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.

Objectives: Systematic evaluations of the benefits of health information technology (HIT) play an essential role in enhancing healthcare quality by improving outcomes. However, there is limited empirical evidence regarding the benefits of IT adoption in healthcare settings. This study aimed to review the benefits of artificial intelligence (AI), the internet of things (IoT), and personal health records (PHR), based on scientific evidence. Methods: The literature published in peer-reviewed journals between 2016 and 2022 was searched for systematic reviews and meta-analysis studies using the PubMed, Cochrane, and Embase databases. Manual searches were also performed using the reference lists of systematic reviews and eligible studies from major health informatics journals. The benefits of each HIT were assessed from multiple perspectives across four outcome domains. Results: Twenty-four systematic review or meta-analysis studies on AI, IoT, and PHR were identified. The benefits of each HIT were assessed and summarized from a multifaceted perspective, focusing on four outcome domains: clinical, psycho-behavioral, managerial, and socioeconomic. The benefits varied depending on the nature of each type of HIT and the diseases to which they were applied. Conclusions Overall, our review indicates that AI and PHR can positively impact clinical outcomes, while IoT holds potential for improving managerial efficiency. Despite ongoing research into the benefits of health IT in line with advances in healthcare, the existing evidence is limited in both volume and scope. The findings of our study can help identify areas for further investigation.