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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 tele-supervised ultrasound education platform on private 5G networks with traditional in-person training for novice doctors., Methods: Conducted at a tertiary academic hospital from November to December 2023, the prospective unblinded randomized controlled pilot study assigned doctors without prior abdominal ultrasound education experience to either the tele-supervision group (TG; n = 20) or direct supervision group (DG; n = 20). Participants received a 15-min video lecture, conducted ultrasound on a phantom, and had 18 images scored by two blinded experts. Additionally, the TG received five minutes of training on basic operation of a head-mounted display (HMD). Communication between doctors in the TG and supervisors was facilitated through an HMD, whereas those in the DG interacted directly with supervisors. Primary outcomes were image quality scores, while secondary outcomes included procedure time, number of supervisor interventions, user experience using NASA-Task load index (NASA-TLX), System Usability Scale (SUS), and self-confidence through pre- and post-surveys., Results: Image quality scores and procedure times showed no significant differences between the groups (TG: 66.8 ± 10.3 vs DG: 66.8 ± 10.4, P = .844; TG: 23.8 ± 8.0 min vs DG: 24.0 ± 8.1 min, P = .946). However, the TG engaged in more educational interventions (TG: 4.0 ± 2.5 vs DG: 0.8 ± 1.1, P <.001), reflecting a more interactive training environment. TG participants reported lower NASA-TLX scores for mental demand (43.8 ± 24.8 vs 60.6 ± 22.4, P = .03), effort (43.1 ± 22.9 vs 67.9 ± 17, P < .001), and frustration (26.9 ± 20.3 vs 45.2 ± 27.8, P = .022), indicating a reduced cognitive load compared the DG. The mean SUS score was also higher in the TG (66.6 ± 9.1 vs 60.2 ± 10.4, P =.046), suggesting better usability. Both groups showed significant improvements in confidence, with the TG showing notably greater improvement in abdominal ultrasound proficiency (Pre-education ━ TG: 1.6 ± 0.9 vs DG: 1.7 ± 0.9, P =.728; Post-education ━ TG: 3.8 ± 0.9 vs DG: 2.8 ± 1.0, P =.006)., Conclusions: Although no significant differences in image quality scores were observed between groups, considerable differences in positive educational interactions, workload, and usability were evident. These findings emphasize the platform's potential to enhance the ultrasound training experience, suggesting more interactive and efficient learning., Clinicaltrial: ClinicalTrials.gov, NCT06171828.

Background: Since its introduction, telemedicine for patients with chronic diseases has been studied in various clinical settings. However, there is limited evidence of the effectiveness and medical safety of the nationwide adoption of telemedicine., Objective: This study aimed to analyze the effects of telemedicine on chronic diseases during the COVID-19 pandemic under a temporary telemedicine policy in South Korea using national claims data., Methods: Health insurance claims data were extracted over 2 years: 1 year before (from February 24, 2019, to February 23, 2020) and 1 year after the policy was implemented (from February 24, 2020, to February 23, 2021). We included all patients who used telemedicine at least once in the first year after the policy was implemented and compared them with a control group of patients who never used telemedicine. The comparison focused on health care use; the medication possession ratio (MPR); and admission rates to general wards (GWs), emergency departments (EDs), and intensive care units (ICUs) using difference-in-differences analysis. A total of 4 chronic diseases were targeted: hypertension, diabetes mellitus (DM), chronic obstructive pulmonary disease (COPD), and common mental disorders., Results: A total of 1,773,454 patients with hypertension; 795,869 patients with DM; 37,460 patients with COPD; and 167,084 patients with common mental disorders were analyzed in this study. Patients diagnosed with hypertension or DM showed increased MPRs without an increase in GW, ED, or ICU admission rates during the policy year. Moreover, patients in the DM group who did not use telemedicine had higher rates of ED, GW, and ICU admissions, and patients in the hypertension group had higher rates of GW or ICU admissions after 1 year of policy implementation. This trend was not evident in COPD and common mental disorders., Conclusions: The temporary telemedicine policy was effective in increasing medication adherence and reducing admission rates for patients with hypertension and DM; however, the efficacy of the policy was limited for patients with COPD and common mental disorders. Future studies are required to demonstrate the long-term effects of telemedicine policies with various outcome measures reflecting disease characteristics., (©Ji Ye Kang, Weon Jung, Hyun Ji Kim, Ji Hyun An, Hee Yoon, Taerim Kim, Hansol Chang, Sung Yeon Hwang, Jong Eun Park, Gun Tak Lee, Won Chul Cha, Sejin Heo, Se Uk Lee. Originally published in JMIR Public Health and Surveillance (https://publichealth.jmir.org), 20.11.2024.)

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., Methods: We organized the Healthcare Prompt-a-thon, a competitive event designed to explore the capabilities of LLMs in generating accurate medical records. The event involved 52 participants who generated 33 initial ED records using HyperCLOVA X, a Korean-specialized LLM. We applied a dual evaluation approach. First, clinical evaluation: 4 medical professionals evaluated the records using a 5-point Likert scale across 5 criteria-appropriateness, accuracy, structure/format, conciseness, and clinical validity. Second, quantitative evaluation: We developed a framework to categorize and count errors in the LLM outputs, identifying 7 key error types. Statistical methods, including Pearson correlation and intraclass correlation coefficients (ICC), were used to assess consistency and agreement among evaluators., Results: The clinical evaluation demonstrated strong interrater reliability, with ICC values ranging from 0.653 to 0.887 (P<.001), and a test-retest reliability Pearson correlation coefficient of 0.776 (P<.001). Quantitative analysis revealed that invalid generation errors were the most common, constituting 35.38% of total errors, while structural malformation errors had the most significant negative impact on the clinical evaluation score (Pearson r=-0.654; P<.001). A strong negative correlation was found between the number of quantitative errors and clinical evaluation scores (Pearson r=-0.633; P<.001), indicating that higher error rates corresponded to lower clinical acceptability., Conclusions: Our research provides robust support for the reliability and clinical acceptability of the proposed evaluation framework. It underscores the framework's potential to mitigate clinical burdens and foster the responsible integration of artificial intelligence technologies in health care, suggesting a promising direction for future research and practical applications in the field., (©Junhyuk Seo, Dasol Choi, Taerim Kim, Won Chul Cha, Minha Kim, Haanju Yoo, Namkee Oh, YongJin Yi, Kye Hwa Lee, Edward Choi. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 20.11.2024.)

