Transforming Lung Health and Disease using AI-based Registries

Through the utilization of artificial intelligence (AI) and predictive algorithms, there is the potential to make considerable advancements in transforming the treatment and management of lung health and disease.

Using unbiased AI/machine learning (ML) approaches, both the structured and unstructured electronic health records (EHRs) of several million patients in the Robert Wood Johnson Barnabas Health System (RWJBH) were analyzed.

This analysis was used to identify opportunities to enhance the management and treatment of chronic obstructive pulmonary disease (COPD) patients who have already experienced at least one exacerbation and predict the disease severity and risk associated, obviating the need to document the tools currently used. Evidence-based analysis of this data suggested that a significant number of patients experienced COPD exacerbations and that a limited percentage of those patients were receiving the recommended therapy to deter COPD exacerbations as per national guidelines.

The expert speakers seek to build a broader and more diverse population of patients to establish a structured and unstructured data registry across academic medical centers (AMCs) for patients with lung diseases. The goal aims to enhance research and development (R&D) and more proactive care options to validate the prototype algorithm that will drive its utility. The application of AI to medical data could be an invaluable tool for improving the identification of at-risk patients, treatment personalization and overall patient outcomes. This discussion will explore the development of a registry that will utilize ML- and AI-driven data analysis techniques to generate a comprehensive longitudinal lung health database.

The registry will create a dynamic, fluid resource for healthcare providers, researchers and pharmaceutical sponsors, which enables investigators to predict treatment responses, disease progression and possible therapeutic interventions. It could connect medical centers and key opinion leaders (KOLs) across the county, ultimately enhancing patient care and fostering innovation and advancements in the management of pulmonary diseases.

It would be designed to process large datasets across AMCs in minutes using real-time data feeds from the involved AMC’s EHRs. This can facilitate the identification of risk factors and patient sub-groups who are responsive to treatments, in addition to increase in recruitment to clinical trials. In this webinar, the expert speakers will discuss real-time updates to health records, a registry that would be continuously updated with new patient data and outcomes, allowing researchers to monitor the effects of emerging treatments with minimal time delays.

Moreover, this registry will create an environment to generate faster and more adaptive clinical trials and drug development processes and potentially accelerate R&D efforts. A registry may ultimately contribute to the advancement of both the science of lung diseases and the quality of care of affected patients.

Register for this webinar to gain insights into the power of AI-driven data analysis to identify at-risk patients, optimize clinical trials and accelerate innovation in pulmonary disease care.

Speakers

Dr. Reynold A. Panettieri, MD, Vice Chancellor for Translational Medicine and Science Director, Rutgers Institute for Translational Medicine and Science Professor of Medicine; Robert Wood Johnson Medical School Emeritus Professor of Medicine, University of Pennsylvania; and Rutgers Institute for Translational Medicine and Science, Rutgers, The State University of New Jersey

Dr. Reynold A. Panettieri, Jr, MD, the inaugural Director of the Institute for Translational Medicine and Science and Vice Chancellor for Translational Medicine and Science at Rutgers University, previously served as the Director of the Airways Biology Initiative at the University of Pennsylvania.

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His interests include the cellular and molecular mechanisms that regulate airway smooth muscle cell growth and the immunobiology of airway smooth muscle. Dr. Panettieri’s lab also focuses on cytosolic signaling pathways that mediate gene expression and alter myocyte growth. Dr. Panettieri also served as the Deputy Director of the Center of Excellence in Environmental Toxicology at the University of Pennsylvania.

In addition to his research and clinical interests, Dr. Panettieri served as chairperson of the NIH Lung Cellular, Molecular and Immunobiology Study Section, is a member of the NIH Distinguished Editorial Panel and is a member of the American Society for Clinical Investigation and Association of American Physicians.

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Peter Casasanto, Senior Vice President & General Manager Life Sciences, Deep 6 AI

Peter Casasanto is the SVP and GM of the Life Sciences business at Deep 6 AI. He started his career in research at Merck & Co., Inc., 20 years ago, transitioning to a career working at global CROs and AI-driven health technology companies, applying his knowledge in drug R&D to various commercial and corporate development positions.

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Prior to joining Deep 6, Peter was the Chief BioPharma Business Officer at SOPHiA Genetics and held senior management positions at CellCarta and Tempus AI. His focus over the past seven years has been in building and leading teams and advising on platform technologies and services that address the growing need of applying biomarker-driven and real-world patient insights to decision-making.

He holds an MBA in Pharmaceutical Management from Drexel’s LeBow College of Business and a Masters in Biomedical Chemistry from Thomas Jefferson University’s Medical College in Philadelphia.

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