Introduction: Safety deviations in nursing are a serious problem. Errors by nurses in the calculation of dosages and flow of drugs can mean a risk to the health and life of patients. The accuracy of these calculations is estimated at 60-90 %. Digital technology can help reduce calculation errors. Purpose: To determine the effectiveness and reliability of the NurseCal mobile application in solving practical calculation cases that occur in the clinical environment and to check the mathematical abilities of nursing students who have already encountered the study material on the calculation of doses and flow of drugs as part of the study program. Methods of work: In April 2022, we studied the reliability of 2nd- and 3rd-year nursing students (n = 198) in calculating doses and flow rates of drugs using the NurseCal application. The control group solved seven tasks for calculating doses and drug flow without the application while the experimental group solved them using the NurseCal application. The Mann-Whitney U test was used to test for differences. Results: The results showed that students solved the tasks faster using NurseCal (U = 619; p < 0.001). They also achieved greater correctness of calculations (U = 7372; p < 0.001). The success of the correct calculation was not influenced by the year of study or the employment status. It was influenced by the self-assessment of mathematical abilities (U = 5202; p = 0.029). Based on the results, we can conclude that NurseCal is a useful and reliable tool that can reduce the number of errors in the calculations of doses and drug flow. Discussion and conclusion: With a 92.5% reliability of solving practical tasks, the NurseCal mobile application has demonstrated its potential and the possibility of integration into the educational and health system. Meanwhile, according to the established technique, employees in the healthcare system solve tasks with 68.9% reliability. To determine the usefulness of the application in clinical practice, further research on other populations and larger samples would be sensible.
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