Development and Validation of Pneumonia Patients Prognosis Prediction Model in Emergency Department Disposition Time.

This study aimed to develop and evaluate an artificial intelligence model to predict 28-day mortality of pneumonia patients at the time of disposition from emergency department (ED). A multicenter retrospective study was conducted on data from pneumonia patients who visited the ED of a tertiary academic hospital for 8 months and from the Medical Information Mart for Intensive Care (MIMIC-IV) database. We combined chest X-ray information, clinical data, and CURB-65 score to develop three models with the CURB-65 score as a baseline.

Impact of an Evidence-Based Large Language Model (LLM) Diagnostic Decision Support System: A Randomised Controlled Trial.

Generative artificial intelligence (AI) influences clinical decision-making in healthcare by analyzing medical data and proposing personalized treatment options based on patient records. However, generative AI limited due to its inability to provide accurate evidence. Therefore, this study aims that the influence of AI-generated diagnostic suggestions on emergency healthcare providers' diagnostic patterns and decision-making, and evaluates the correlation between clinicians' adoption and diagnosis accuracy.

Development and validation of a transformer model-based early warning score for real-time prediction of adverse outcomes in the emergency department.

This study aimed to develop and validate a transformer-based early warning score (TEWS) system for predicting adverse events (AEs) in the emergency department (ED). We conducted a retrospective study analyzing adult ED visits at a tertiary hospital. The TEWS was developed to predict five AEs within 24 h: vasopressor use, respiratory support, intensive care unit admission, septic shock, and cardiac arrest.

Comparison of Emergency Department Overcrowding Metrics and Healthcare Providers' Subjective Assessments.

Emergency department overcrowding remains a persistent challenge despite established metrics; our comparison of hourly smartwatch-derived Subjective Overcrowding Index (SOI) with NEDOCS (r = 0.277), EDWIN (r = 0.177), and EDOR (r = 0.

Optimizing Automated KCD Coding: A Retrieval-Verification Approach.

This study proposes a two-step Retrieval-Verification system for automating the assignment of Korean Standard Classification of Diseases (KCD) codes to free-text diagnoses. The system uses SapBERT-XLMR for initial retrieval, followed by Llama 3.1 for final verification and code selection.

Prediction of contrast-associated acute kidney injury with machine-learning in patients undergoing contrast-enhanced computed tomography in emergency department.

Radiocontrast media is a major cause of nephrotoxic acute kidney injury(AKI). Contrast-enhanced CT(CE-CT) is commonly performed in emergency departments(ED). Predicting individualized risks of contrast-associated AKI(CA-AKI) in ED patients is challenging due to a narrow time window and rapid patient turnover.

The Impact of Introducing a Temporary Telemedicine Policy on Primary Care Visits: An Analysis of National Claims Data in South Korea.

: This study evaluates the impact of temporary telemedicine implementation on primary care visits, which surged during the COVID-19 pandemic in South Korea. : This study was conducted using national claims data from February 24, 2020 to February 23, 2021. The study included 1,926,300 patients with acute mild respiratory diseases and 1,031,174 patients with acute mild gastrointestinal diseases.

A Mixed Reality-Based Telesupervised Ultrasound Education Platform on 5G Network Compared to Direct Supervision: Prospective Randomized Pilot Trial.

Background: Ultrasound education is transitioning from in-person training to remote methods using mixed reality (MR) and 5G networks. Previous studies are mainly experimental, lacking randomized controlled trials in direct training scenarios.

Objective: This study aimed to compare an MR-based telesupervised ultrasound education platform on private 5G networks with traditional in-person training for novice doctors.

Prognostic Efficacy of the Albumin-Bilirubin Score and Treatment Outcomes in Hepatocellular Carcinoma: A Large-Scale, Multi-Center Real-World Database Study.

Introduction: Hepatocellular carcinoma (HCC) remains a leading cause of cancer-related mortality globally, with treatment outcomes closely tied to liver function. This study evaluates the prognostic utility of the albumin-bilirubin (ALBI) score compared to the traditional Child-Pugh (CP) grading, leveraging real-world evidence from a large-scale, multi-center database.

Methods: The Liver Cancer IN Korea (LINK) research network, a multi-center initiative, retrospectively collected electronic health records from three academic hospitals in South Korea, encompassing HCC patients diagnosed between 2015 and 2020.

Evaluation Framework of Large Language Models in Medical Documentation: Development and Usability Study.