Introduction: Preparing appropriate red blood cells (RBCs) before surgery is crucial for improving both the efficacy of perioperative workflow and patient safety. In particular, thoracic surgery (TS) is a procedure that requires massive transfusion with high variability for each patient. Hence, the precise prediction of RBC requirements for individual patients is becoming increasingly important. This study aimed to 1) develop and validate a machine learning algorithm for personalized RBC predictions for TS patients and 2) assess the usability of a clinical decision support system (CDSS) integrating this artificial intelligence model., Methods: Adult patients who underwent TS between January 2016 and October 2021 were included in this study. Multiple models were developed by employing both traditional statistical- and machine-learning approaches. The primary outcome evaluated the model's performance in predicting RBC requirements through root mean square error and adjusted R 2 . Surgeons and informaticians determined the precision MSBOS-Thoracic Surgery (pMSBOS-TS) algorithm through a consensus process. The usability of the pMSBOS-TS was assessed using the System Usability Scale (SUS) survey with 60 clinicians., Results: We identified 7,843 cases (6,200 for training and 1,643 for test sets) of TSs. Among the models with variable performance indices, the extreme gradient boosting model was selected as the pMSBOS-TS algorithm. The pMSBOS-TS model showed statistically significant lower root mean square error (mean: 3.203 and 95% confidence interval [CI]: 3.186-3.220) compared to the calculated Maximum Surgical Blood Ordering Schedule (MSBOS) and a higher adjusted R 2 (mean: 0.399 and 95% CI: 0.395-0.403) compared to the calculated MSBOS, while requiring approximately 200 fewer packs for RBC preparation compared to the calculated MSBOS. The SUS score of the pMSBOS-TS CDSS was 72.5 points, indicating good acceptability., Conclusions: We successfully developed the pMSBOS-TS capable of predicting personalized RBC transfusion requirements for perioperative patients undergoing TS., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: [Financial support for this study was provided entirely by a grant from the Ministry of Health & Welfare, Republic of Korea. The funding agreement ensured the authors’ independence in designing the study, interpreting the data, writing, and publishing the report.]., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

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. This study aims to investigate the prevalence and mechanism of drug incompatibility by analysing hospital-wide prescription and administration data., Methods: This retrospective cross-sectional study, conducted at a tertiary academic hospital, included data extracted from the clinical data warehouse of the study institution on patients admitted between January 1, 2021, and May 31, 2021. Potential contacts in drug pairs (PCs) were identified using the study site clinical workflow. Drug incompatibility for each PC was determined by using a commercial drug incompatibility database, the Trissel's™ 2 Clinical Pharmaceutics Database (Trissel's 2 database). Drivers of drug incompatibility were identified, based on a descriptive analysis, after which, multivariate logistic regression was conducted to assess the risk factors for experiencing one or more drug incompatibilities during admission., Results: Among 30,359 patients (representing 40,061 hospitalisations), 24,270 patients (32,912 hospitalisations) with 764,501 drug prescriptions (1,001,685 IV administrations) were analysed, after checking for eligibility. Based on the rule for determining PCs, 5,813,794 cases of PCs were identified. Among these, 25,108 (0.4 %) cases were incompatible PCs: 391 (1.6 %) PCs occurred during the prescription process and 24,717 (98.4 %) PCs during the administration process. By classifying these results, we identified the following drivers contributing to drug incompatibility: incorrect order factor; incorrect administration factor; and lack of related research. In multivariate analysis, the risk of encountering incompatible PCs was higher for patients who were male, older, with longer lengths of stay, with higher comorbidity, and admitted to medical ICUs., Conclusions: We comprehensively described the current state of drug incompatibility by analysing hospital-wide drug prescription and administration data. The results showed that drug incompatibility frequently occurs in clinical settings., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Junsang Yoo and Won Chul Cha reports disclose financial support from National Research Foundation of Korea (NRF). The results, discussion, and conclusion of this paper are independent of the funding source., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

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., Methods: We enrolled patients and family members aged over 19 years in the ED, providing the chat service for up to 4 h per ED visit. Trained research nurses followed specific guidelines to respond to messages from the participants. After participation, participants were required to complete a survey. Those who agreed also participated in interviews to provide insights on their experiences with the ED chat service., Results: A total of 40 participants (20 patients and 20 family members) sent 305 messages (72 by patients and 233 by family members), with patients sending an average of 3.6 messages and family members 11.7. Research nurses resolved 41.4% of patient inquiries and 70.9% of family member inquiries without further healthcare provider involvement. High usability was reported, with positive feedback on communication with healthcare workers, information accessibility, and emotional support., Conclusions: The ED chat service was found to be feasible and led to positive user experiences for both patients and their family members., (© 2024. The Author(s).)

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. A linear regression model was used to evaluate the relationship between changes in potassium levels and use of anti-hyperkalemic medications. Cox proportional hazards regression analysis was performed to analyze the relationship between the use of anti-hyperkalemic agents and the achievement of return of spontaneous circulation (ROSC)., Results: Among 839 episodes, 465 patients received anti-hyperkalemic medication before ROSC. The rate of ROSC was higher in the no anti-hyperkalemic group than in the anti-hyperkalemic group (55.9 % vs 47.7 %, P = 0.019). The decrease in potassium level in the anti-hyperkalemic group from pre-ROSC to post-ROSC was significantly greater than that in the no anti-hyperkalemic group (coefficient 0.38, 95 % confidence interval [CI], 0.13-0.64, P = 0.003). In Cox proportional hazards regression analysis, the use of anti-hyperkalemic medication was related to a decreased ROSC rate in the overall group (adjusted hazard ratio [aHR] 0.66, 95 % CI, 0.54-0.81, P < 0.001), but there were no differences among subgroups classified according to initial potassium levels., Conclusions: Anti-hyperkalemic agents were associated with substantial decreases in potassium levels in OHCA patients. However, administration of anti-hyperkalemic agents did not affect the achievement of ROSC., Competing Interests: The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:Tae Gun Shin reports financial support was provided by 10.13039/501100003725National Research Foundation of Korea. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Author(s).)

