Objective: This study analyzed trends in emergency department (ED) visits in South Korea using the National Emergency Department Information System (NEDIS) data from 2018 to 2022. Methods: This was a retrospective observational study using data from the NEDIS database from 2018 to 2022. Age- and sex-standardized ED visits per 100,000 population, as well as age- and sex-standardized rates for mortality, admission, and transfer, were calculated. Results: The standardized ED visits per 100,000 population was approximately 20,000 from 2018 to 2019 and decreased to about 18,000 in 2022. The standardized mortality rate ranged from 1.4% to 1.7%. The admission rate (18.4%–19.4%) and the transfer rates (1.6%–1.8%) were similar during the study period. Approximately 5.5% of patients were triaged as Korean Triage and Acuity Scale score 1 or 2. About 91% of patients visited the ED directly and 21.7% of patients visited the ED with an ambulance. The ED length of stay was less than 6 hours in 90.3% of patients and the ED mortality rate was 0.6%. Acute gastroenteritis was the most common diagnosis. Respiratory virus symptoms, such as fever and sore throat, were also common chief complaints.Conclusion: ED visits decreased during the 5-year period, while admission, transfer, and death rates remained relatively stable.

(1) Background: During a pandemic, patients and processes in the emergency department (ED) change. These circumstances affect the length of stay (LOS) or degree of crowding in the ED. The processes for patients with acute critical illness, such as cerebrovascular disease (CVD), can be also delayed. Using the process mining (PM) method, this study aimed to evaluate LOS, ED processes for CVD, and delayed processes during the coronavirus disease 2019 (COVID-19) pandemic. (2) Methods: Data were collected from the Clinical Data Warehouse of a medical center. Phase 1 included patients who visited the ED before the COVID-19 outbreak. In Phase 2, post-COVID-19 ED patients were divided into the COVID-19 tested group (CTG) and COVID-19 not tested group (CNTG) according to whether polymerase chain reaction test was performed. We analyzed patients’ ED processes before and after COVID-19 using the PM method. We analyzed patients with acute CVD separately to determine whether the process and LOS of patients with acute critical illness were changed or delayed. (3) Results: After the COVID-19 outbreak, the overall LOS was delayed and all processes in CTG patients were delayed. Registration to triage and triage were delayed in both CTG and CNTG patients. The brain imaging process for CTG patients with acute CVD was also delayed. (4) Conclusion: After a pandemic, some processes were changed, new processes were developed, and processes for patients with acute CVD who needed proper time management were not exempted.

Background: Alert fatigue is unavoidable when many irrelevant alerts are generated in response to a small number of useful alerts. It is necessary to increase the effectiveness of the clinical decision support system (CDSS) by understanding physicians’ responses. Objective: This study aimed to understand the CDSS and physicians’ behavior by evaluating the clinical appropriateness of alerts and the corresponding physicians’ responses in a medication-related passive alert system. Methods: Data on medication-related orders, alerts, and patients’ electronic medical records were analyzed. The analyzed data were generated between August 2019 and June 2020 while the patient was in the emergency department. We evaluated the appropriateness of alerts and physicians’ responses for a subset of 382 alert cases and classified them. Results: Of the 382 alert cases, only 7.3% (n=28) of the alerts were clinically appropriate. Regarding the appropriateness of the physicians’ responses about the alerts, 92.4% (n=353) were deemed appropriate. In the classification of alerts, only 3.4% (n=13) of alerts were successfully triggered, and 2.1% (n=8) were inappropriate in both alert clinical relevance and physician’s response. In this study, the override rate was 92.9% (n=355). Conclusions: We evaluated the appropriateness of alerts and physicians’ responses through a detailed medical record review of the medication-related passive alert system. An excessive number of unnecessary alerts are generated, because the algorithm operates as a rule base without reflecting the individual condition of the patient. It is important to maximize the value of the CDSS by comprehending physicians’ responses.

Bacteremia is a life-threatening condition that has increased in prevalence over the past two decades. Prompt recognition of bacteremia is important; however, identification of bacteremia requires 1 to 2 days. This retrospective cohort study, conducted from 10 November 2014 to November 2019, among patients with suspected infection who visited the emergency department (ED), aimed to develop and validate a simple tool for predicting bacteremia. The study population was randomly divided into derivation and development cohorts. Predictors of bacteremia based on the literature and logistic regression were assessed. A weighted value was assigned to predictors to develop a prediction model for bacteremia using the derivation cohort; discrimination was then assessed using the area under the receiver operating characteristic curve (AUC). Among the 22,519 patients enrolled, 18,015 were assigned to the derivation group and 4504 to the validation group. Sixteen candidate variables were selected, and all sixteen were used as significant predictors of bacteremia (model 1). Among the sixteen variables, the top five with higher odds ratio, including procalcitonin, neutrophil–lymphocyte ratio (NLR), lactate level, platelet count, and body temperature, were used for the simple bacteremia score (model 2). The proportion of bacteremia increased according to the simple bacteremia score in both cohorts. The AUC for model 1 was 0.805 (95% confidence interval [CI] 0.785–0.824) and model 2 was 0.791 (95% CI 0.772–0.810). The simple bacteremia prediction score using only five variables demonstrated a comparable performance with the model including sixteen variables using all laboratory results and vital signs. This simple score is useful for predicting bacteremia-assisted clinical decisions.

Objective: This study used a nationwide database to identify and analyze factors that influence emergency department (ED) length of stay (LOS) and improve the efficiency of emergency care.Methods: This retrospective study analyzed data from the National Emergency Department Information System (NEDIS) database in Korea: 25,578,263 ED visits from 2018 to 2022. Patient demographics, clinical characteristics, and ED operational variables were examined. Univariate and multivariate logistic regression analyses were used to assess the associations between the variables and prolonged ED LOS, defined as 6 hours or more.Results: Among the 25,578,263 patients, the median ED LOS was 2.1 hours (interquartile range, 1.050–3.830 hours), with 12.6% experiencing a prolonged ED LOS. Elderly patients (aged ≥65 years) were significantly more likely than younger patients to experience prolonged ED LOS (adjusted odds ratio [aOR], 1.415; 95% confidence interval [CI]: 1.411–1.419). Patients transferred from other hospitals (aOR, 1.469; 95% CI, 1.463–1.474) and those arriving by emergency medical services (aOR, 1.093; 95% CI, 1.077–1.108) also had high odds of prolonged LOS. Conversely, pediatric patients had a low likelihood of extended stay (aOR, 0.682; 95% CI, 0.678–0.686). Severe illness, including sepsis (aOR, 1.324; 95% CI, 1.311–1.340) and COVID-19 infection (aOR, 1.413; 95% CI, 1.399–1.427), was strongly associated with prolonged LOS.Conclusion: Prolonged ED LOS is influenced by a combination of patient demographics, clinical severity, and systemic factors. Targeted interventions for older adults, severe illness, and operational inefficiencies such as hospital transfers are essential for reducing ED LOS and improving overall emergency care delivery.