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Nursing students’ diagnostic accuracy using a computer-based clinical scenario simulation

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posted on 29.11.2018 by Vanessa E. C. Sousa Freire, Marcos V. O. Lopes, Gail M. Keenan, Karen Dunn Lopez
Background: Being able to make accurate clinical decisions about actual or potential health problems is crucial to provide a safe and effective care. However, nursing students generally have difficulties identifying nursing diagnoses accurately. Objective: To compare the diagnostic accuracy within and across the NANDA-I diagnoses domains of junior, senior, and graduate-entry students. Design: Descriptive study. Participants and Setting: The sample comprised one hundred thirty nursing students from a Midwestern American university. Methods: The participants were divided in three groups (juniors, seniors and graduate-entry) and invited to engage in a series of diagnostic exercises presented in a software. Students were presented with 13 scenarios and asked to identify the applicable defining characteristics, related factors, and nursing diagnoses from the NANDA-I taxonomy. The number of correct answers per scenario was used to compute diagnostic accuracy. Age, gender, previous exposure to the NANDA-I taxonomy, and student level were covariates in the analysis. Results: The average percent correct answers across all groups was 64.4% and no statistical differences between the groups were found. The scenarios belonging to the Health Promotion, Self-Perception, and Growth/Development Domains were those in which students had a higher number of incorrect answers. Students also had more difficulty recognizing the correct nursing diagnoses compared with related factors and defining characteristics. Conclusions: This study found no associations between demographic variables, exposure to the NANDA-I taxonomy, or academic program level and diagnostic accuracy. Some areas in which students had a poor performance indicate need for improvement in diagnostic reasoning skills.


Source of Funding We thank financial support from the National Council for Scientific and Technological Development - CNPq (Grant No. 235237/2014-0). Acknowledgement Wise Nurse® is a registered computer software (INPI-BR 51 2015 000755-6) that was created as part of a Dissertation by V.E.C.S. Freire, and submitted to the Federal University of Ceara, Brazil. The software is currently not available for use or distribution.



Sousa Freire, V. E. C., Lopes, M. V. O., Keenan, G. M., & Dunn Lopez, K. (2018). Nursing students' diagnostic accuracy using a computer-based clinical scenario simulation. Nurse Education Today, 71, 240-246. doi:10.1016/j.nedt.2018.10.001







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