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. Structured prompts enabled ChatGPT to engage in in-context learning, with the number of patient examples varying from zero to six. The predictive capabilities of ChatGPT-3.5-turbo and ChatGPT-4 were then compared against a gradient boosting model (GBM) using various performance metrics., Results: From the KSA database, 4,786 patients formed the 7-day mortality prediction dataset, of whom 718 died, and 4,025 patients formed the 30-day dataset, with 1,368 deaths. Age and clinical markers (e.g., Sequential Organ Failure Assessment score and lactic acid levels) showed significant differences between survivors and non-survivors in both datasets. For 7-day mortality predictions, the area under the receiver operating characteristic curve (AUROC) was 0.70-0.83 for GPT-4, 0.51-0.70 for GPT-3.5, and 0.79 for GBM. The AUROC for 30-day mortality was 0.51-0.59 for GPT-4, 0.47-0.57 for GPT-3.5, and 0.76 for GBM. Zero-shot predictions using GPT-4 for mortality from ICU admission to day 30 showed AUROCs from the mid-0.60s to 0.75 for GPT-4 and mainly from 0.47 to 0.63 for GPT-3.5., Conclusions: GPT-4 demonstrated potential in predicting short-term in-hospital mortality, although its performance varied across different evaluation metrics.

Background: Accurate prediction of renal function following kidney donation and careful selection of living donors are essential for living-kidney donation programs. We aimed to develop a prediction model for post-donation renal function following living kidney donation using machine learning., Methods: This retrospective cohort study was conducted with 823 living kidney donors between 2009 and 2020. The dataset was randomly split into training (80%) and test sets (20%). The main outcome was the post-donation estimated glomerular filtration rate (eGFR) 12 months after nephrectomy. We compared the performance of machine learning techniques, traditional regression models, and models from previous studies. The best-performing model was selected based on the mean absolute error (MAE) and root mean square error (RMSE)., Results: The mean age of the participants was 45.2 ± 12.3 years, and 48.4% were males. The mean pre-donation and post-donation eGFRs were 101.3 ± 13.0 and 68.8 ± 12.7 mL/min/1.73 m 2 , respectively. The XGBoost model with the eGFR, age, serum creatinine, 24-h urine creatinine, 24-h urine sodium, creatinine clearance, cystatin C, cystatin C-based eGFR, computed tomography volume of the remaining kidney/body weight, normalized GFR of the remaining kidney measured through a diethylenetriaminepentaacetic acid scan, and sex, showed the best performance with a mean absolute error of 6.23 and root mean square error of 8.06. An easy-to-use web application titled Kidney Donation with Nephrologic Intelligence (KDNI) was developed., Conclusions: The prediction model using XGBoost accurately predicted the post-donation eGFR after living kidney donation. This model can be applied in clinical practice using KDNI, the developed web application., (© 2024. The Author(s) under exclusive licence to Italian Society of Nephrology.)

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). Nonparametric statistical analysis was performed to describe the data, and linear regression analysis was conducted to estimate the impact of work experience on perceived workload., Results: Cardiopulmonary resuscitation (CPR) had the highest median workload, followed by interruption from a patient and their family members. Although inexperienced nurses perceived the 'special care' procedures (CPR and defibrillation) as more challenging compared with other categories, analysis revealed that nurses with more than 107 months of experience reported a significantly higher workload than those with less than 36 months of experience., Conclusion: Addressing interruptions and customizing training can alleviate ED nursing workload. Quantified perceived workload is useful for identifying acceptable thresholds to maintain optimal workload, which ultimately contributes to predicting nursing staffing needs and ED crowding., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Ltd.)

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., Objective: The aim of this study was to develop, introduce, and evaluate an AR-based educational program designed for nurses, focusing on its potential to facilitate hands-on practice and self-directed learning., Methods: An AR-based educational program for nursing was developed anchored by the Kern six-step framework. First, we identified challenges in conventional teaching methods through interviews and literature reviews. Interviews highlighted the need for hands-on practice and on-site self-directed learning with feedback from a remote site. The training goals of the platform were established by expert trainers and researchers, focusing on the utilization of a ventilator and extracorporeal membrane oxygenation system. Intensive care nurses were enrolled to evaluate AR education. We then assessed usability and acceptability of the AR training using the System Usability Scale and Technology Acceptance Model with intensive care nurses who agreed to test the new platform. Additionally, selected participants provided deeper insights through semistructured interviews., Results: This study highlights feasibility and key considerations for implementing an AR-based educational program for intensive care unit nurses, focusing on training objectives of the platform. Implemented over 2 months using Microsoft Dynamics 365 Guides and HoloLens 2, 28 participants were trained. Feedback gathered through interviews with the trainers and trainees indicated a positive reception. In particular, the trainees mentioned finding AR particularly useful for hands-on learning, appreciating its realism and the ability for repetitive practice. However, some challenges such as difficulty in adapting to the new technology were expressed. Overall, AR exhibits potential as a supplementary tool in nurse education., Conclusions: To our knowledge, this is the first study to substitute conventional methods with AR in this specific area of critical care nursing. These results indicate the multiple principal factors to take into consideration when adopting AR education in hospitals. AR is effective in promoting self-directed learning and hands-on practice, with participants displaying active engagement and enhanced skill acquisition., Trial Registration: ClinicalTrials.gov NCT05629663; https://clinicaltrials.gov/study/NCT05629663., (©Suyoung Yoo, Sejin Heo, Soojin Song, Aeyoung Park, Hyunchung Cho, Yuna Kim, Won Chul Cha, Kyeongsug Kim, Meong Hi Son. Originally published in JMIR Serious Games (https://games.jmir.org), 23.05.2024.)

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 questionnaire encompassed 19 multiple-choice questions covering demographics, current education, perceptions, and barriers, and the final question was an open-ended inquiry seeking suggestions for POCUS education. All EM departments of the 40 medical schools responded, of which only 13 (33%) reported providing POCUS education. The implementation of POCUS education primarily occurred in the third and fourth years, with less than 4 hours of dedicated training time. Five schools offered a hands-on education. Among schools offering ultrasound education, POCUS training for trauma cases is the most common. Eight schools had designated professors responsible for POCUS education and only 2 possessed educational ultrasound devices. Of the respondents, 64% expressed the belief that POCUS education for medical students is necessary, whereas 36%, including those with neutral opinions, did not anticipate its importance. The identified barriers to POCUS education included faculty shortages (83%), infrastructure limitations (76%), training time constraints (74%), and a limited awareness of POCUS (29%). POCUS education in Korean medical schools was limited to a minority of EM departments (33%). To successfully implement POCUS education in medical curricula, it is crucial to clarify learning objectives, enhance faculty recognition, and improve the infrastructure. These findings provide valuable insights for advancing ultrasound training in medical schools to ensure the provision of high-quality POCUS education for future healthcare professionals., Competing Interests: The authors have no conflicts of interest to disclose., (Copyright © 2024 the Author(s). Published by Wolters Kluwer Health, Inc.)

