Purpose
The purposes of this study were to develop a prediction model for pressure injury using a machine learning algorithm and to integrate it into clinical practice.
Methods
This was a retrospective study of tertiary hospitals in Seoul, Korea. It analyzed patients in 12 departments where many pressure injuries occurred, including 8 general wards and 4 intensive care units from January 2018 to May 2022. In total, 182 variables were included in the model development. A pressure injury prediction model was developed using the gradient boosting algorithm, logistic regression, and decision tree methods, and it was compared to the Braden scale.
Results
Among the 1,389,660 general ward cases, there were 451 cases of pressure injuries, and among 139,897 intensive care unit cases, there were 297 cases of pressure injuries. Among the tested prediction models, the gradient boosting algorithm showed the highest predictive performance. The area under the receiver operating characteristic curve of the gradient boosting algorithm's pressure injury prediction model in the general ward and intensive care unit was 0.86 (95% confidence interval, 0.83~0.89) and 0.83 (95% confidence interval, 0.79~0.87), respectively. This model was integrated into the electronic health record system to show each patient's probability for pressure injury occurrence, and the risk factors calculated every hour.
Conclusion
The prediction model developed using the gradient boosting algorithm exhibited higher performance than the Braden scale. A clinical decision support system that automatically assesses pressure injury risk allows nurses to focus on patients at high risk for pressure injuries without increasing their workload.

PURPOSE
This study was done to develop and evaluate a smartphone application for the medication confirmation of high-alert medications.
METHODS
A nonequivalent control group non-synchronized design was used for this study. Participants in the treatment group used the application for four weeks. Data were analyzed using descriptive analysis, chi2-test, and t-test for the homogeneity of participants, and a paired t-test for effectiveness in each group with the SPSS 18.0.
RESULTS
Stability of medication administration was estimated by knowledge and certainty, ranged from a score of one to three. A correct answer with high certainty was coded as high stability, low certainty regardless of correct answer was coded as a moderate stability, and incorrect answers with high certainty were rated as low stability. There were no differences in 'knowledge of high alert medication', 'Certainty of knowledge', 'stability of medication administration', 'confidence of single checking medication', and 'medication safety activities' between the treatment group and the comparison group. The treatment group reported a greater difference between pretest and post-test in 'certainty of medication knowledge' (t=3.51, p=.001) than the comparison group.
CONCLUSION
Smartphone application for medication confirmation of high-alert medications will provide an important platform for reducing medication errors risk.

Citations

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