Background/Objectives: Rapid and accurate electrocardiogram (ECG) interpretation is essential for timely recognition of ST-elevation myocardial infarction (STEMI) and initiation of reperfusion therapy in the emergency department (ED). We evaluated the diagnostic performance of a real-time artificial intelligence (AI) ECG interpretation system and its pragmatic impact when integrated into routine ED workflows. Methods: This prospective, single-center pragmatic observational study was conducted in a regional emergency medical center ED in Busan, Republic of Korea (1 January–31 December 2024). Consecutive adults (≥18 years) undergoing 12-lead ECG for cardiovascular-related symptoms were enrolled (N = 1524). A predefined alternating-day protocol allocated visits to physician-only interpretation days (physician-days, n = 763) or AI-output disclosure days (AI-days, n = 761). Diagnostic performance for STEMI was assessed using paired ECG-level comparisons between physician-alone interpretation and AI output against a blinded expert-panel reference standard; clinical impact outcomes included reperfusion-related time metrics, hospital length of stay (LOS), and in-hospital mortality. Results: Against the expert reference standard, AI showed higher STEMI sensitivity than physician-alone interpretation (96.7% vs. 68.3%; McNemar p = 0.027), while specificity was lower (75.9% vs. 84.5%; p = 0.018). In pragmatic day-level comparisons, door-to-balloon time was shorter on AI-days (40.0 ± 19.81 vs. 47.34 ± 21.90 min; p = 0.001), and time to PCI was significantly reduced among patients with atypical presentations (42.3 ± 18.21 vs. 57.1 ± 20.11 min; p = 0.013). Among admitted patients, hospital LOS was shorter on AI-days (13 ± 9.21 vs. 17 ± 10.31 days; p = 0.010), whereas in-hospital mortality did not differ significantly between groups (17.0% vs. 16.77%; p = 0.191). Conclusions: Real-time AI-ECG integration in the ED was associated with improved STEMI detection sensitivity and shorter reperfusion-related time metrics, particularly in atypical presentations, and with reduced hospital LOS among admitted patients. Short-term mortality was comparable between groups. Further multicenter studies are warranted to confirm generalizability and to balance benefits against potential false-positive–related operational impacts.

Background/Objectives: Rapid and accurate electrocardiogram (ECG) interpretation is essential for timely recognition of ST-elevation myocardial infarction (STEMI) and initiation of reperfusion therapy in the emergency department (ED). We evaluated the diagnostic performance of a real-time artificial intelligence (AI) ECG interpretation system and its pragmatic impact when integrated into routine ED workflows. Methods: This prospective, single-center pragmatic observational study was conducted in a regional emergency medical center ED in Busan, Republic of Korea (1 January–31 December 2024). Consecutive adults (≥18 years) undergoing 12-lead ECG for cardiovascular-related symptoms were enrolled (N = 1524). A predefined alternating-day protocol allocated visits to physician-only interpretation days (physician-days, N = 763) or AI output disclosure days (AI-days, N = 761). Diagnostic performance for STEMI was assessed using paired ECG-level comparisons between physician-alone interpretation and AI output against a blinded expert-panel reference standard; clinical impact outcomes included reperfusion-related time metrics, hospital length of stay (LOS), and in-hospital mortality. Results: Against the expert reference standard, AI showed higher STEMI sensitivity than physician-alone interpretation (96.7% vs. 68.3%; McNemar p = 0.027), while specificity was lower (75.9% vs. 84.5%; p = 0.018). In pragmatic day-level comparisons, door-to-balloon time was shorter on AI-days (40.0 ± 19.81 vs. 47.34 ± 21.90 min; p = 0.001), and time to PCI was significantly reduced among patients with atypical presentations (42.3 ± 18.21 vs. 57.1 ± 20.11 min; p = 0.013). Among admitted patients, hospital LOS was shorter on AI-days (13 ± 9.21 vs. 17 ± 10.31 days; p = 0.010), whereas in-hospital mortality did not differ significantly between groups (17.0% vs. 16.77%; p = 0.191). Conclusions: Real-time AI-ECG integration in the ED was associated with improved STEMI detection sensitivity and shorter reperfusion-related time metrics, particularly in atypical presentations, and with reduced hospital LOS among admitted patients. Short-term mortality was comparable between groups. Further multicenter studies are warranted to confirm generalizability and to balance benefits against potential false-positive-related operational impacts.