An independent correlation between pre-RDW and 1-year mortality after surgery in elderly hip fracture can be used to predict mortality in elderly hip fracture patients and has predictive significance in anemia patients. With further research, a treatment algorithm can be developed to potentially identify patients at high risk of preoperative mortality., Introduction: Red blood cell distribution width (RDW) is an independent predictor of various disease states in elderly individuals, but its association with the prognosis of elderly hip fracture patients is controversial. This study aimed to evaluate the prognostic value of RDW in such patients, construct a prediction model containing RDW using random survival forest (RSF) and Cox regression analysis, and compare RDW in patients with and without anemia., Methods: We retrospectively analyzed the data of elderly patients who underwent hip fracture surgery, selected the best variables using RSF, stratified the independent variables by Cox regression analysis, constructed a 1-year mortality prediction model of elderly hip fracture with RDW, and conducted internal validation and external validation., Results: Two thousand one hundred six patients were included in this study. The RSF algorithm selects 12 important influencing factors, and Cox regression analysis showed that eight variables including preoperative RDW (pre-RDW) were independent risk factors for death within 1-year after hip fracture surgery in elderly patients. Stratified analysis showed that pre-RDW was still independently associated with 1-year mortality in the non-anemia group and not in the anemia group. The nomogram prediction model had high differentiation and fit, and the prediction model constructed by the total cohort of patients was also used for validation of patients in the anemia patients and obtained good clinical benefits., Conclusion: An independent correlation between pre-RDW and 1-year mortality after surgery in elderly hip fracture can be used to predict mortality in elderly hip fracture patients and has predictive significance in anemia patients., (© 2023. International Osteoporosis Foundation and Bone Health and Osteoporosis Foundation.)

Emergency departments (ED) are complex, triage is a main task in the ED to prioritize patient with limited medical resources who need them most. Machine learning (ML) based ED triage tool, Score for Emergency Risk Prediction (SERP), was previously developed using an interpretable ML framework with single center. We aimed to develop SERP with 3 Korean multicenter cohorts based on common data model (CDM) without data sharing and compare performance with inter-hospital validation design. This retrospective cohort study included all adult emergency visit patients of 3 hospitals in Korea from 2016 to 2017. We adopted CDM for the standardized multicenter research. The outcome of interest was 2-day mortality after the patients' ED visit. We developed each hospital SERP using interpretable ML framework and validated inter-hospital wisely. We accessed the performance of each hospital's score based on some metrics considering data imbalance strategy. The study population for each hospital included 87,670, 83,363 and 54,423 ED visits from 2016 to 2017. The 2-day mortality rate were 0.51%, 0.56% and 0.65%. Validation results showed accurate for inter hospital validation which has at least AUROC of 0.899 (0.858-0.940). We developed multicenter based Interpretable ML model using CDM for 2-day mortality prediction and executed Inter-hospital external validation which showed enough high accuracy., (© 2024. The Author(s).)

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 TM . A retrospective chart review study was performed in 29 hospitals and antibiotic allergy alerts data were collected from May to August 2022. A total of 15,535 allergy alert cases from 1586 patients were reviewed. The most frequently prescribed antibiotics were cephalosporins (48.5%), and there were more alerts of potential cross-reactivity between beta-lactam antibiotics than between antibiotics with the same ingredients or of the same class. Regarding allergy symptoms, dermatological disorders were the most common (38.8%), followed by gastrointestinal disorders (28.4%). The 714 cases (4.5%) of immune system disorders included 222 cases of anaphylaxis and 61 cases of severe cutaneous adverse reactions. Alerts for severe symptoms were reported in 6.4% of all cases. This study confirmed that K-CDS can effectively detect antibiotic allergies and prevent the prescription of potentially allergy-causing antibiotics among patients with a history of antibiotic allergies. If K-CDS is expanded to medical institutions nationwide in the future, it can prevent an increase in allergy recurrence related to drug prescriptions through cloud-based allergy detection CDSSs.

Background: Due to the ongoing effects of climate change, the incidence of heatwave-related mortality is rising globally. Improved allocation and utilization of healthcare resources could help alleviate this issue. This study aimed to identify healthcare resource factors associated with heatwave-related mortality in seven major cities of South Korea., Methods: We analyzed daily time-series data on mean temperature and all-cause mortality from 2011 to 2019. Using principal component analysis (PCA), we clustered district-level healthcare resource indicators into three principal components (PCs). To estimate district-specific heatwave-mortality risk, we used a distributed lag model with a quasi-Poisson distribution. Furthermore, a meta-regression was performed to examine the association between healthcare resources and heatwave-mortality risk., Findings: A total of 310,363 deaths were analyzed in 74 districts. The lag-cumulative heatwave-related mortality (RRs) ranged from 1.12 (95% confidence interval [CI]: 1.07, 1.17) to 1.21 (95% CI 1.05, 1.38), depending on the definitions used for heatwaves. Of the three PCs for healthcare resources (PC1: pre-hospital emergency medical service, PC2: hospital resources, PC3: timely access), timely access was associated with reduced risk of heatwave-related mortality, particularly among the elderly. Specifically, timely access to any emergency room (ER) exhibited the strongest association with lower heatwave-related mortality., Interpretation: Our findings suggest that timely access to any ER is more effective in reducing heatwave-related mortality risk than access to higher-level healthcare facilities, especially among the elderly. Therefore, healthcare resource factors and ER accessibility should be prioritized when identifying vulnerable populations for heatwaves, along with known individual and socio-demographic factors., Funding: This work was supported by the Research Program funded by the Korea Disease Control and Prevention Agency (2022-12-303), the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No. 2022R1A2C2092353) and the MD-PhD/Medical Scientist Training Program through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea., Competing Interests: We declare no competing interests., (© 2024 The Authors.)

