Seo-Yoon Lee, PhD, MPH, RN

Dr. Seo-Yoon Lee is an Assistant Professor at the College of Nursing whose work sits at the intersection of health services research, clinical informatics, and artificial intelligence. Before moving into research, she practiced as a bedside nurse on a hematopoietic stem cell transplant unit, an experience that continues to shape her focus on the day-to-day realities of nursing workload and patient acuity. She completed her PhD in Nursing Science at the University of Illinois Chicago and an MPH at Yonsei University, and went on to a postdoctoral appointment at the Vanderbilt University School of Nursing’s Center for Research Development and Scholarship. She holds active RN licensure in both the United States and South Korea. Dr. Lee’s research aims to build data-driven, EHR-integrated tools that help health systems better measure and manage nursing workload, ultimately supporting safer patient care and a more sustainable and reliable nursing workforce.

Dr. Lee’s program of research sits at the intersection of health services research and health informatics, using healthcare data to make the value of nursing care visible and to optimize how that care is delivered. Her work centers on a central question: how can health systems use data already captured in the electronic health record to better understand and support the real-time demands placed on nurses, so that staffing and technology decisions strengthen, rather than add to, nursing workload.

Her doctoral dissertation at the University of Illinois Chicago estimated the patient-level cost of nursing care using integrated EHR and staffing data, establishing that nursing costs represent a substantial and previously hidden share of hospital charges and are systematically linked to patient and nurse characteristics (Lee et al., 2025, CIN: Computers, Informatics, Nursing). This research agenda grew during her postdoctoral fellowship at Vanderbilt University, where she contributed to programs of research on NICU nurse workload and began extending her work into workforce and workload management more broadly. Her work draws on established frameworks, including the Donabedian Structure-Process-Outcome model and the Systems Engineering Initiative for Patient Safety (SEIPS), to connect organizational, technological, and clinical factors to clinician behavior and patient outcomes.

Dr. Lee’s research interests span four connected themes: the economic and clinical value of nursing care, care delivery optimization, the application of big health data and AI/machine learning methods to nursing science, and their downstream effects on clinician and patient outcomes. Her long-term goal is to develop reliable, sustainable intelligent health systems that optimize clinical workflows and improve outcomes for both patients and clinicians.