Background: This study aimed to evaluate the association between comorbidity and hospital admission, hospital length of stay (LOS), and in-hospital mortality among adult trauma patients across the injury severity spectrum in South Korea, and to assess whether these associations vary according to injury severity. Methods: We conducted a retrospective cohort study using the national Community-Based Severe Trauma Survey (2019–2023). Adult patients (≥18 years) with trauma were included after excluding records with missing key exposure or outcome variables. Comorbidity was defined using the ICD-10–based Elixhauser comorbidity framework. In addition to a binary classification (any vs. none), comorbidity burden was categorized into 0, 1, 2, and ≥3 conditions to evaluate dose–response relationships. The primary outcomes were hospital admission, LOS, and in-hospital mortality. Multivariable logistic regression models were used for admission and mortality, and regression models were applied for LOS, adjusting for demographic characteristics, injury mechanism, physiologic status, and system-level factors. Effect modification by injury severity was assessed using interaction terms and ISS-stratified analyses. Results: Among 49,259 patients, 32,999 (67.0%) had at least one comorbidity. Patients with comorbidities were older, had higher injury severity, and showed higher admission rates, longer LOS, and higher in-hospital mortality compared with those without comorbidities. After adjustment, comorbidity remained independently associated with increased odds of admission, prolonged LOS, and in-hospital mortality. A dose–response relationship was observed, with increasing comorbidity burden associated with progressively worse outcomes (p for trend < 0.001). In addition, substantial heterogeneity was identified across individual comorbidities, with conditions such as metastatic cancer, liver disease, coagulopathy, renal disease, and fluid and electrolyte disorders showing stronger associations with adverse outcomes. The magnitude of these associations varied across ISS strata, indicating injury severity-dependent effects. Conclusions: In this nationwide cohort, comorbidity burden and type were important determinants of acute-care utilization and in-hospital mortality among trauma patients. Incorporating comorbidity information into early risk stratification may improve prognostic accuracy and support more efficient resource allocation and clinical decision-making across the trauma care continuum.

Background/Objectives: Injury mechanism and intent are key determinants of patient outcomes in the emergency department, yet their combined effects remain insufficiently understood. Emergency department disposition after injury may differ according to mechanism and intent and may be further influenced by specific mechanism–intent combinations. This study aimed to evaluate the associations of injury mechanism, intent, and their interaction with emergency department disposition and injury severity measured using the International Classification of Diseases-based Injury Severity Score (ICISS). Methods: We conducted a retrospective analysis of injury-related emergency department visits recorded between 1 January 2019 and 31 December 2023. Eligible visits included those with valid arrival and departure timestamps and complete disposition data; records with missing key variables or implausible length of stay were excluded. A total of 1,029,875 visits were analyzed. The primary outcome was emergency department disposition, categorized as discharge, admission, or transfer. Multinomial logistic regression was used to estimate relative risk ratios, with discharge as the reference category, and to derive predicted probabilities for selected mechanism–intent combinations. Injury severity was assessed using ICISS and modeled with injury mechanism, intent, their interaction, and prespecified covariates. Results: Of all visits, 69.9% resulted in discharge, 24.3% in admission, and 5.8% in transfer. Compared with traffic accidents, the highest likelihood of admission was observed in suffocation, drowning, and poisoning injuries. Transfer was more frequent in drowning, suffocation, penetrating injuries, and poisoning. Self-harm was associated with increased risks of both admission and transfer compared with unintentional injuries. Interaction analyses showed that certain combinations, particularly poisoning with self-harm and suffocation with self-harm, were associated with substantially higher risks than either factor alone. Predicted probabilities further highlighted high-risk combinations, including markedly elevated admission probabilities in self-harm-related poisoning and suffocation, and increased transfer probability in unintentional drowning. Injury mechanism, intent, and selected interactions were also significantly associated with ICISS-based injury severity. Conclusions: Injury mechanism and intent are independently associated with emergency department disposition and injury severity, with additional risk amplification observed for specific combinations. These findings suggest that mechanism–intent combinations may serve as clinically useful risk indicators in emergency department triage and decision-making, supporting improved risk stratification and system-level coordination.

