Jiyoun Song , MS, AGACNP-BC, RN »
Jonas Scholar Directory
Jiyoun Song , MS, AGACNP-BC, RNColumbia University PhD
As a predoctoral student at Columbia University, I have worked closely with the interdisciplinary research teams including “Nursing Intensity of Patient Care Needs and Rates of Healthcare-associated Infections [NIC-HAI],” and “The Center for Improving Palliative Care in Vulnerable Adults with multiple chronic conditions [CIPC].” In addition, as a graduate research assistant and data manager, I have many experiences in managing and processing big data utilizing statistical analysis software such as SAS, R, SQL, and MS Excel. I am proficient in quantitative research. I am interested in using new technologies like large data and data analytics (i.e., machine learning) to support health-care research including research on healthcare-associated infections. The real-time application of methodological approaches to comprehensively understand, identify the risk factors and accurately predict the likelihood of HAIs is my research area. During my clinical experiences as staff nursing in a surgical intensive care unit and nurse practitioner over 6 years, I have broadened my perspective in nursing and strong desire to generate the evidence as a research scientist and to apply the evidence as a clinical nurse. Also, I successfully played a role of bridging between Ph.D. student and DNP student by serving as president of the Doctoral Student Organization (DSO) of Columbia University School of Nursing, last year. With my unique background and perspectives, I look forward that my researches contribute to raising the quality of life for public health and patient care.
Research/Clinical Practice Area: Jonas Scholar – Preventive Health
Dissertation: Analyzing Risk Factors for Healthcare-Associated Infections Using Multidimensional Methodological Approaches: The overall purpose of the dissertation is to examine risk factors for HAIs and predict HAIs using multidimensional methodological approaches (i.e., integrative review, machine learning, and time-trend analysis).