Background: The advancement of large language models (LLMs) offers significant opportunities for health care, particularly in the generation of medical documentation. However, challenges related to ensuring the accuracy and reliability of LLM outputs, coupled with the absence of established quality standards, have raised concerns about their clinical application.

Objective: This study aimed to develop and validate an evaluation framework for assessing the accuracy and clinical applicability of LLM-generated emergency department (ED) records, aiming to enhance artificial intelligence integration in health care documentation.

A trial of a chat service for patients and their family members in an emergency department.

Background: Effective communication between patients and healthcare providers in the emergency department (ED) is challenging due to the dynamic nature of the ED environment. This study aimed to trial a chat service enabling patients in the ED and their family members to ask questions freely, exploring the service's feasibility and user experience.

Objectives: To identify the types of needs and inquiries from patients and family members in the ED that could be addressed through the chat service and to assess the user experience of the service.

Efficacy of anti-hyperkalemic agents during cardiopulmonary resuscitation in out-of-hospital cardiac arrest.

Aim: We assessed the efficacy of anti-hyperkalemic agents for alleviating hyperkalemia and improving clinical outcomes in patients with out-of-hospital cardiac arrest (OHCA).

Methods: This was a single-center, retrospective observational study of OHCA patients treated at tertiary hospitals between 2010 and 2020. Adult patients aged 18 or older who were in cardiac arrest at the time of arrival and had records of potassium levels measured during cardiac arrest were included.

ChatGPT Predicts In-Hospital All-Cause Mortality for Sepsis: In-Context Learning with the Korean Sepsis Alliance Database.

Objectives: Sepsis is a leading global cause of mortality, and predicting its outcomes is vital for improving patient care. This study explored the capabilities of ChatGPT, a state-of-the-art natural language processing model, in predicting in-hospital mortality for sepsis patients.

Methods: This study utilized data from the Korean Sepsis Alliance (KSA) database, collected between 2019 and 2021, focusing on adult intensive care unit (ICU) patients and aiming to determine whether ChatGPT could predict all-cause mortality after ICU admission at 7 and 30 days.

The potential for drug incompatibility and its drivers - A hospital wide retrospective descriptive study.

Objective: Drug incompatibility, a significant subset of medication errors, threaten patient safety during the medication administration phase. Despite the undeniably high prevalence of drug incompatibility, it is currently poorly understood because previous studies are focused predominantly on intensive care unit (ICU) settings. To enhance patient safety, it is crucial to expand our understanding of this issue from a comprehensive viewpoint.

Adoption of Augmented Reality in Educational Programs for Nurses in Intensive Care Units of Tertiary Academic Hospitals: Mixed Methods Study.

Background: In the wake of challenges brought by the COVID-19 pandemic to conventional medical education, the demand for innovative teaching methods has surged. Nurse training, with its focus on hands-on practice and self-directed learning, encountered significant hurdles with conventional approaches. Augmented reality (AR) offers a potential solution to addressing this issue.

Status and perception of point-of-care ultrasound education in Korean medical schools: A national cross-sectional study.

As point-of-care ultrasound (POCUS) is increasingly being used in clinical settings, ultrasound education is expanding into student curricula. We aimed to determine the status and awareness of POCUS education in Korean medical schools using a nationwide cross-sectional survey. In October 2021, a survey questionnaire consisting of 20 questions was distributed via e-mail to professors in the emergency medicine (EM) departments of Korean medical schools.

The Application of Knowledge-Based Clinical Decision Support Systems to Detect Antibiotic Allergy.

Prevention of drug allergies is important for patient safety. The objective of this study was to evaluate the outcomes of antibiotic allergy-checking clinical decision support system (CDSS), K-CDS. A retrospective chart review study was performed in 29 hospitals and antibiotic allergy alerts data were collected from May to August 2022.

Quantifying emergency department nursing workload at the task level using NASA-TLX: An exploratory descriptive study.

Background: Emergency departments (ED) nurses experience high mental workloads because of unpredictable work environments; however, research evaluating ED nursing workload using a tool incorporating nurses' perception is lacking. Quantify ED nursing subjective workload and explore the impact of work experience on perceived workload.

Methods: Thirty-two ED nurses at a tertiary academic hospital in the Republic of Korea were surveyed to assess their subjective workload for ED procedures using the National Aeronautics and Space Administration Task Load Index (NASA-TLX).