Patient Engagement in Data Sharing

1. Patient Engagement in Data Sharing Case Study Example

2. Disclosure • The following information has been developed by: • Joel Beetsch, PhD – VP of Global Patient Advocacy, Celgene Corporation; member of the BD4P Steering Committee. • Jennifer King, PhD – Director of Science and Research, Lung Cancer Alliance; Co-Founder, SHARE For Cures; member of the BD4P Steering Committee. • This information does not constitute an endorsement by the Celgene Corporation, the Lung Cancer Alliance, Project Data Sphere, or SHARE For Cures.

3. Case Study Patient Engagement in Data Sharing: Patient-Directed Data Sharing & Sharing Clinical Trial Data • Compare and contrast: • Opt-in research • Data sharing • Data privacy • Patient data access • Focus: Project Data Sphere & SHARE For Cures

4. Cancer Research • Cancer research is slowand expensive • It takes up to $2 billion to bring a new drug to market – but an estimated 7.6 million people die from cancer each year* • Overall cancer death rates continue to decline, but progress is far too slow • Cancer accounts for more deaths than heart disease • We haven’t come far since the “War on Cancer” was declared *Source: Journal of the National Cancer Institute (JNCI)

5. Project Data Sphere • A free digital library-laboratory that provides one place where the research community can share, integrate, and analyze patient-level data from academic and industry phase III cancer clinical trials • Available to independent and affiliated researchers – anyone can apply to become an authorized user • Fully-public platform of de-identified, historic, raw cancer trial data, protocols and case report form with advanced analytics, and collaboration tools • To responsibly speed cancer research What if we could share, integrate, and analyze our collective historical cancer research data in a single location?

6. Data Sharing • Each data provider enters into a Data Sharing Agreement for the data sets provided • There is no general consent for individuals to contribute their data • The Clinical Data Interchange Standards Consortium (CDISC) Study Data Tabulation Model (SDTM) is the recommended format for data sharing

7. Project Data Sphere – How it Works Source: Project Data Sphere www.projectdatasphere.org

8. Project Data Sphere “Value Chain” From data to patients…

9. Data-Sharing in Oncology: Why do it…?1 7.6 million lives lost each year worldwide • Faster, more efficient research • Improved trial design and statistical methodology • Secondary hypotheses and epidemiology • Disease model development • Smaller trials sizing • Reduced duplication and transparency • Real-world corroboration with trial data • Data standards and meta-analysis • Unknowns2 1. Vickers 2006 2. www.cardia.dopm.uab.edu: 475 publications from a single large dataset

10. So Why Hasn’t it Happened? Unique challenges in healthcare Multiple (very valid) attempts Attitude is “don’t share unless I can prove no harm occurs”1 Solutions: 1. Vickers 2006

11. Solution #1: Privacy • De-identification • HIPAA Expert Determination method (EU DPD) • Multiple strategies to generalize patient demographics • Additional Measures • Enrollment • Data use agreements • Data security

12. Solution #2: Security • Hardened SAS Hosting Environment • Firewall • All access to data behind SAS firewall • Secure Socket Layer Transmission • All transmissions encrypted • Content virus scanning • All documents scanned before being made public • Enrollment • Role-based permissions • Password policy – to minimize chances of unauthorized entry • Application acceptance for access – BUT broad criteria

13. Solution #3: IP • Uniform Use-Agreements by design • Data provider • Data user • Comparator arm initially • The Data Provider retains ownership and all existing IP • Limited restrictions of the use of the data • No explicit prohibition on researchers’ IP for new inventions • Publication acknowledgement but not manuscript review

14. Solution #4: Resources • Minimal resources required • Especially relative to trial cost and benefits of sharing • IT, legal, and Project Data Sphere infrastructure in place – no cost • Only requirements • “Champion” within organization • Data preparation • i.e. Bio-statistician time to de-identify single Phase III data set (~40 hours of programming) • Legal review

15. Solution #5: Incentives • Access to new data • Model development • Open research • Data standards • Competitions • Honors the sacrifice • Data used effectively • Better/faster therapies • Smaller trials • Mutual pre-competitive data insights • Crowd insights • Productivity1 & Cost-savings2 • Collaboration & Transparency • Right thing to do 1: Paul et al, Nature Rev Drug Disc, March 2010 2: $251MM savings across broad program Internal data on file

16. Potential to Increase Productivity WIP * p(TS) * V C*CT Productivity = WIP: Work in progress, how many compounds are being tested? p(TS): Probability of technical success V: Value C: Cost CT: Cycle time Source: Paul et al., Nature Rev Drug Disc, March 2010

17. Making Progress…and Growing • 44,000+ patient lives in preparation for addition • Goal: 100,000 patient lives by May 2017

18. SHARE For Cures “The estimated 40,000 women (and a few men) who die annually [from breast cancer] can’t wait years for FDA-approved, ‘gold standard’ clinical trials. We’re dying now.” Laurie Becklund As I Lay Dying Los Angeles Times Op-Ed February 20, 2015

19. SHARE For Cures • There is a growing movement in all aspects of healthcare around using the “siloed” data that is captured to benefit everyone – especially in the patient community • Above all patients wantand need more progress to be made in research • We see this movement in the press every day • At the same time…we live in an era of growing data and public privacy concerns • There are good reasons for those concerns – data is being stolen and in most causes legally bought and sold without their consent or knowledge

20. Barriers to Progress Existing issues that SHARE For Cures is trying to address: • Researchers • Expensive and difficult to gather and analyze large datasets • Lack of standards/interoperability • Expensive licensing fees and/or technology costs • Patients • Lack of transparency and engagement in current models • Limited opportunities to participate in the research process • Advocacy Organizations • Want opportunities to advance research for their communities in innovative ways – but often lack training or funding

