Paul Kalnins

Healthcare analytics and clinical data professional with a background in physiology and clinical medicine. Focused on real-world data, longitudinal analysis, and interpretable models of health and disease.

I am a healthcare analytics and clinical data professional with a background spanning clinical medicine, physiology, and computational analysis. My work focuses on understanding health, resilience, and chronic disease through the study of real-world clinical data, longitudinal patterns, and systems-level biological processes.

My research and analytical interests center on how clinical biomarkers, physiological processes, and electronic health record (EHR) data can be used to develop interpretable models that support clinical reasoning and healthcare decision-making. I am particularly interested in complexity, time-dependent processes, and multi-system interactions in human health.

Research Focus

My current areas of interest include:

  • Longitudinal and time-dependent clinical data analysis
  • Clinical and healthcare analytics using real-world datasets
  • Physiological and systems-oriented modeling perspectives
  • Digital health and wearable data interpretation
  • Methods for identifying patterns of risk, adaptation, and resilience

Analytical Background

I completed a two-year National Library of Medicine postdoctoral fellowship in bioinformatics and computational biomedicine at Oregon Health & Science University (OHSU), where my work focused on multimodal health data, predictive modeling workflows, and applied clinical data analysis. My analytical approach emphasizes interpretable, decision-focused methods, with particular interest in connecting quantitative patterns to clinically meaningful phenomena.

My earlier academic training in physics and mathematics strongly shaped my interest in dynamic systems, variability, and pattern formation. This interdisciplinary foundation continues to inform my perspective on biological and clinical data as arising from complex, interacting processes rather than isolated mechanisms.

Clinical & Academic Background

Prior to focusing on healthcare analytics and clinical data analysis, I spent over two decades in integrative clinical medicine and academic teaching. I trained as a naturopathic physician and completed graduate education in Chinese/East Asian medicine, followed by residency-based clinical practice.

I later served for more than 17 years as an assistant professor at the National University of Natural Medicine (NUNM), where I taught physiology, biochemistry, pathology, endocrinology, and related biomedical sciences, while supervising patient care in academic teaching clinics.

This clinical and educational experience continues to inform my analytical work by providing a grounded understanding of clinical workflows, biological variability, and the practical realities underlying real-world healthcare data.

Integrative Perspective

I maintain a strong interest in systems-oriented and integrative perspectives on health, particularly frameworks that emphasize organism-level regulation, pattern recognition, and multi-scale interactions. I view such models primarily as heuristic and hypothesis-generating tools that can complement empirical data analysis when applied cautiously and critically.

My central focus remains the use of quantitative methods and clinical data to investigate real-world physiological and health-related phenomena.