Background: This study aims to verify whether the blood flow velocity and the diameter size, measured through intra-carotid artery Doppler measurements performed on sepsis patients visiting the emergency department, are useful as tools for predicting the risk of early death. Methods: As a prospective study, this research was performed on sepsis patients who visited a local emergency medical center from August 2021 to February 2023. The sepsis patients’ carotid artery was measured using Doppler imaging, and they were divided into patients measured for the size of systolic and diastolic mean blood flow velocity and diameter size: those measured for their qSOFA (quick sequential organ failure assessment) score and those measured using the SIRS (systemic inflammatory response syndrome) criteria. By measuring and comparing their mortality prediction accuracies, this study sought to verify the usefulness of blood flow velocity and the diameter size of the intra-carotid artery as tools to predict early death. Results: This study was conducted on 1026 patients, excluding 45 patients out of the total of 1071 patients. All sepsis patients were measured using systolic and diastolic blood flow velocity and diameter by Doppler imaging of the intra-carotid artery, assessed using qSOFA and evaluated using SIRS criteria. The results of the analysis performed to compare the mortality prediction accuracy were as follows. First, the hazard ratio (95% CI) of the intra-carotid artery was significant (p < 0.05), at 1.020 (1.004–1.036); the hazard ratio (95% CI) of qSOFA was significant (p < 0.05), at 3.871 (2.526–5.931); and the hazard ratio (95% CI) of SIRS showed no significant difference, at 1.002 (0.995–1.009). After 2 h of infusion treatment, the diameter size was 4.72 ± 1.23, showing a significant difference (p < 0.05). After 2 h of fluid treatment, the blood flow velocity was 101 m/s ± 21.12, which showed a significant difference (p < 0.05). Conclusions: Measuring the mean blood flow velocity in the intra-carotid arteries of sepsis patients who visit the emergency department is useful for predicting the risk of death at an early stage. And this study showed that Doppler measurement of the diameter size of the carotid artery significantly increased after performing fluid treatment after early recognition.

Background/Objectives: Emergency department length of stay (ED LOS) is a key indicator reflecting emergency department crowding, patient safety, and healthcare resource efficiency. Among injured patients, ED LOS may be prolonged depending on injury severity and disposition pathways (admission and inter-hospital transfer). This nationwide study using the Korean National Emergency Department Information System (NEDIS) aimed to (1) describe the distribution and determinants of ED LOS among injured patients and (2) quantify the mediating effects of disposition (admission and transfer) on the association between injury severity measured by the International Classification of Diseases-based Injury Severity Score (ICISS) and ED LOS. Methods: We analyzed NEDIS injury-related ED visit records collected from the date of IRB approval through 12 January 2026. We conducted a retrospective observational study using NEDIS data. Of 1,048,575 injury-related ED visits, 1,035,484 visits with valid ED LOS and eligible records were included after excluding missing key variables and implausible time values. ED LOS was calculated in minutes using arrival and departure timestamps. Injury severity was assessed using ICISS (primary: based on 15 diagnoses; sensitivity: based on 20 diagnoses). Determinants of ED LOS were evaluated using gamma regression with a log link. Disposition was categorized as discharge, admission, and inter-hospital transfer; admission and transfer were modeled as binary mediators. Causal mediation analyses estimated the average causal mediation effect (ACME), average direct effect (ADE), total effect, and proportion mediated. Multiple sensitivity analyses (outlier handling, missing-data approaches, alternative log-linear modeling, and EMS arrival subgroup analyses) assessed robustness. Results: The median ED LOS was 150 min (IQR 90–260). ED LOS differed substantially by disposition: 120 min for discharged patients, 420 min for admitted patients, and 360 min for transferred patients. Overall, 17.9% of visits had an ED LOS ≥ 6 h, and prolonged stays were concentrated among admitted (≥6 h: 55.0%) and transferred (≥6 h: 45.0%) patients. In gamma regression, a 0.05 decrease in ICISS (greater severity) was associated with longer ED LOSs in the unadjusted model (Ratio 1.34) and remained significant in the fully adjusted model (Ratio 1.12, 95% CI 1.11–1.13). Admission and transfer were strong determinants of ED LOS in the final model (ratios of 2.35 and 2.05, respectively). In mediation analyses, admission mediated 36.8% of the severity–ED LOS association (ACME 0.085; ADE 0.146), and transfer mediated 14.3% (ACME 0.033; ADE 0.198). Findings were consistent across sensitivity analyses. Conclusions: In this nationwide cohort of injured patients, ED LOS showed a right-skewed distribution, with prolonged stays concentrated in admission and transfer pathways. Injury severity (ICISS) was independently associated with longer ED LOS, and a substantial proportion of this association was mediated through admission and transfer. Reducing ED LOS among severely injured patients likely requires not only streamlining diagnostic and treatment processes but also system-level interventions targeting output-stage bottlenecks, including inpatient bed operations/boarding management and transfer coordination.