Conectar
The last-minute cancellation of surgeries profoundly affects patients and their families. This research aimed to forecast these cancellations using EMR data and meteorological conditions at the time of the appointment, using a machine learning approach. We retrospectively gathered medical data from 13 440 pediatric patients slated for surgery from 2018 to 2021. Following data preprocessing, we utilized random forests, logistic regression, linear support vector machines, gradient boosting trees, and extreme gradient boosting trees to predict these abrupt cancellations. The efficacy of these models was assessed through performance metrics. The analysis revealed that key factors influencing last-minute cancellations included the impact of the coronavirus disease 2019 pandemic, average wind speed, average rainfall, preanesthetic assessments, and patient age. The extreme gradient boosting algorithm outperformed other models in predicting cancellations, boasting an area under the curve value of 0.923 and an accuracy of 0.841. This algorithm yielded superior sensitivity (0.840), precision (0.837), and F1 score (0.838) relative to the other models. These insights underscore the potential of machine learning, informed by EMRs and meteorological data, in forecasting last-minute surgical cancellations. The extreme gradient boosting algorithm holds promise for clinical deployment to curtail healthcare expenses and avert adverse patient-family experiences. Decision support systems have been widely used in healthcare in recent years; however, there is lack of evidence on global trends and hotspots. This descriptive bibliometric study aimed to analyze bibliometric patterns of decision support systems in nursing. Data were extracted from the Web of Science Core Collection. Published research articles on decision support systems in nursing were identified. Co-occurrence and co-citation analysis was performed using CiteSpace version 6.1.R2. In total, 165 articles were analyzed. A total of 358 authors and 257 institutions from 20 countries contributed to this research field. The most productive authors were Andrew Johnson, Suzanne Bakken, Alessandro Febretti, Eileen S. O'Neill, and Kathryn H. Bowles. The most productive country and institution were the United States and Duke University, respectively. The top 10 keywords were “care,” “clinical decision support,” “clinical decision support system,” “decision support system,” “electronic health record,” “system,” “nursing informatics,” “guideline,” “decision support,” and “outcomes.” Common themes on keywords were planning intervention, national health information infrastructure, and methodological challenge. This study will help to find potential partners, countries, and institutions for future researchers, practitioners, and scholars. Additionally, it will contribute to health policy development, evidence-based practice, and further studies for researchers, practitioners, and scholars. No abstract available 
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