Objectives: Medical artificial intelligence (AI) has recently attracted considerable attention. However, training medical AI models is challenging due to privacy-protection regulations. Among the proposed solutions, federated learning (FL) stands out. FL involves transmitting only model parameters without sharing the original data, making it particularly suitable for the medical field, where data privacy is paramount. This study reviews the application of FL in the medical domain., Methods: We conducted a literature search using the keywords "federated learning" in combination with "medical," "healthcare," or "clinical" on Google Scholar and PubMed. After reviewing titles and abstracts, 58 papers were selected for analysis. These FL studies were categorized based on the types of data used, the target disease, the use of open datasets, the local model of FL, and the neural network model. We also examined issues related to heterogeneity and security., Results: In the investigated FL studies, the most commonly used data type was image data, and the most studied target diseases were cancer and COVID-19. The majority of studies utilized open datasets. Furthermore, 72% of the FL articles addressed heterogeneity issues, while 50% discussed security concerns., Conclusions: FL in the medical domain appears to be in its early stages, with most research using open data and focusing on specific data types and diseases for performance verification purposes. Nonetheless, medical FL research is anticipated to be increasingly applied and to become a vital component of multi-institutional research.

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.

A knowledgebase (KB) transition of a clinical decision support (CDS) system occurred at the study site. The transition was made from one commercial database to another, provided by a different vendor. The change was applied to all medications in the institute. The aim of this study was to analyze the effect of KB transition on medication-related orders and alert patterns in an emergency department (ED). Data of patients, medication-related orders and alerts, and physicians in the ED from January 2018 to December 2020 were analyzed in this study. A set of definitions was set to define orders, alerts, and alert overrides. Changes in order and alert patterns before and after the conversion, which took place in May 2019, were assessed. Overall, 101,450 patients visited the ED, and 1325 physicians made 829,474 prescription orders to patients during visit and at discharge. Alert rates (alert count divided by order count) for periods A and B were 12.6% and 14.1%, and override rates (alert override count divided by alert count) were 60.8% and 67.4%, respectively. Of the 296 drugs that were used more than 100 times during each period, 64.5% of the drugs had an increase in alert rate after the transition. Changes in alert rates were tested using chi-squared test and Fisher's exact test. We found that the CDS system knowledgebase transition was associated with a significant change in alert patterns at the medication level in the ED. Careful consideration is advised when such a transition is performed., (© 2023. The Author(s).)

To save time during transport, where resuscitation quality can degrade in a moving ambulance, it would be prudent to continue the resuscitation on scene if there is a high likelihood of ROSC occurring at the scene. We developed the pre-hospital real-time cardiac arrest outcome prediction (PReCAP) model to predict ROSC at the scene using prehospital input variables with time-adaptive cohort. The patient survival at discharge from the emergency department (ED), the 30-day survival rate, and the final Cerebral Performance Category (CPC) were secondary prediction outcomes in this study. The Pan-Asian Resuscitation Outcome Study (PAROS) database, which includes out-of-hospital cardiac arrest (OHCA) patients transferred by emergency medical service in Asia between 2009 and 2018, was utilized for this study. From the variables available in the PAROS database, we selected relevant variables to predict OHCA outcomes. Light gradient-boosting machine (LightGBM) was used to build the PReCAP model. Between 2009 and 2018, 157,654 patients in the PAROS database were enrolled in our study. In terms of prediction of ROSC on scene, the PReCAP had an AUROC score between 0.85 and 0.87. The PReCAP had an AUROC score between 0.91 and 0.93 for predicting survived to discharge from ED, and an AUROC score between 0.80 and 0.86 for predicting the 30-day survival. The PReCAP predicted CPC with an AUROC score ranging from 0.84 to 0.91. The feature importance differed with time in the PReCAP model prediction of ROSC on scene. Using the PAROS database, PReCAP predicted ROSC on scene, survival to discharge from ED, 30-day survival, and CPC for each minute with an AUROC score ranging from 0.8 to 0.93. As this model used a multi-national database, it might be applicable for a variety of environments and populations., (© 2023. The Author(s).)

Background: Anxiety and communication difficulties in the emergency department (ED) may increase for various reasons, including isolation due to coronavirus disease 2019 (COVID-19). However, little research on anxiety and communication in EDs exists. This study explored the isolation-related anxiety and communication experiences of ED patients during the COVID-19 pandemic., Methods: A prospective mixed-methods study was conducted from May to August 2021 at the Samsung Medical Center ED, Seoul. There were two patient groups: isolation and control. Patients measured their anxiety using the State-Trait Anxiety Inventory (STAI X1) at two time points, and we surveyed patients at two time points about factors contributing to their anxiety and communication experiences. These were measured through a mobile web-based survey. Researchers interviewed patients after their discharge., Results: ED patients were not anxious regardless of isolation, and there was no statistical significance between each group at the two time points. STAI X1 was 48.4 (standard deviation [SD], 8.0) and 47.3 (SD, 10.9) for early follow-up and 46.3 (SD, 13.0) and 46.2 (SD, 13.6) for late follow-up for the isolation and control groups, respectively. The clinical process was the greatest factor contributing to anxiety as opposed to the physical environment or communication. Communication was satisfactory in 71.4% of the isolation group and 66.7% of the control group. The most important aspects of communication were information about the clinical process and patient status., Conclusion: ED patients were not anxious and were generally satisfied with medical providers' communication regardless of their isolation status. However, patients need clinical process information for anxiety reduction and better communication., Competing Interests: The authors have no potential conflicts of interest to disclose., (© 2023 The Korean Academy of Medical Sciences.)

Abstract: Objective/Introduction : Sequential vital-sign information and trends in vital signs are useful for predicting changes in patient state. This study aims to predict latent shock by observing sequential changes in patient vital signs. Methods : The dataset for this retrospective study contained a total of 93,194 emergency department (ED) visits from January 1, 2016, and December 31, 2020, and Medical Information Mart for Intensive Care (MIMIC)-IV-ED data. We further divided the data into training and validation datasets by random sampling without replacement at a 7:3 ratio. We carried out external validation with MIMIC-IV-ED. Our prediction model included logistic regression (LR), random forest (RF) classifier, a multilayer perceptron (MLP), and a recurrent neural network (RNN). To analyze the model performance, we used area under the receiver operating characteristic curve (AUROC). Results : Data of 89,250 visits of patients who met prespecified criteria were used to develop a latent-shock prediction model. Data of 142,250 patient visits from MIMIC-IV-ED satisfying the same inclusion criteria were used for external validation of the prediction model. The AUROC values of prediction for latent shock were 0.822, 0.841, 0.852, and 0.830 with RNN, MLP, RF, and LR methods, respectively, at 3 h before latent shock. This is higher than the shock index or adjusted shock index. Conclusion : We developed a latent shock prediction model based on 24 h of vital-sign sequence that changed with time and predicted the results by individual., Competing Interests: The authors report no conflicts of interest., (Copyright © 2023 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the Shock Society.)

