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

Scientific Core: AppLab

http://sinaiapplab.org

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

Opportunities created by patient and social media generated data to fast

track evidence generation

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

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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

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

Report Card showing Unified View of IBD Quality

Quality  of  Life  

Quality  of  care  

Resource  U2liza2on  

http://healthpromise.org

Dashboard- Patient’s Quality Report

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?

•  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

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

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

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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Payers

Startups

Others

Societies

Grants agency

Angels VCs

Pharma

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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? ashish.atreja@mssm.edu

http://nodehealth.org