Even though the nursing workforce is experiencing a looming shortage of nurses, schools of nursing are struggling with the selection of qualified applicants who will be able to successfully navigate nursing programs. According to a report from AACN, more than 60,000 qualified applicants are turned away each year due to rigorous admission requirements, lack of faculty and clinical spaces. One large undergraduate nursing program in the Southwest began to examine the potential of a holistic admission process to maintain diversity, improve student success and assist with filling all available slots in their traditional, second degree and veteran to BSN programs. The three programs are offered on one flagship campus, three regional campuses and two distance sites.
Methods:
A predictive analytics task force was established to identify patterns and variables influencing the selection of a diverse group of competent students with the ability to not only graduate, but also to be licensed and have a smooth transition to practice. As a holistic approach to admissions was initiated, this quality improvement project focused on retrospectively finding predictors of “unsuccessful students pre-enrollment profiles” and to use this data from large cohorts (N=907) to assist faculty in guiding matriculation of students during program completion and beyond. Results of the project are shared in de-identified student demographics and data captured retrospectively.
Results:
Of the 907 students studied, 123 were considered unsuccessful which yielded a 13.56% rate. Previously, pre-requisite science course failures were an issue for achieving success. However, the number of miles traveled from their permanent residence and the learning campus were also identified as significant factors contributing to the inability to complete the program within the designated length of the program.
Conclusion:
Careful examination of pre-requisite variables for a holistic admission process entails an in-depth examination of the patterns and trends from the existing data sets. These patterns and trends are then used to predict future outcomes with a greater accuracy for forecasting.