Background and Aims: This study developed a clinical support system based on federated learning to predict the need for a revised Korea Triage Acuity Scale (KTAS) to facilitate triage., Methods: This was a retrospective study that used data from 11,952,887 patients in the Korean National Emergency Department Information System (NEDIS) from 2016 to 2018 for model development. Separate cohorts were created based on the emergency medical center level in the NEDIS: regional emergency medical center (REMC), local emergency medical center (LEMC), and local emergency medical institution (LEMI). External and temporal validation used data from emergency department (ED) of the study site from 2019 to 2021. Patient features obtained during the triage process and the initial KTAS scores were used to develop the prediction model. Federated learning was used to rectify the disparity in data quality between EDs. The patient's demographic information, vital signs in triage, mental status, arrival information, and initial KTAS were included in the input feature., Results: 3,626,154 patients' visits were included in the regional emergency medical center cohort; 8,278,081 patients' visits were included in the local emergency medical center cohort; and 48,652 patients' visits were included in the local emergency medical institution cohort. The study site cohort, which is used for external and temporal validation, included 135,780 patients visits. Among the patients in the REMC and study site cohorts, KTAS level 3 patients accounted for the highest proportion at 42.4% and 45.1%, respectively, whereas in the LEMC and LEMI cohorts, KTAS level 4 patients accounted for the highest proportion. The area under the receiver operating characteristic curve for the prediction model was 0.786, 0.750, and 0.770 in the external and temporal validation. Patients with revised KTAS scores had a higher admission rate and ED mortality rate than those with unaltered KTAS scores., Conclusions: This novel system might accurately predict the likelihood of KTAS acuity revision and support clinician-based triage., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 The Authors. Published by Elsevier Ltd.)

This pilot study aimed to develop a new, reliable, and easy-to-use method for the evaluation of diastolic function through the M-mode measurement of mitral valve (MV) movement in the parasternal long axis (PSLA), similar to E-point septal separation (EPSS) used for systolic function estimation. Thirty healthy volunteers from a tertiary emergency department (ED) underwent M-mode measurements of the MV anterior leaflet in the PSLA view. EPSS, A-point septal separation (APSS), A-point opening length (APOL), and E-point opening length (EPOL) were measured in the PSLA view, along with the E and A velocities and e' velocity in the apical four-chamber view. Correlation analyses were performed to assess the relationship between M-mode and Doppler measurements, and the measurement time was evaluated. No significant correlations were found between M-mode and Doppler measurements in the study. However, M-mode measurements exhibited high reproducibility and faster acquisition, and the EPOL value consistently exceeded the APOL value, resembling the E and A pattern. These findings suggest that visually assessing the M-mode pattern on the MV anterior leaflet in the PSLA view may be a practical approach to estimating diastolic function in the ED. Further investigations with a larger and more diverse patient population are needed to validate these findings.

Introduction: Post-donation renal outcomes are a crucial issue for living kidney donors considering young donors' high life expectancy and elderly donors' comorbidities that affect kidney function. We developed a prediction model for renal adaptation after living kidney donation using interpretable machine learning., Methods: The study included 823 living kidney donors who underwent nephrectomy in 2009-2020. AutoScore, a machine learning-based score generator, was used to develop a prediction model. Fair and good renal adaptation were defined as post-donation estimated glomerular filtration rate (eGFR) of ≥ 60 mL/min/1.73 m 2 and ≥ 65% of the pre-donation values, respectively., Results: The mean age was 45.2 years; 51.6% were female. The model included pre-donation demographic and laboratory variables, GFR measured by diethylenetriamine pentaacetate scan, and computed tomography kidney volume/body weight of both kidneys and the remaining kidney. The areas under the receiver operating characteristic curve were 0.846 (95% confidence interval, 0.762-0.930) and 0.626 (0.541-0.712), while the areas under the precision-recall curve were 0.965 (0.944-0.978) and 0.709 (0.647-0.788) for fair and good renal adaptation, respectively. An interactive clinical decision support system was developed., Conclusion: The prediction tool for post-donation renal adaptation showed good predictive capability and may help clinical decisions through an easy-to-use web-based application., Competing Interests: The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The handling editor KH declared shared affiliation with author JY during the time of review., (Copyright © 2023 Jeon, Yu, Song, Jung, Lee, Lee, Huh, Cha and Jang.)

Despite the increasing presence of social robots (SRs) in Human-Robot Interaction, there are few studies that quantify these interactions and explore children's attitudes by analyzing real-time data as they communicate with SRs. Therefore, we attempted to explore the interaction between pediatric patients and SRs by analyzing the interaction log collected from real-time. This study is a retrospective analysis of data collected in a prospective study conducted on 10 pediatric cancer patients at tertiary hospitals in Korea. Using the Wizard of Oz method, we collected the interaction log during the interaction between pediatric cancer patients and the robot. Out of the collected data, 955 sentences from the robot and 332 sentences from the children were available for analysis, except for the logs that were missing due to environmental errors. we analyzed the delay time from saving the interaction log and the sentence similarity of the interaction log. The interaction log delay time between robot and child was 5.01 seconds. And the child's delay time averaged 7.2 seconds, which was longer than the robot's delay time of 4.29 seconds. Additionally, as a result of analyzing the sentence similarity of the interaction log, the robot (97.2%) was higher than the children (46.2%). The results of the sentiment analysis of the patient's attitude toward the robot were 73% neutral, 13.59% positive, and 12.42% negative. The observational evaluations of pediatric psychological experts identified curiosity (n=7, 70.0%), activity (n=5, 50.0%), passivity (n=5, 50.0%), sympathy (n=7, 70.0%), concentration (n=6, 60.0%), high interest (n=5, 50.0%), positive attitude (n=9, 90.0%), and low interaction initiative (n=6, 60.0%). This study made it possible to explore the feasibility of interaction with SRs and to confirm differences in attitudes toward robots according to child characteristics. To increase the feasibility of human-robot interaction, measures such as improving the completeness of log records by enhancing the network environment are required.