21. “What Can I Do? Concept Relationships Type 1 Diabetes Patient Advocate: “I can get my head around the fact that autoimmunity is difficult to understand and cure. I can’t get my head around the fact that we have this data that is structured, machine readable, that we already understand, that’s sitting in pockets in all of the living rooms and computers of patients that generate this data around the country and around the world. The fact that we can’t combine that data so that researchers can understand patterns and make connections is incredibly frustrating to me.” Blurry Vision Nausea/flu-like symptoms Slow Wound Healing Rapid weight Loss Thirst Hunger Fatigue Frequent Urination Family history Genetics Environment Viruses Diet Microbiome A1C Fasting Plasma Glucose Oral Glucose Tolerance Test RISK FACTORS DIAGNOSIS SYMPTOMS Insulin Symlin Glp-1 Analogs Nutrition Exercise Insulin Pumps Pens Side Effect Mgmt Behavior/Coping Gluten Avoidance Type 1 Diabetes Autoimmune Disease Process TREATMENT Type 1 Diabetes SNOMED 46635009 LIFESTYLE Carbohydrates Water Fat/Protein/Fiber Weight Aerobic Exercise Strength Training Stretching Sleep Stress Management Adherence NCIt C2986 Concept Attributes HOME: Blood Glucose Monitoring Continuous Glucose Monitoring Blood pressure Weight Mood/energy LABORATORY/PHYSICIAN: A1C Kidney functioning HDL/LDL/Triglycerides Hemoglobin Retinal Imaging Filament test (nerves) Medical Concept ICD-9/10 250.01/E10 MONITORING Diabetes Mellitus, Type 1 TREATMENT TEAM Primary Care Physician Endocrinologist Nurse Educator Dietician Nephrologist Psychiatrist/Counselor Ophthalmologist Cardiologist Podiatrist COMPLICATIONS BIOLOGY Immune response Autoantibodies Beta cells Insulin production Blood Sugar Microvascular damage Tissue glycation Microbiome Retinopathy Vision Loss/Blindness NeuropathyInfection/Amputation NephropathyCKD/Dialysis Heart DiseaseMI/CHF HypertensionCKD/Stroke Depression/Anxiety Cognitive Impairment/Decline

22. The Right Time • 68% of 3,000 respondents – “Over two-thirds…would be willing to share their health information anonymously with researchers.” • Source: Truven Data Privacy Health Poll 11/2014 • 81% of chronically ill patientsand 67% of those not chronically ill want the ability to both extract and annotate their clinical records for their own use. • Source: Accenture Patient Engagement Survey 2015 • 90% of researchersstate there is value in having access to clinical, pharmaceutical, wellness, and lifestyle data…but the barriers are too great. • Source: Harvard Business Review 2016

23. SHARE For Cures SHARE For Cures Mission To empower you to use your health data to advance medical research and save lives. SHARE For Cures Transformational Vision Crowdsourcing cures by empowering everyday people to drive medical research forward.

24. SHARE For Cures To accomplish all we have set out to do, we knew we needed to create… A way for you to access your health data all in one place and make a safe, secure, hassle-free, cost-free, and meaningfulcontribution to disease research now SHARE – System for Health And Research data Exchange A simple UI and a powerful platform to engage patients and aggregate real-world data from many sources and enable patient-driven data sharing for research

25. Consent • SHARE For Cures uses a general consent so that participants can opt-in to sharing their data • With researchers – participants can revoke sharing consent at any time • Sharing Preferences – participants can identify how/where they will allow their data to be shared

26. Adding Medical Data • Participants can upload their data (including EMRs) directly from their medical providers • Connects to ~50% of hospitals in the US • Also CVS, Walgreens, RiteAid, Quest labs, etc. • Participants can also upload data from health apps • Fitbit, Runkeeper, 23andMe, Google Fit, Apple Watch, etc. • This information will display in the participant’s SHARE account • They can determine who has access to their data, and for what purposes (i.e. all uses, research, with identifiable data)

27. Other Current User-Facing Features Ability to view and download your data from all connected sources Ability to annotate or add to your data Graphical views of your data Surveys “Facebook Style” Feed – future use for educational materials, clinical trial opportunities, etc.

28. Future Plans SHARE Button to seamlessly connect with other patient-facing platforms Caregiver/Proxy logins Mobile App Researcher Portal Enhanced Artificial-Intelligence Algorithms Citizen Scientist Portal

29. Legal & Ethical Considerations Users have complete control over who can use their data, and visibility into who has seen it and why. General legal consent (direct patient authorization, outside of HIPAA) for data sharing based on preferences. Specific studies request datasets from SFC but get their own IRB approval/waiver. Built-in ability to gather up-front consent for specific studies using the study consent documents. Currently, all agreements are for research purposes. More legal work to be done if data is used for clinical care.

30. Privacy & Security • Infrastructure in Amazon’s HIPAA-compliant cloud • Built with HITRUST controls in mind. • HITRUST encompasses HIPAA, HITECH, Meaningful Use, Individual State Laws, PCI, ISO, and COBIT • Pursuing HITRUST certification • Users have complete control over who can use their data, and visibility into who has seen it and why.

31. Other Patient-Directed Data Sharing Disease-Specific Projects & Registries Personal Health Records

32. Discussion: Compare & Contrast Project Data Sphere SHARE For Cures Think about: • Mechanisms of Consent • Privacy • Data Collected (remember the Vs) • Data Sharing • Patient Data Access