Purpose: This study aimed to identify the incidence and related factors of delirium among cancer patients admitted to the general wards of a tertiary hospital. Methods: A retrospective analysis was performed on 9,749 adult cancer patients. Patients who screened positive for delirium were assessed using the Korean version of the Nursing Delirium Screening Scale (NuDESC). Data were analyzed using χ² tests, t-tests, and multiple logistic regression. Results: The incidence of delirium was 2.5% (n=243) based on screening results. Significant predictors included older age, longer hospital stays, emergency room admission, 90-day readmission, completion of life-sustaining treatment decisions, surgical department admission, altered consciousness (the strongest predictor), elevated white blood cell and blood urea nitrogen levels, and low albumin levels (p<.050).These 10 factors showed high discriminatory power for delirium (AUC=.91, 95% CI: 0.90~0.93). Conclusion The Nu-DESC should be actively utilized in clinical practice for early delirium detection. Nursing interventions must prioritize managing modifiable factors such as nutritional status, infection, and dehydration. Furthermore, case-sharing systems and regular education are essential to enhance nursing expertise and ensure patient safety.

Objectives: Emergency department (ED) overcrowding significantly impacts healthcare efficiency, safety, and resource management. Predictive models that utilize triage information can streamline the admission process. This review evaluates existing hospital admission prediction models that have been developed or validated using triage data for adult ED patients. Methods: A systematic search of PubMed, Embase, CINAHL, Web of Science, and the Cochrane Library was conducted. Studies were selected if they developed or validated predictive models for hospital admission using triage data from adult ED patients. Data extraction adhered to the CHARMS (Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies), and the risk of bias was evaluated using PROBAST (Prediction model Risk of Bias Assessment Tool). Results: Twenty studies met the inclusion criteria, employing logistic regression and machine learning techniques. Logistic regression was noted for its traditional use and clinical interpretability, whereas machine learning provided enhanced flexibility and potential for better predictive accuracy. Common predictors included patient demographics, triage category, vital signs, and mode of arrival. The area under the curve values for model performance ranged from 0.80 to 0.89, demonstrating strong discriminatory ability. However, external validation was limited, and there was variability in outcome definitions and model generalizability. Conclusions Predictive models based on triage data show promise in supporting ED operations by facilitating early predictions of hospital admissions, which could help decrease boarding times and enhance patient flow. Further research is necessary to validate these models in various settings to confirm their applicability and reliability.

Objectives: Emergency department (ED) overcrowding significantly impacts healthcare efficiency, safety, and resource management. Predictive models that utilize triage information can streamline the admission process. This review evaluates existing hospital admission prediction models that have been developed or validated using triage data for adult ED patients. Methods: A systematic search of PubMed, Embase, CINAHL, Web of Science, and the Cochrane Library was conducted. Studies were selected if they developed or validated predictive models for hospital admission using triage data from adult ED patients. Data extraction adhered to the CHARMS (Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies), and the risk of bias was evaluated using PROBAST (Prediction model Risk of Bias Assessment Tool). Results: Twenty studies met the inclusion criteria, employing logistic regression and machine learning techniques. Logistic regression was noted for its traditional use and clinical interpretability, whereas machine learning provided enhanced flexibility and potential for better predictive accuracy. Common predictors included patient demographics, triage category, vital signs, and mode of arrival. The area under the curve values for model performance ranged from 0.80 to 0.89, demonstrating strong discriminatory ability. However, external validation was limited, and there was variability in outcome definitions and model generalizability. Conclusions Predictive models based on triage data show promise in supporting ED operations by facilitating early predictions of hospital admissions, which could help decrease boarding times and enhance patient flow. Further research is necessary to validate these models in various settings to confirm their applicability and reliability.

Objectives: Emergency department (ED) overcrowding significantly impacts healthcare efficiency, safety, and resource management. Predictive models that utilize triage information can streamline the admission process. This review evaluates existing hospital admission prediction models that have been developed or validated using triage data for adult ED patients. Methods: A systematic search of PubMed, Embase, CINAHL, Web of Science, and the Cochrane Library was conducted. Studies were selected if they developed or validated predictive models for hospital admission using triage data from adult ED patients. Data extraction adhered to the CHARMS (Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies), and the risk of bias was evaluated using PROBAST (Prediction model Risk of Bias Assessment Tool). Results: Twenty studies met the inclusion criteria, employing logistic regression and machine learning techniques. Logistic regression was noted for its traditional use and clinical interpretability, whereas machine learning provided enhanced flexibility and potential for better predictive accuracy. Common predictors included patient demographics, triage category, vital signs, and mode of arrival. The area under the curve values for model performance ranged from 0.80 to 0.89, demonstrating strong discriminatory ability. However, external validation was limited, and there was variability in outcome definitions and model generalizability. Conclusions Predictive models based on triage data show promise in supporting ED operations by facilitating early predictions of hospital admissions, which could help decrease boarding times and enhance patient flow. Further research is necessary to validate these models in various settings to confirm their applicability and reliability.

Objectives: Education in biomedical and health informatics is essential for managing complex healthcare systems, bridging the gap between healthcare and information technology, and adapting to the digital requirements of the healthcare industry. This review presents the current status of biomedical and health informatics education domestically and internationally and proposes recommendations for future development. Methods: We analyzed evidence from reports and papers to explore global trends and international and domestic examples of education. The challenges and future strategies in Korea were also discussed based on the experts’ opinions. Results: This review presents international recommendations for establishing education in biomedical and health informatics, as well as global examples at the undergraduate and graduate levels in medical and nursing education. It provides a thorough examination of the best practices, strategies, and competencies in informatics education. The review also assesses the current state of medical informatics and nursing informatics education in Korea. We highlight the challenges faced by academic institutions and conclude with a call to action for educators to enhance the preparation of professionals to effectively utilize technology in any healthcare setting. Conclusions To adapt to the digitalization of healthcare, systematic and continuous workforce development is essential. Future education should prioritize curriculum innovations and the establishment of integrated education programs, focusing not only on students but also on educators and all healthcare personnel in the field. Addressing these challenges requires collaboration among educational institutions, academic societies, government agencies, and international bodies dedicated to systematic and continuous workforce development.