“Making Decisions in the Complexity of Healthcare” (Michael Liebman, PhD)

Cancer patients and caregivers face challenges in coordinating at least three complex systems: the patient, the disease, and the practice of medicine. It is also important to understand that disease is a process and not a state, and that complicates its diagnosis and potential management, especially since much of the critical data about the patient and about the disease may be missing, inaccurate, or not yet identified, and our understanding is continuing to evolve. Accurate and transparent communication between patients and their caregivers is critical to optimizing disease management and patient outcome. A basic understanding of the process of diagnosis, the challenges of clinical trials, and selection of treatment can lead to identifying the right questions for the patient to ask as well as how to evaluate and interpret the many channels of information. Increasingly, another wrinkle is the possible role of AI/ML In diagnosing and treating “my cancer”.

Michael N. Liebman, Ph.D (theoretical chemistry and protein crystallography) is uniquely qualified to lead a discussion on the complexities of treatment decision-making. He is the Managing Director of IPQ Analytics, LLC, after serving as the Executive Director of the Windber Research Institute from 2003-2007. He is an Adjunct Professor of Pharmacology and Physiology, Drexel College of Medicine, Resident Professor of Biology, University of Massachusetts-Lowell, and Adjunct Professor of Drug Discovery, Fudan University. Previously, he was Director, Computational Biology and Biomedical Informatics, University of Penn Cancer Center. He served as Global Head of Computational Genomics, Roche Pharmaceuticals and Director, Bioinformatics and Pharmacogenomics, Wyeth. He was Associate Professor of Pharmacology and Physiology/Biophysics at Mount Sinai School of Medicine. He is an Invited Professor, Shanghai Center for Bioinformatics Technology, and of the Chinese Academy of Sciences. He focuses on computational models of disease that stress risk detection, disease process, and clinical pathway modeling, and stratification from the clinical perspective. He utilizes systems modeling to represent for risk/benefit analysis in pharmaceutical development and healthcare. Current applications focus on women’s health: triple negative breast cancer, hypertension, and hypertensive disorders of pregnancy, infant-maternal morbidity and mortality, perimenopause-menopause transition addressing health disparities. He has launched a non-profit to focus on these women’s health issues.

Please join this discussion to get answers to questions, such as:

• How should we think about the enormously large and complex multidimensional healthcare system and learn to deal with both known unknowns and unknown unknowns?
• What are the limitations in all the testing in terms of optimizing diagnosis and treatment decisions?
• Is genetics the basis for all cancers vs. environment/lifestyle?
• Is there a difference between personalized medicine and accurate medicine?
• How can these models be applied to give more granular views of each person’s condition, and therefore more specific ideas for treatment?