We are in an age where the volume of healthcare data is increasing at lightning speed, but our ability to learn from that data to improve health outcomes lags far behind. The barriers are many and include information silos, poor data quality from unstructured EHRs, underutilized genomics, and persistent healthcare inequities. Bringing stakeholders together, from patients to biotech to health systems, pharmacy, big data and more, will accelerate the process of data-driven healthcare. William Oh, MD, Clinical Professor, Mount Sinai and Track Chair AI & Data Sciences in Drug Discovery & Clinical Research, PMWC 2023 Silicon Valley, January 25-27
AI is considered to have the potential to be the most disruptive technological innovation of our time. AI applications and leveraging of different types and structures of data (structured, unstructured, and semi-structured) for integrated healthcare processes are being adopted across the healthcare industry. Healthcare data provides organizations with a roadmap to improve patient outcomes, improve operations (i.e. analyze workflow needs, finances and resources), create new models of care (e.g. preventive care programs), optimize clinical trials and – on drug discovery and development – accelerate and optimize the selection of therapeutic candidates.
While the importance of AI and data science is understood, major challenges need to be addressed in order to successfully harness the full potential of AI processes and applications, whether selecting and to implement the appropriate computing infrastructure, to integrate various types of data, to achieve and maintain data quality. , unifying teams working with different tools and languages, breaking down data silos, and addressing and enforcing issues of trust, security, and data governance.
Track 2/Day 3 focuses on solving exactly these challenges during PMWC 2023 Silicon Valley, January 25-27. We have high-level experts in the clinical and pharmaceutical sectors contributing to this important track with the aim of bringing together key stakeholders so that learnings can be shared, challenges and needs can be discussed and, hopefully, work towards a consensual approach. help advance this field to accelerate therapy discovery, clinical research and patient outcomes. Emphasis will be placed on the exploitation of data. The main topics in this context are FAIR data principles, NLP applications for analyzing unstructured text, AI applications for clinical trial design and patient selection, AI applications for predicting results and decision support, and the pharmaceutical data IT ecosystem.
The evolutionary path:
• PMWC 2023 Luminary Award with winner Gad Getz (Broad Institute) for its widely used pioneering tools for the analysis of cancer genomics.
• How healthcare can solve its data problem – presentation by Rod Tarrago (AWS)
• Analysis of omics data using new AI strategies provides insights and applications in healthcare – lecture by Michael Snyder (Stanford University)
• NLP Applications to Parse Unstructured Medical Text – session chaired by William Oh (Mount Sinai) with Rong Chen (Sema4)
• AI/ML applications in hospital/clinical settings, for clinical trial design and patient selection – with Matthew Lungren (Nuance/Microsoft)
• Improving the Likelihood of Trial and Regulatory Success – a group chaired by Elizabeth Lamont (Medidata AI)
• Applications of AI/ML for predicting patient outcomes and supporting clinical decision-making – a panel chaired by Alex Sherman (Harvard University) with Thomas Fuchs (Mount Sinai), Indu Navar (EverythingALS), Nuray Yurt ( Novartis Oncology), Jake Donoghue (Beacon), and Marie Abele (Harvard University)
• FAIR data approaches to make data usable, accessible and findable – a talk by Bhavesh Patel (Calmi2)
• The pharmaceutical informatics ecosystem – a panel chaired by Maria Karasarides (BMS) with Colin Hill (GNS) and Christine Bakan (Roche/Genentech)
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