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 developed a program to facilitate evidence-based practice (EBP) in one nursing organization, and identifies the effects of the program on the nurses' EBP facilitators.
METHODS
The program was based on the Transtheoretical Model of stages of organizational change, a literature review, the cases of hospitals overseas, and a prior study. To identify the effects of the program, a one-group pretest-posttest study was conducted with 45 nurses who participated in the EBP implementation.
RESULTS
The program consisted of EBP educational sessions, consultations with academic nursing faculty and clinical EBP mentors, and support from the administration and relevant departments. After the EBP program, there was a statistically significant difference in belief in the value of EBP between the pretest and the posttest (t=2.31, p=.026). However, no significant differences were found between the pretest and the posttest for organizational support to develop EBP (t=0.62, p=.537), skills in locating and evaluating research reports (s=-1.00, p=.987), knowledge of research language and skills (s=-1.00, p=.986), and time to devote to EBP (s=-23.00, p=.711).
CONCLUSION
The findings provide important data that can be used to develop and implement strategies for enhancing EBP in clinical settings in Korea.