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More articles from Volume 1, Issue 1, 2018
Welcome and Introduction
Keynote Address: Transformational Technologies in Healthcare - All Aboard!
The Decentralization Zeitgeist: Can the Revolution be Real?
Block-Education: Applying a “Fit-for-Purpose” Framework to Advance Health Blockchain Education
What's Next for Blockchain?
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IEEE-SA and BHTY Workgroup: Roadmap for Digitalizing Clinical Trials
Pharmacy Podcast Network
doc.ai
Bayer
Beth Israel Deaconess System
New England Healthcare Exchange Network (NEHEN)
Harvard Medical School
Published: 29.11.2018.
Volume 1, Issue 1 (2018)
Abstract
Session Description: Digitalization of Clinical Trials from End-to-End – The Right Time is Now to Jump in - there are many hype words thrown around in the healthcare sector in an effort to place the impetus on the need for change. – “disruptive,” “innovation,” “breakthrough” and more- Put aside the hype – the lukewarm approach to embracing digital technologies for clinical trials is over. The fever pitch of emerging technology applications impacting clinical trials are providing solutions for trial sponsors to combat the mounting challenges of cost and delays in bringing medicine to the market. The time is right to jump in to digitalizing the process if you want to get ahead of the curve in efficiency and efficacy to enhance patient safety and accelerate drug development. The reality is simple - cutting-edge technologies can revolutionize the clinical trials model not only by optimizing the processes but delivering more value for patients and all the critical stakeholders. This session will feature an interactive panel discussion addressing the following key points:
1. What is the roadmap for an end-to-end digitalized clinical trial? What are the limitations? What are the opportunities?
2. Debunking the myth that digitization is digitalization of clinical trials
3. Identifying all potential areas where technologies like Blockchain, Robotic Process Automation, IoT, and Artificial Intelligence can digitize Clinical Trials
4. Can digitalization meet all the expectations of all stakeholders – regulatory, patients, pharma and others?
5. The combination of AI and Machine learning for “data-driven” decision models -what are we looking to solve, what will it change?
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