Uploaded byDarshan Kulkarni
Considerations for companies debating implementing blockchain based databases 6.13.18 b
The document explores the implications of implementing blockchain-based databases in life sciences, highlighting challenges such as data control and ownership, regulatory concerns, and the evolving landscape of patient data engagement. It emphasizes the need for life science companies to rethink their data strategies amidst changing technological and legal environments. Additionally, the speaker, an expert in regulatory strategy and clinical practice, discusses the potential of artificial intelligence to enhance, rather than replace, healthcare professionals in decision-making.
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Considerations for companies debating implementing blockchain based databases 6.13.18 b
- 1. ConsiderationsforCompanies DebatingImplementing Blockchain-BasedDatabases IntoTheirOperations –TheStoryofData• DARSHAN KULKARNI,PHARM.D., M.S., ESQ. • Vice President, Regulatory Strategy and Policy,
- 3. © Copyright 2015Certara, L.P. All rights reserved. Steps Nirvana Change in Data Flux Causes of Lack of Control No Patient Control
- 4. © Copyright 2015Certara, L.P. All rights reserved. Goal of my talk • What is the future? • What are the rate limiting steps of the future? • This is not a “how to”, but a “what about” talk. A perspective from a FDA regulatory lawyer with 15 years of experience and a clinician with 20 years of experience
- 5. Development/Coding Strategy Clinical Pharma Health Legal Who here is/hasthe following background?
- 6. © Copyright 2015Certara, L.P. All rights reserved. Who here believes: Life Science companies (LSCs) have all the data they need LSCs have optimized the use of their data LSCs are ethically using all the data they have Laws like GDPR halt or slow down progress
- 7. © Copyright 2015Certara, L.P. All rights reserved. Steps Nirvana Change in Data Flux Causes of Lack of Control No Patient Control
- 8. © Copyright 2015Certara, L.P. All rights reserved. World is Flat 1400 to 1800: The first era involved countries globalizing 1800 to 2000.: In the second era companies or multinationals were globalizing. 2000-2010: Individuals and small groups from all nationalities began globalizing themselves. > 2010: Data in the cloud and is globalized. 8
- 9. © Copyright 2015Certara, L.P. All rights reserved. Current Movements Patient engagement Data Management Transparency 9
- 10. © Copyright 2015Certara, L.P. All rights reserved. Data Use 1 patient to 1 patient Traditional medicine Many patients to many patients Clinical Research RWE AI Many patients to one patient ??? 10 Not dependable Biased
- 11. Headline Text Here Headline Text Here 11 Lackof Control of Data by Patients
- 12. © Copyright 2015Certara, L.P. All rights reserved. Changes in Data Ownership
- 13. Headline Text Here Headline Text Here 13 Causesof lack of control
- 14. © Copyright 2015Certara, L.P. All rights reserved. Siloed Data hospitals pharmacies patients Pharma MD offices Government Payors Payors MD offices
- 15.
- 16. © Copyright 2015Certara, L.P. All rights reserved. 16 Stake Holder Consideration Checkpoint Real World Limitation Flag Impact Consideration Banner Unempowered Patient Summit
- 17. © Copyright 2015Certara, L.P. All rights reserved. Stakeholder Considerations 17
- 18. © Copyright 2015Certara, L.P. All rights reserved. Who are the stakeholders in patient data? 18
- 19. © Copyright 2015Certara, L.P. All rights reserved. Stake Holder Considerations Patients Caregivers Regulatory Agencies HCPs Life Sciences Companies CROs IRBs Promotional agencies 19
- 20. © Copyright 2015Certara, L.P. All rights reserved. What rights should non patients have to patient data? 20
- 21. © Copyright 2015Certara, L.P. All rights reserved. Real World Limitations 21
- 22. © Copyright 2015Certara, L.P. All rights reserved. Current Data Science Maturity Model Collect Describe Discover Predict Advise
- 23. © Copyright 2015Certara, L.P. All rights reserved. 1 2 3 6 5 4 7 8 9 Identify Data De-Silo Data Access Data Overlay DataGet Data InsightM2M Data Data Ownership Collaborate on Data Nirvana Identify Data • Public Data • Private Data • Data Standards Desilo Data • Convert Data • Prep Data • Validate Data • Tamper Proof Data Access Data • Real Time Data • Search Data • Aggregate Data Overlay Data • Annotate Data • Redact Data • Aggregate Data Get Data Insight • Identify Data Caps • Simulate Data • Prioritize Data M2M Data • Automatic Data Integration • Machine Readable Data Data Ownership • Meet Data Compliance • Maintain Data Privacy • Secure Data • Provide Data Transparency • Show Data Provenance • Decentralize Data Storage Collaborate on Data • Trade Data • Attribute Data Value • Create Data Exchange Trust Engine • Decentralize Data Processing
