Jean Digitale received her PhD in Epidemiology and Translational Science from the University of California, San Francisco. She completed her Bachelor of Science in Nursing at the University of Pennsylvania and has a decade of experience working as a pediatric nurse, primarily in critical care. She pursued her Master of Public Health at Columbia University and subsequently worked in global health research, focusing on adolescent girls in Zambia and Malawi. Her doctoral dissertation integrated her clinical background with advanced research methodology to improve decision-making processes around pediatric extubation.
Her work is grounded in the principles of learning health systems, which leverage real-world healthcare data to drive continuous improvement in patient care. Through clinical informatics, advanced analytics, and epidemiological methods, she focuses on optimizing the use of electronic health record (EHR) data and predictive analytics (including artificial intelligence) to enhance quality and safety in hospital care. By embedding models and tools into EHR workflows, she aims to reduce preventable harm, improve patient outcomes, and advance data-driven decision-making. She collaborates with a multidisciplinary team of experts in clinical decision support, machine learning, and implementation science to ensure that data-driven innovations translate into meaningful improvements in care. She is currently evaluating tools for real-time sepsis detection and intervention. Her research is also supported by the UCSF Learning Health System Embedded Scientist Training and Research (LHS E-STAR) program.
Research Interests: Advancing healthcare quality and safety through electronic health record data, clinical informatics, and advanced analytics—including causal inference and machine learning—within learning health system frameworks.