Background: Although chemotherapy-induced febrile neutropenia (FN) is the most common and life-threatening oncologic emergency, the characteristics and outcomes associated with return visits to the emergency department (ED) in these patients are uncertain. Hence, we aimed to investigate the predictive factors and clinical outcomes of chemotherapy-induced FN patients returning to the ED., Method: This single-center, retrospective observational study spanning 14 years included chemotherapy-induced FN patients who visited the ED and were discharged. The primary outcome was a return visit to the ED within five days. We conducted logistic regression analyses to evaluate the factors influencing ED return visit., Results: This study included 1318 FN patients, 154 (12.1%) of whom revisited the ED within five days. Patients (53.3%) revisited the ED owing to persistent fever (56.5%), with no intensive care unit admission and only one mortality case who was discharged hopelessly. Multivariable analysis revealed that shock index >0.9 (odds ratio [OR]: 1.45, 95% confidence interval [CI], 1.01-2.10), thrombocytopenia (<100 × 10 3 /uL) (OR: 1.64, 95% CI, 1.11-2.42), and lactic acid level > 2 mmol/L (OR: 1.51, 95% CI, 0.99-2.25) were associated with an increased risk of a return visit to the ED, whereas being transferred into the ED from other hospitals (OR: 0.08; 95% CI, 0.005-0.38) was associated with a decreased risk of a return visit to the ED., Conclusion: High shock index, lactic acid, thrombocytopenia, and ED arrival type can predict return visits to the ED in chemotherapy-induced FN patients., Competing Interests: Declaration of Competing Interest The authors have no potential conflicts of interest to disclose., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Various efforts have been made to diagnose acute cardiovascular diseases (CVDs) early in patients. However, the sole option currently is symptom education. It may be possible for the patient to obtain an early 12-lead electrocardiogram (ECG) before the first medical contact (FMC), which could decrease the physical contact between patients and medical staff. Thus, we aimed to verify whether laypersons can obtain a 12-lead ECG in an off-site setting for clinical treatment and diagnosis using a patch-type wireless 12-lead ECG (PWECG). Participants who were ≥ 19 years old and under outpatient cardiology treatment were enrolled in this simulation-based one-arm interventional study. We confirmed that participants, regardless of age and education level, can use the PWECG on their own. The median age of the participants was 59 years (interquartile range [IQR] = 56-62 years), and the median duration to obtain a 12-lead ECG result was 179 s (IQR = 148-221 s). With appropriate education and guidance, it is possible for a layperson to obtain a 12-lead ECG, minimizing the contact with a healthcare provider. These results can be used subsequently for treatment., (© 2023. The Author(s).)

Background: Field triage is critical in injury patients as the appropriate transport of patients to trauma centers is directly associated with clinical outcomes. Several prehospital triage scores have been developed in Western and European cohorts; however, their validity and applicability in Asia remains unclear. Therefore, we aimed to develop and validate an interpretable field triage scoring systems based on a multinational trauma registry in Asia., Methods: This retrospective and multinational cohort study included all adult transferred injury patients from Korea, Malaysia, Vietnam, and Taiwan between 2016 and 2018. The outcome of interest was a death in the emergency department (ED) after the patients' ED visit. Using these results, we developed the interpretable field triage score with the Korea registry using an interpretable machine learning framework and validated the score externally. The performance of each country's score was assessed using the area under the receiver operating characteristic curve (AUROC). Furthermore, a website for real-world application was developed using R Shiny., Findings: The study population included 26,294, 9404, 673 and 826 transferred injury patients between 2016 and 2018 from Korea, Malaysia, Vietnam, and Taiwan, respectively. The corresponding rates of a death in the ED were 0.30%, 0.60%, 4.0%, and 4.6% respectively. Age and vital sign were found to be the significant variables for predicting mortality. External validation showed the accuracy of the model with an AUROC of 0.756-0.850., Interpretation: The Grade for Interpretable Field Triage (GIFT) score is an interpretable and practical tool to predict mortality in field triage for trauma., Funding: This research was supported by a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (Grant Number: HI19C1328)., Competing Interests: This research was supported by a grant of the Korea Health Technology R&D Project through the 10.13039/501100003710Korea Health Industry Development Institute (KHIDI), funded by the 10.13039/501100003625Ministry of Health & Welfare, Republic of Korea (Grant Number: HI19C1328). This funding was granted to Jae Yong Yu., (© 2023 The Authors.)

Objectives: Although medical artificial intelligence (AI) systems that assist healthcare professionals in critical care settings are expected to improve healthcare, skepticism exists regarding whether their potential has been fully actualized. Therefore, we aimed to conduct a qualitative study with physicians and nurses to understand their needs, expectations, and concerns regarding medical AI; explore their expected responses to recommendations by medical AI that contradicted their judgments; and derive strategies to implement medical AI in practice successfully., Methods: Semi-structured interviews were conducted with 15 healthcare professionals working in the emergency room and intensive care unit in a tertiary teaching hospital in Seoul. The data were interpreted using summative content analysis. In total, 26 medical AI topics were extracted from the interviews. Eight were related to treatment recommendation, seven were related to diagnosis prediction, and seven were related to process improvement., Results: While the participants expressed expectations that medical AI could enhance their patients' outcomes, increase work efficiency, and reduce hospital operating costs, they also mentioned concerns regarding distortions in the workflow, deskilling, alert fatigue, and unsophisticated algorithms. If medical AI decisions contradicted their judgment, most participants would consult other medical staff and thereafter reconsider their initial judgment., Conclusions: Healthcare professionals wanted to use medical AI in practice and emphasized that artificial intelligence systems should be trustworthy from the standpoint of healthcare professionals. They also highlighted the importance of alert fatigue management and the integration of AI systems into the workflow.

Neutropenia and its complications are major adverse effects of cytotoxic chemotherapy. The time to recovery from neutropenia varies from patient to patient, and cannot be easily predicted even by experts. Therefore, we trained a deep learning model using data from 525 pediatric patients with solid tumors to predict the day when patients recover from severe neutropenia after high-dose chemotherapy. We validated the model with data from 99 patients and compared its performance to those of clinicians. The accuracy of the model at predicting the recovery day, with a 1-day error, was 76%; its performance was better than those of the specialist group (58.59%) and the resident group (32.33%). In addition, 80% of clinicians changed their initial predictions at least once after the model's prediction was conveyed to them. In total, 86 prediction changes (90.53%) improved the recovery day estimate., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: MHS, SYS, and HC have a pending patent application on some of the material reported in this manuscript. The remaining authors declare no competing interests., (Copyright © 2022 Elsevier Inc. All rights reserved.)