- 24. © Copyright 2015Certara, L.P. All rights reserved. What are the limitations of the Model? •Only sees information from the developers viewpoint Built on process of big data applying predictably to larger populations Ignores that treatment is one patient at a time. (I don’t care if it applies to 100 patients 99% of the time. Will it apply to this patient?) Built on AI model for M2M. Does not look at the healthcare data user Ignores patient impact Ignores many regulatory concerns 24
- 25. © Copyright 2015Certara, L.P. All rights reserved. Impact Considerations 25
- 26. © Copyright 2015Certara, L.P. All rights reserved. Real World Limitations Legal Regulatory Clinical Impact Financial viability Engagement 26
- 27. © Copyright 2015Certara, L.P. All rights reserved. Impact Considerations Transparency 27 Sunshine Act Medicaid/care payment disclosures Pricing transparency GINA HIPAA GDPR HITECHPCI
- 28. Headline Text Here Headline Text Here 28 Reaggregatingand Controlling data
- 29. © Copyright 2015Certara, L.P. All rights reserved. Result 29
- 30. © Copyright 2015Certara, L.P. All rights reserved. New Model: AI is not looking to replace, but augment HCPs Collaborate And create relevant data streams Contextualize To an individual patient for an individual problem Suggest Treatment options AT POINT OF TREATMENT Likelihood of success Explain AI determined that it is contextually important. What makes it important? 30 Why?Regulatory buy-inLegal liabilityUntested data
- 31. © Copyright 2015Certara, L.P. All rights reserved. Why augment and not replace? Clinical Acceptance Leads HCPs to see AI as a consultant and not a replacer Lead to better adoption Will allow for better care Better regulatory approval Understandable Reproducable Fixable Bias, if present, is identifiable. 31
- 32. © Copyright 2015Certara, L.P. All rights reserved. 32
- 33. © Copyright 2015Certara, L.P. All rights reserved. 33
- 34. © Copyright 2015Certara, L.P. All rights reserved. 34
- 35. © Copyright 2015Certara, L.P. All rights reserved. 35
- 36. © Copyright 2015Certara, L.P. All rights reserved. 36 Openpharma.com
- 37. © Copyright 2015Certara, L.P. All rights reserved. Questions?Questions? 37
Editor's Notes
- #4 No Patient Control Henrietta Lacks- vulnerable populations at greater risk John Moore Ted Slavin who is selling vials of his blood Causes of Lack of Control Data fragmentation Data Right Fragmentation Turf Wars Flux New Laws Patient Empowerment Change in Data New right structuring Land Grab Nirvana Patient control Monetization
- #7 No Patient Control Henrietta Lacks- vulnerable populations at greater risk John Moore Ted Slavin who is selling vials of his blood Causes of Lack of Control Data fragmentation Data Right Fragmentation Turf Wars Flux New Laws Patient Empowerment Change in Data New right structuring Land Grab Nirvana Patient control Monetization
- #8 No Patient Control Henrietta Lacks- vulnerable populations at greater risk John Moore Ted Slavin who is selling vials of his blood Causes of Lack of Control Data fragmentation Data Right Fragmentation Turf Wars Flux New Laws Patient Empowerment Change in Data New right structuring Land Grab Nirvana Patient control Monetization
- #13 Henrietta Lacks John Moore Ted Slavin (A hemophiliac whose doctor told him his cells were valuable. Slavin founded Essential Biologicals, a company that sold his cells, and later cells from other people so individuals could profit from their own biological materials.)
- #36 Here at Synchrogenix – we are already working on internal projects like PEONY – an open platform patient identification tool to minimize patient crossover; Patient engagement tools like the patient wiki which is a crowdsourced, AI optimized, blockchain tool which allows for engaged patient discussions in a fluid democracy format.
- #37 But all our innovation is simply the creation of new silos or aggregated silos. It is important to go further and connect these silos. Allowing each to work independently and efficiently, but come together using open APIs to work together like a city. Open pharma is our effort to create the plumbing for the city. It isn’t sexy, but it is critical and addresses an important need.