“Making Decisions in the Complexity of Healthcare”

“Making Decisions in the Complexity of Healthcare” (Michael Liebman, PhD) [#144] Brad Power May 21, 2025 “There's ambiguity in diagnosing.

If we take any kind of clinical variable or biomarker, what we can look at in three patients is that, at some point in time, patient #1 and patient #2 may look identical in one or multiple biomarkers, but the reality is, over time, because disease is a process, not a state, they may not be actually on the same trajectories.

Similarly, patient #1 and patient #3 are on exactly the same trajectories, but when they come in for diagnosis, they come in at different stages of the disease, so their test results are not the same. This is the kind of complexity that a physician has to deal with every day to sort out the patient that they're looking at and where to actually place them.

” – Michael Liebman, PhD “Increasingly, we try to take advantage of AI, ML, and large language models to read the literature and give us some perspective of what's going on. However, in a recent study of high impact papers, fewer than half of the experiments were reproducible.

In other words, the data that's being used to generate these large language models when they aren't highly curated can be misleading and needs to be further tempered, but they can be a good starting point when care is used. Any of these need further validation and should not necessarily be considered a definitive source of evidence to make clinical decisions on.

” – Michael Liebman, PhD “One sensitive area when it comes to trust is at the beginning of the whole medical journey, when you're trying to select an oncologist or a physician or some part of the medical team. A lot of people are uncomfortable and concerned that they will offend their current doctors by requesting to have a second opinion.

Indicating that you would feel more comfortable having a second opinion and discussing it openly with your doctor should actually reinforce the direct communication you have with your physician, and if they are truly patient-centric, they will understand.

” – Michael Liebman, PhD Meeting Summary Cancer patients and caregivers face challenges in coordinating at least three complex systems: you (the patient), your disease, and the practice of medicine.

It is 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 you and about your disease may be missing, inaccurate, or not yet identified (measured), and the true understanding of disease continues to evolve.

Accurate and transparent communication with your medical team is critical to optimizing disease management and your outcomes. 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 you to ask as well as how to evaluate and interpret the many channels of information.

optimizing disease management and your outcomes. 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 you to ask as well as how to evaluate and interpret the many channels of information.

Increasingly, another wrinkle is the possible use of AI In diagnosing and treating “your cancer”, and how you can determine what information you can trust. Are analyses based on “more data” better than those only using “good data”? How can biases, known or unknown, affect your confidence in your decision-making?

“Making Decisions in the Complexity of Healthcare” (Michael Liebman, PhD) [#144] 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 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. Why do you need to pay attention to how you make medical decisions?

●To improve the accuracy and personalization of your treatment, ultimately leading to better health outcomes ●To integrate and align multiple interconnected factors - you, your disease, and medical practice ●Because your physician has limited time to make decisions ●Because there are psychological biases in decision-making that can lead to errors, as highlighted by Nobel Prize winner Daniel Kahneman's work on slow and fast thinking processes What are key challenges in making complex medical decisions?

ses in decision-making that can lead to errors, as highlighted by Nobel Prize winner Daniel Kahneman's work on slow and fast thinking processes What are key challenges in making complex medical decisions? ●Disease complexity : Diseases are processes, not static states, with varying trajectories and progression rates that are difficult to capture.

●Biomarker limitations : Current biomarkers often provide incomplete or inconsistent information, and their interpretation can vary between institutions. They represent measurable entities that we try to associate with our limited understanding of disease trajectories. ●Comorbidities: Patients often have multiple conditions that interact and complicate diagnosis and treatment.

●Heterogeneity: Patients receiving the same diagnosis can have very different underlying disease characteristics.

“Making Decisions in the Complexity of Healthcare” (Michael Liebman, PhD) [#144] What limitations in information from tests should you be aware of that can impact your medical decision-making? ●Biomarkers can be misleading because they may vary over time during disease progression, and different institutions may use different thresholds for positive/negative results.

●Test results can vary because different lab equipment and calibration can produce different results, different pathologists may interpret the same sample differently, and normal ranges are often based on population averages that may not reflect you.

●Test interpretation can be hard because tests don't capture the full complexity of your disease trajectory, comorbidities can significantly impact test interpretation, and discrete measurements might miss important trends or outliers. How can you make better medical decisions?

●Consider your disease as a dynamic trajectory rather than a static state ●Collaborate with researchers and clinicians to uncover deeper insights ●Focus on personalized approaches that recognize your variations in disease progression, lifestyle, and environmental factors How can you create a collaborative, two-way dialogue with your medical team so that they understand your unique situation and concerns and you feel fully informed and engaged in your care?

●Keep a detailed journal of symptoms, observations, and questions, and share a copy with your physician, but don’t expect them to read it during your office visit. Ask probing questions about your specific condition, such as "Are there other perspectives or approaches we should consider?

o be fully informed and confident in the treatment approach ●Use nurse navigators as bridges to help translate complex medical information ●Focus on the four key elements of trust – consistency, compassion, communication, and competency – when selecting and working with your healthcare providers How can AI help in making complex medical decisions?

●Reading and synthesizing large volumes of medical literature ●Identifying patterns in complex disease processes ●Supporting more personalized approaches to diagnosis and treatment What are some limitations of current AI tools?

“Making Decisions in the Complexity of Healthcare” (Michael Liebman, PhD) [#144] ●Large language models can be misleading if not carefully curated. ●Fewer than half of high-impact medical research experiments are reproducible.

●AI should be used as a starting point for investigation, to enhance understanding, not as a definitive source for clinical decisions, or a replacement for human expertise and clinical judgment. What are the benefits of seeking second opinions?

●Confirm your diagnosis and treatment plan ●Explore alternative treatment options ●Gain additional insights into your condition ●Increase confidence in your medical decisions How can you navigate getting a second opinion without offending your current medical team?

●Approach your current doctor by saying you want to be proactive about your health ●Ask if they can recommend a colleague for a second opinion that they would trust ●Frame it as wanting to ensure you're exploring all potential options ●Emphasize that you value their expertise and are not challenging their judgment A good physician should support your desire to be fully informed and engaged in your healthcare.

If a doctor reacts negatively to a request for a second opinion, that may be a red flag indicating you might want to seek a more patient-centered provider. How can you learn more about making medical decisions? ●See our previous conversation with Michael Liebman "Modeling Disease" [#24] ●Contact Michael Liebman at Michael.Liebman@IPQanalytics.

com ●Share your treatment needs and preferences and a journal of observations and symptoms with your medical team and ask questions ●See other conversations on cancer care decision-making: ○“Opening up Access to Cancer Data for Patients" (Frank Nothaft) [#76] ○“Using GenAI to Assist Rare Cancer Care” (Bill Paseman) [#132] ○“A Rogue Cancer Patient Gets Better Outcomes” (Ari Akerstein) [#109] ○"Decisions in Advanced Prostate Cancer" (Rick Stanton) [#8] ○“Helping Patients Navigate Cancer” (Manta Cares) [#93] The information and opinions expressed on this website or platform, or during discussions and presentations (both verbal and written) are not intended as health care recommendations or medical advice by Cancer Patient Lab, its principals, presenters, participants, or representatives for any medical treatment, product, or course of action.