Blockchain in Health - From Pilots to Mainstream and Implications for AI Speakers delve into and beyond the BHTY published article “Moving Beyond Proof of Concept and Pilots to Mainstream: Discovery and Lessons from Blockchain in Healthcare,” located at https://doi.org/10.30953/bhty.v6.280. This continuous journey extends the framework and solution assemblies including further developments, with cross over into generative AI and ethics. Article Authors and Speakers Sathya Krishnasamy, MS | Founder, ChainAim, USA Badri Gopalakrishnan, PhD | University of Washington, USA Atul Apte, BSc | Founder and Chief Research Officer, Adarza LLC, USA Moderator: Anjum Khurshid, MD, PhD | Department of Population Medicine,, Harvard Pilgrim Health Care Institute & Harvard Medical School, USA
Groundbreaking Scalable Initiative That Controls "Pack" vs "Batch" Daniel Laverick | Head of SAP and IT Solutions, Zuellig Pharma How is blockchain technology empowering patients to verify provenance and quality of COVID-19 vaccines and other lifesaving drugs through Asia's first blockchain solution for supply chain connectivity and traceability for pharmaceuticals and vaccines? With two million products on the blockchain network, this ground breaking scalable initiative controls "pack" vs "batch." Authorities are now seizing counterfeits and consumers are benefitting by verifying their drug(s) are effective and can be trusted as safe and efficacious. Why use blockchain and what are the vaccine management issues along the pharma supply chain?
Best Practices to Improve Healthcare Delivery Using AI Prasad Kothari, Director, Axtria Health Tarun Mohan Lal, Vice President, Chief Analytics and Solutions Officer, Atrium/Navicent and incoming President of Society for Health Systems Dr. Anand S. Rao, Partner and Global AI Lead, PwC Swathi Young, Chief Technology Officer, Integrity Management Services Inc. A candid discussion that dives into the recesses of AI and ethics including topics such as what lessons can be used in healthcare from tech players implementing responsible AI and unbiased systems, how can responsible AI help build a clinical support system, examples from providers and payers and learnings from responsible AI which can be used in Obamacare and clinical trial implementation. Discussion topics will include: What is responsible AI? Is it the same as Ethical AI? What lessons can be used in healthcare from tech players implementing responsible AI and unbiased systems? Why is responsible AI important for healthcare? Is there demographic bias in the treatment part of responsible AI? How can responsible AI help build a clinical support system? What are some examples of responsible AI in the healthcare industry with respect to providers and payers? What are learnings from responsible AI which can be used in Obamacare implementation? HHS introduced an interoperability rule between EMR/EHR systems in 2019. How does it help in the implementation of responsible AI? How responsible AI is currently covered in new US government initiative such as AI.gov Responsible AI & data bias in clinical trials
Empowering and Protecting Patient Data with Open Source DLT's Maria Palombini | Director, Healthcare & LIfe Science Global Practice Lead, IEEE SA Sachin Shetty | Associate Director , Virginia Modeling, Analysis and Simulation Center (VMASC), Old Dominion University, and Executive Director of the Center for Secure and Intelligent Critical Systems Daniel Harrell, PhD | Research Associate, Department of Population Health, Dell Medical School, University of Texas at Austin Key discussion points of this panel session include: The role of open source DLTs in empowering and protecting patients data The role of where technical and data standards are pragmatically needed The technical and ethical considerations of patient data governance
Bringing Acute-level care Into the Home Dr. Sandeep Pulim | Medical Director, Biofourmis Until recently, acute care technological innovations were centered on improvements in procedural interventions or critical care. Accelerated by the pandemic, there has been a shift and developing interest in bringing acute-level care into the home. As a result, more tech-enabled improvements are supporting decentralized care models. Developments in artificial intelligence (AI) and machine learning (ML), combined with clinical necessity, are enabling healthcare systems to deliver better care beyond the four walls of the hospital.