Title : Integration of data analytics into nursing curricula: A pilot study
Abstract:
Big data collection within healthcare has grown significantly over the last decade. Bi et al. (2023) report that there is a need to focus on data science literacy, given that big data processing is generated in multiple sectors of the healthcare arena. Furthermore, the National League of Nursing has listed data science and big data as key areas within nursing education that need further research development that produces innovative teaching and learning strategies in its “NLN Research Priorities in Nursing Education 2024-2027” (NLN, 2024). Graduate nurses’ and nursing students’ perceptions of big data and analytics largely reflect a tension between recognizing the importance of data competencies and feeling underprepared to meet these expectations. For instance, a 2023 study by Raghunathan et al. found that 84.5% of nursing students reported no formal informatics education in their curriculum. Similarly, a 2021 bibliometric analysis by Carter-Templeton et al. highlighted that while there is a growing interest in big data within nursing scholarships, there is notable lack of application of big data approaches in nursing education and practice. The purpose of the study was to increase the knowledge base of master’s in nursing education students use of health informatics to examine, analyze, interpret data and analyze population health and nursing education datasets by applying the data analysis process. Online learning modules were designed and placed into a Learning Management System (LMS) to assist advanced-level nursing participants with acquisition of knowledge, understanding, competency, and skills to use the Statistical Analysis System (SAS) to manage big datasets. Pre- and post-survey data were collected using the Modified Data Science Self-Efficacy Tool. Two datasets were provided for manipulation with participant screen recordings captured to demonstrate the use of SAS. Based on participant responses in the pre-survey, participants anticipated their ability in two of the five data science skillsets most strongly which included 1) the ability to formulate investigative questions that align with the nature of a problem, reflects the research and planning skillset and 2) the ability to understand the structure and characteristics of diverse datasets which reflects data management and handling skills. The weighted Likert scale average of these two skills sets was four (4) which corresponded to participants selecting ‘agree” on the Likert scale. Post-survey responses demonstrated a high level in the data science skillsets of researching and planning, and business and communication. The average of Likert score range for these skillsets was also four (4). One limitation in this pilot [case] study is that a single participant responded to the post-survey, whereas three participants responded in the pre-survey. Therefore, a follow-up study needs to be conducted with a larger sample, performing both pre- and post-assessment surveys. When data analytics are integrated into nursing curricula as a valuable clinical skill rather than a peripheral technical task, students are more likely to view it positively (Foster & Tasnim, 2020). This suggests that early, frequent, and contextually relevant exposure to data concepts is critical in shaping positive attitudes and reducing apprehension among nursing students.
