Andrew Loza, MD, PhD, recently joined Yale BIDS as an Instructor of Biomedical Informatics and Data Science. His research centers on applying statistical and deep learning methods to improve patient care, particularly by enhancing predictive models and refining data collection processes. Dr. Loza also emphasizes how his experience at the VA Connecticut Healthcare System, combined with the resources at Yale BIDS, will further advance his work. He recently shared his insights in a Q&A.
What is your primary academic focus or research specialty, and how did your background in biophysics, internal medicine, and clinical informatics shape your research direction in healthcare delivery?
AL: My primary focus is in developing statistical and deep learning methods to better understand patients’ health trajectories and clinical care delivery. I think that each step of my background has emphasized that as systems get more complex, the history and trajectory matter more and more compared to a single snapshot. Human health is complex, and to make the right diagnosis and deliver the best care requires knowing not just the current vitals or symptoms, but knowing the whole patient narrative. My background in biophysics and clinical informatics have provided a toolkit to translate these ideas into a mathematical framework.
What long-term goals do you have in biomedical informatics and data science, particularly in the context of healthcare delivery and patient outcomes? How do you see your work evolving as you continue to explore predictive models for hospitalized patients?
AL: My long-term goals are to enhance our ability to transform the data we collect into actionable information, to improve what data we collect, and to improve the software infrastructure we use to deliver this information to physicians and patients. Predictive models are a great intersection of all these ideas – a successful prediction model relies on the right inputs, the right model, and the right channels of delivery to ensure they are integrated into, rather than interrupting workflow.
How do you think the resources and collaborative environment at Yale BIDS, as well as your experience with the VA Connecticut Healthcare System, will help you achieve your goals in using transformer-based generative models for healthcare?
AL: Three things come to mind. First, creativity: our best ideas often do not emerge fully formed, but grow and develop through conversations. Having great colleagues can accelerate this process. Second scale: the ability to work with data between Yale and the VA can provide insights into practice patterns and trends beyond what a single system might show and the scale of the data can be used to reveal patterns that would otherwise be missed. And last logistics: the best ideas remain just words on a page without the ability to translate them into action. Whether this is understanding the data dictionaries within massive databases or scaling up an intervention across multiple sites, logistical knowledge is critical. This knowledge often doesn’t live in a book or come as the answer to a well-crafted prompt, but requires a group of talented individuals.
If you could meet any scientist, living or deceased, who would it be and why?
AL: I would have to pick John von Neumann. I think that few individuals have had such impact across the breadth of fields from pure mathematics to economics to nuclear and quantum physics to chemical engineering to computing. As examples, he founded the mathematical field of game theory, developed the merge sort algorithm, provided critical insights for the Manhattan project, and developed the first computational climate models. To hear his thoughts on the current state of computing and deep learning would be fascinating, not to mention that his ideas would probably accelerate the field by 10 years if I could write them down.