Speed of Evidence Impacts Entire Translational Research Continuum
Basic Biomedical Discovery
Clinical Efficacy
Clinical Effectiveness Clinical 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
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Exponential Innovation in Apps, Wearables and Analytics- Crowdsourcing Quantified Self
50M Wearables shipped
165,000 Apps
Terabytes of new data/second
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
Report Card showing Unified View of IBD Quality
Quality of Life
Quality of care
Resource U2liza2on
http://healthpromise.org
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
Creating a comprehensive “research profile” for each patient
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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)
#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
Social Media as source for research data
• “scrape” a website • “data grants”
• scripted API queries Courtesy: Nick Genes, MD, Ph.D
Which platform is best?
• 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
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
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
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
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%).
Evidence-Based Digital Medicine (EBDM)
RIGOR Evidence-based Medicine
INNOVATION Digital Technologies
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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
National Registry of Digital
Medicine Pilots
Unique Assets for Curating Evidence
http://nodehealth.org
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
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Are We Ready to Disrupt Translational Research?
Questions? [email protected]
http://nodehealth.org