Donghyun Kim, Junsang Yoo, Ye Rim Lee, Ji Sim Yoon, Seung Jin Maeng, Minha Kim, Sejin Heo, Jong Eun Park, Gun Tak Lee, Se Uk Lee, Taerim Kim, Sung Yeon Hwang, Hee Yoon, Won Chul Cha, Hansol Chang
In Press, Received September 23, 2025 Accepted December 29, 2025 Available online February 27, 2026
Objective Emergency department (ED) physicians face substantial cognitive and physical demands, yet workload data applicable to real-world staffing and operational decisions remain limited. This study aimed to quantify perceived workload across diverse ED tasks using the NASA Task Load Index (NASA-TLX) and to determine how workload varies by physician experience, patient acuity, and clinical context. A secondary aim was to generate practical insights that may inform resource allocation and experience-based task distribution in the ED.
Methods We conducted an observational survey of interns, residents, and specialists working in the ED of a tertiary hospital between June and July 2022. NASA-TLX questionnaires were administered to assess workload across common procedures and patient-care tasks. Analyses were stratified by physician experience, Korean Triage and Acuity Scale (KTAS) level, and chief complaint. Nonparametric methods were used to evaluate differences in workload patterns.
Results Sixty physicians participated (30 interns, 30 residents/specialists). Procedures with high technical complexity, such as thoracentesis and lumbar puncture, showed the highest workload among interns. Among residents, workload decreased from postgraduate year 1 to 3 but rose again in year 4, reflecting increased supervisory responsibilities. Higher patient acuity (KTAS 1–2) and neurological chief complaints were consistently associated with elevated workload across all experience levels.
Conclusion Perceived workload in the ED varies significantly by task type, experience level, and patient acuity. These findings provide actionable data that may support evidence-based staffing decisions, workload redistribution, and training strategies to optimize physician performance and mitigate cognitive overload in resource-limited emergency departments.
Objective Trauma is one of the leading causes of obstetric morbidity and mortality, and incorrect seat belt use is a major determinant of adverse outcomes. This study evaluated the impact of structured education on proper seat belt use during pregnancy and examined preferences for future interventions.
Methods This prospective pilot study was conducted at a single center in Korea, over an 8-week period. Pregnant women between 20 and 37 weeks of gestation who visited the outpatient obstetrics and gynecology department were enrolled. A pre-education survey assessed baseline seat belt practices. Participants then received structured education, and a post-education survey 1 month later evaluated changes in awareness and behavior.
Results Sixty participants were included in the final analysis. Most reported obtaining seat belt information from unverified sources, particularly online communities. Many indicated a preference for structured education delivered by obstetricians, especially early in pregnancy. After the intervention, knowledge of proper seat belt use increased from 21.7% to 85.0% (P<0.001), and correct seat belt usage rose from 16.7% to 88.3% (P<0.001). Seat belt use while driving also improved markedly, increasing from 19.5% to 78.0% (P<0.001), demonstrating the program’s effectiveness in enhancing awareness and behavior.
Conclusion Education on seat belt use during pregnancy significantly improved both knowledge and correct usage among pregnant women. Routine, formal educational programs are recommended to promote maternal and fetal safety.
