We are in an era when the volume of healthcare data is growing at lightning speed but our ability to gain insights from that data for improved health outcomes remains far behind. Barriers are many and include silos of information, poor data quality from non-structured EHRs, underutilized genomics, and continued healthcare inequities. Bringing together stakeholders from patients to biotech to health systems to pharma to big data and others will accelerate the process of data-driven health care." 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 technology innovation of our lifetime. AI applications and leveraging different data types and structures (structured, unstructured, and semi-structured) for integrated health care processes are embraced across the health care sector. Healthcare data is providing organizations with a roadmap for improving patient outcomes, improving operations (i.e., analyzing workflow needs, financials, and resources), creating new healthcare models (e.g., preventative care programs), optimizing clinical trials, and – on the drug discovery and development side - accelerating and optimizing therapeutic candidate selection.
Although the importance of AI & data sciences is understood, major challenges must be addressed in order to successfully leverage the full potential of AI processes and applications, be that selecting and implementing the proper compute infrastructure, integrating various data types, achieving and maintaining data quality, unifying teams working with different tools and languages, breaking data silos, and addressing and enforcing data trust, security and governance issues.
Track 2 / Day 3 is focused on addressing exactly these challenges during PMWC 2023 Silicon Valley, January 25-27. We have high-level experts across the clinical and pharmaceutical sectors contributing to this important track with the goal to bring key stakeholders together so that learnings can be exchanged, challenges and needs can be discussed, and hopefully work towards a consensus approach can be accomplished to help move this field forward to accelerate therapeutics discovery, clinical research, and patient outcome. The focus will be on making data more valuable. Major topics in this context are FAIR data principles, NLP applications to parse unstructured text, AI applications for clinical trial design and patient selection, AI applications for outcomes prediction and decision support, and the pharma data informatics ecosystem.
The evolving track:
• PMWC 2023 Luminary Award with honoree Gad Getz (Broad Institute) for pioneering widely used tools for analyzing cancer genomics.
• How Healthcare Can Solve Its Data Problem – talk by Rod Tarrago (AWS)
• Analysis of Omics Data Using Novel AI Strategies Provides Insights and Applications into Healthcare – talk 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 the Hospital/Clinical Setting, for Clinical Trial Design and Patient Selection – with Matthew Lungren (Nuance/Microsoft)
• Improving the Probability of Trial and Regulatory Success – a panel chaired by Elizabeth Lamont (Medidata AI)
• AI/ML Applications for Patient Outcomes Prediction and Clinical Decision Support – 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 Pharma Informatics Ecosystem – a panel chaired by Maria Karasarides (BMS) with Colin Hill (GNS) and Christine Bakan (Roche/Genentech)
