Faculty are awarded collaborative UMNursing grants for research into improving post-cardiac surgery outcomes and the effects of gratitude practices and meaningful recognition in inpatient settings.
photos: left to right, top row: Rachel Blankstein Breman and Eun-Shim Nahm; bottom row: Hannah McGraw and Kristin L. Seidl
The University of Maryland School of Nursing’s (UMSON) Rachel Blankstein Breman, PhD '18, MPH, RN, FAWHONN, assistant professor, and Eun-Shim Nahm, PhD '03, RN, FAAN, FGSA, professor and associate dean for the PhD program, have received one-year, $15,000 UMNursing grants, a joint venture between UMSON and the University of Maryland Medical Center (UMMC).
Breman has partnered with Hannah McGraw, MS '16, MS '11, RNC-OB, C-EFM, IBCLC, senior clinical nurse I and lactation consultant, for the project "Randomized Control Trial of Flange Fitting for NICU Pumping Parents to Explore Improved Milk Production and Satisfaction," while Nahm has partnered with Kristin L. Seidl, PhD, RN, director, nursing and patient care outcomes at UMMC with a joint appointment as an assistant professor at UMSON,
for the project "Fostering Data-Informed Nursing Workforce in the 21st Century: Development of an Instrument to Assess Health Data Science and Artificial Intelligence (DSAI) Competency."
In addition, the UMNursing grant provides $2,500 for each of the researchers, to be allocated based on requests over the course of the 12-month period.
About the "Randomized Control Trial of Flange Fitting for NICU Pumping Parents to Explore Improved Milk Production and Satisfaction" Project
In the neonatal intensive care unit (NICU), breastmilk is highly recommended for better newborn health and long-term growth, development, and overall outcomes. Premature and critically ill newborns often struggle with latching at the breast or are unable to latch at all. Because of this situtioan, parents of NICU babies are encouraged to use a breast pump to stimulate and increase milk supply to provide breastmilk for their babies.
The purpose of this study is to explore whether there is an increase in milk production and breastfeeding satisfacation with the use of the Flange FITS (TM) Guide sizing method compared to traditional care among new breasfeeding parents in the NICU.
In this randomized control trial, the researchers wil randomize breast pumping parents to either the experimental group, which is the Flange FITS Guide sizing method, or the control group, those who will get the standard flanges distributed with the Medela pump set. For primary outcomes, the researchers will measure milk production and breastfeeding satsifaction in both groups. The secondary outcome is comfort with pumping. They aim to recruit 130 participants (65 per group).
The researchers hypothesize that those in the experimental group will experience higher volumes of human milk pumped and higher (more positive) levels of satisfaction than those in the control group. With this new knowledge, the results can help support other NICUs to change their flange fitting practices for new parents who have a neonate in the NICU.
About the "Fostering a Data-Informed Nursing Workforce in the 21st Century: Development of an Instrument to Assess Health Data Science and Artificial Intelligence (DSAI) Competency" Project
The rapid growth of large health datasets and advanced analytics tools has transformed care delivery, driving innovations in patient care, health care practice, and research. By harnessing predictive analytics, care providers can detect potential health risks early and implement proactive interventions, enhancing patient outcomes.
Recent progress in artificial intelligence (AI) holds significant potential for enhancing diagnostic tests, treatments, and clinical workflows. However, these benefits can be fully realized when clinicians are equipped to use these tools, understand their outputs (e.g., risk assessment values), and effectively integrate them into practice. As health care organizations and clinical systems increasingly adopt real-time analytics and AI-powered tools, it is crucial for clinicians to be prepared to seamlessly integrate them into practice.
Compared to other clinical fields, such as medicine or dentistry, nursing has been slow to adopt data science and artificial intelligence (DSAI). Currently, little is known about the nursing workforce's understanding of DSAI and its application of the tools. Furthermore, the specific knowledge and skills clinical nurses need to effectively engage with DSAI remain undefined. Leveraging their in-depth expertise and experience in working with clinical nurses and informaticians and conducting projects in the DSAI field, the researchers aim to address this important knowledge gap.
The overarching goal of this study is to identify specific content areas and core competencies for DSAI in nursing practice and develop an assessment tool (Aim 1). Subsequently, the researchers will evaluate the instrument's preliminary psychometric properties through a pilot study (Aim 2).
The ultimate impact of the study is to advance nursing science and improve health care outcomes, including nursing-sensitive outcomes and care quality, by fostering the adoption of DSAI in nursing practice. Findings from this pilot study will generate critical preliminary data and an assessment tool needed to develop a successful R21 proposal, which will focus on conducting a large-scale survey of clinical nurses to identify specific gaps in their capacity to adopt DSAI and DSAI-driven clinical programs. It will also provide recommendations for strategies to develop effective approaches, preparing nurses for a DSAI-driven health care landscape, with the ultimate goal of improving care quality and safety.