Objective This study aimed to develop and validate MEDIVAL (Medical Documentation Validation), a progressive chain-of-thought (CoT) evaluation framework for automated assessment of large language model (LLM)-generated emergency department documentation, designed to align with expert clinical judgment in acute care settings. Methods We designed a three-tier evaluation framework incorporating persona-based, error-enhanced, and insight-integrated strategies. The framework was tested across four LLMs (GPT-4o, GPT-4.1, Claude-3.5, Claude-3.7) on 33 emergency department records reviewed by four expert emergency physicians. Each model applied the three CoT strategies across five criteria: appropriateness, accuracy, structure/format, conciseness, and clinical validity. Model outputs were compared with expert ratings using Spearman correlation coefficients. Differences were analyzed with the Friedman test and Wilcoxon signed rank test with Bonferroni correction. Reproducibility was assessed through intraclass correlation coefficient (ICC) analysis. Results All models demonstrated stronger alignment with expert ratings as CoT complexity increased, with Claude-3.7 (r=0.712, P<0.001) and GPT-4o (r=0.702, P<0.001) showing the highest correlations under the insight-integrated strategy. GPT-4.1 showed the greatest relative improvement (43.3% increase, r=0.457 to r=0.655, P<0.001). Significant overall differences were observed across strategies (χ2 (2)=48.39, P<0.001), though the error-enhanced and insight-integrated approaches differed only modestly yet significantly (P=0.002). High reproducibility was confirmed (ICC >0.919), with Claude-3.5 achieving the most consistent results (ICC, 0.997–0.998). Conclusion MEDIVAL demonstrates that progressive CoT strategies systematically improve automated evaluation of emergency department documentation while maintaining excellent reproducibility. This framework offers a viable prescreening tool to reduce expert workload and support reliable artificial intelligence integration into emergency medicine workflows.
Soyun Shim, Jae Yong Yu, Seyong Jekal, Yee Jun Song, Ki Tae Moon, Ju Hee Lee, Kyung Mi Yeom, Sook Hyun Park, In Sook Cho, Mi Ra Song, Sejin Heo, Jeong Hee Hong
Clin Exp Emerg Med 2022;9(4):345-353. Published online September 21, 2022
Objective Falls are one of the most frequently occurring adverse events among hospitalized patients. The Morse Fall Scale, which has been widely used for fall risk assessment, has the two limitations of low specificity and difficulty in practical implementation. The aim of this study was to develop and validate an interpretable machine learning model for prediction of falls to be integrated in an electronic medical record (EMR) system.
Methods This was a retrospective study involving a tertiary teaching hospital in Seoul, Korea. Based on the literature, 83 known predictors were grouped into seven categories. Interpretable fall event prediction models were developed using multiple machine learning models including gradient boosting and Shapley values.
Results Overall, 191,778 cases with 272 fall events (0.1%) were included in the analysis. With the validation cohort of 2020, the area under the receiver operating curve (AUROC) of the gradient boosting model was 0.817 (95% confidence interval [CI], 0.720–0.904), better performance than random forest (AUROC, 0.801; 95% CI, 0.708–0.890), logistic regression (AUROC, 0.802; 95% CI, 0.721–0.878), artificial neural net (AUROC, 0.736; 95% CI, 0.650–0.821), and conventional Morse fall score (AUROC, 0.652; 95% CI, 0.570–0.715). The model’s interpretability was enhanced at both the population and patient levels. The algorithm was later integrated into the current EMR system.
Conclusion We developed an interpretable machine learning prediction model for inpatient fall events using EMR integration formats.
Citations
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Development and Validation of Machine Learning Models for Predicting Falls Among Hospitalized Older Adults: Retrospective Cross-Sectional Study Xiyao Yang, Juan Ren, Dan Su, Manzhen Bao, Miao Zhang, Xiaoming Chen, Yanhua Li, Zonggui Wang, Xiujing Dai, Zengzeng Wei, Shuiyu Zhang, Yuxin Zhang, Juan Li, Xiaolin Li, Junjin Xu, Nan Mo JMIR Aging.2026; 9: e80602. CrossRef
Cognitive readiness of nurses regarding artificial intelligence predictions: understanding through the dual lens of verbatim and gist knowledge Insook Cho, Soyun Shim, Hyunchul Park JAMIA Open.2026;[Epub] CrossRef
Prediction of inpatient falls and key predictors using machine learning applied to electronic health records: a retrospective cohort study in a tertiary hospital in Türkiye Veysel Karani Baris, Burcu Hudaverdi BMJ Open.2026; 16(5): e113384. CrossRef
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Artificial intelligence in healthcare: transforming patient safety with intelligent systems—A systematic review Francesco De Micco, Gianmarco Di Palma, Davide Ferorelli, Anna De Benedictis, Luca Tomassini, Vittoradolfo Tambone, Mariano Cingolani, Roberto Scendoni Frontiers in Medicine.2025;[Epub] CrossRef
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