Background: In South Korea, after the spread of the Middle East Respiratory Syndrome epidemic was aggravated by long stays in crowded emergency departments (EDs), a 24-hour target policy for EDs was introduced to prevent crowding and reduce patients' length of stay (LOS). The policy requires at least 95% of all patients to be admitted, discharged or transferred from an ED within 24 hours of arrival. This study analyzes the effects of the 24-hour target policy on ED LOS and compliance rates and describes the consequences of the policy., Methods: A mixed-methods approach was applied to a retrospective observational study of ED visits combined with a survey of medical professionals. The primary measure was ED LOS, and the secondary measure was policy compliance rate which refers to the proportion of patient visits with a LOS shorter than 24 hours. Patient flow, quality of care, patient safety, staff workload, and staff satisfaction were also investigated through surveys. Mann-Whitney U and χ2 tests were used to compare variables before and after the introduction of the policy., Results: The median ED LOS increased from 3.9 hours (interquartile range [IQR] = 2.1-7.6) to 4.5 hours (IQR = 2.5-8.5) after the policy was introduced. This was likely influenced by the average monthly number of patients, which greatly increased from 4819 (SD = 340) to 5870 (SD = 462) during the same period. The proportion of patients with ED LOS greater than 24 hours remained below5% only after 6 months of policy implementation, but the number of patients whose disposition was decided at 23 hours increased by 4.84 times. Survey results suggested that patient flow and quality of care improved slightly, while the workload of medical staff worsened., Conclusions: After implementing the 24-hour target policy, the proportion of patients whose ED LOS exceeded 24 hours decreased, even though the median ED LOS increased. However, the unintended consequences of the policy were observed such as increased medical professional workload and abrupt expulsion of patients before 24 hours., (© 2022. The Author(s).)

Background: This study reports trends in pediatric out-of-hospital cardiac arrest (OHCA) and factors affecting clinical outcomes by age group., Methods: We identified 4,561 OHCA patients younger than 18 years between January 2009 and December 2018 in the Korean OHCA Registry. The patients were divided into four groups: group 1 (1 year or younger), group 2 (1 to 5 years), group 3 (6 to 12 years), and group 4 (13 to 17 years). The primary outcome was survival to hospital discharge, and the secondary outcomes were return of spontaneous circulation (ROSC) at the emergency department (ED) and good neurological status at discharge. Multivariate logistic analyses were performed., Results: The incidence rate of pediatric OHCA in group 1 increased from 45.57 to 60.89 per 100,000 person-years, while that of the overall population decreased over the 10 years. The rates of ROSC at the ED, survival to hospital discharge, and good neurologic outcome were highest in group 4 (37.9%, 9.7%, 4.9%, respectively) and lowest in group 1 (28.3%, 7.1%, 3.2%). The positive factors for survival to discharge were event location of a public/commercial building or place of recreation, type of first responder, prehospital delivery of automated external defibrillator shock, initial shockable rhythm at the ED. The factors affecting survival outcomes differed by age group., Conclusion: This study reports comprehensive trends in pediatric OHCA in the Republic of Korea. Our findings imply that preventive methods for the targeted population should be customized by age group., Competing Interests: The authors have no potential conflicts of interest to disclose., (© 2022 The Korean Academy of Medical Sciences.)

Emergency departments (EDs) are experiencing complex demands. An ED triage tool, the Score for Emergency Risk Prediction (SERP), was previously developed using an interpretable machine learning framework. It achieved a good performance in the Singapore population. We aimed to externally validate the SERP in a Korean cohort for all ED patients and compare its performance with Korean triage acuity scale (KTAS). This retrospective cohort study included all adult ED patients of Samsung Medical Center from 2016 to 2020. The outcomes were 30-day and in-hospital mortality after the patients' ED visit. We used the area under the receiver operating characteristic curve (AUROC) to assess the performance of the SERP and other conventional scores, including KTAS. The study population included 285,523 ED visits, of which 53,541 were after the COVID-19 outbreak (2020). The whole cohort, in-hospital, and 30 days mortality rates were 1.60%, and 3.80%. The SERP achieved an AUROC of 0.821 and 0.803, outperforming KTAS of 0.679 and 0.729 for in-hospital and 30-day mortality, respectively. SERP was superior to other scores for in-hospital and 30-day mortality prediction in an external validation cohort. SERP is a generic, intuitive, and effective triage tool to stratify general patients who present to the emergency department., (© 2022. The Author(s).)

This study sought to determine whether the C-MAC video laryngoscope (VL) performed better than a direct laryngoscope (DL) when attempting endotracheal intubation (ETI) in the emergency department (ED) while wearing personal protective equipment (PPE). This was a retrospective single-center observational study conducted in an academic ED between February 2020 and March 2022. All emergency medical personnel who participated in any ETI procedure were required to wear PPE. The patients were divided into the C-MAC VL group and the DL group based on the device used during the first ETI attempt. The primary outcome measure was the first-pass success (FPS) rate. A multiple logistic regression was used to determine the factors associated with FPS. Of the 756 eligible patients, 650 were assigned to the C-MAC group and 106 to the DL group. The overall FPS rate was 83.5% (n = 631/756). The C-MAC group had a significantly higher FPS rate than the DL group (85.7% vs. 69.8%, p < 0.001). In the multivariable logistic regression analysis, C-MAC use was significantly associated with an increased FPS rate (adjusted odds ratio, 2.86; 95% confidence interval, 1.69−4.08; p < 0.001). In this study, we found that the FPS rate of ETI was significantly higher when the C-MAC VL was used than when a DL was used by emergency physicians constrained by cumbersome PPE.

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., (©Hyunjung Park, Minjung Kathy Chae, Woohyeon Jeong, Jaeyong Yu, Weon Jung, Hansol Chang, Won Chul Cha. Originally published in JMIR Medical Informatics (https://medinform.jmir.org), 04.10.2022.)

Background: In the emergency department (ED), the result obtained using the 12-lead electrocardiography (ECG) is the basis for diagnosing and treating patients with chest pain. It was found that performing ECG at the appropriate time could improve treatment outcomes. Hence, a wearable ECG device with a timer can ensure that the findings are continuously recorded., Objective: We aimed to compare the time accuracy of a single-patch 12-lead ECG (SP-ECG) with that of conventional ECG (C-ECG). We hypothesized that SP-ECG would result in better time accuracy., Methods: Adult patients who visited the emergency room with chest pain but were not in shock were randomly assigned to one of the following 2 groups: the SP-ECG group or the C-ECG group. The final analysis included 33 (92%) of the 36 patients recruited. The primary outcome was the comparison of the time taken by the 2 groups to record the ECG. The average ages of the participants in the SP-ECG and C-ECG groups were 63.7 (SD 18.4) and 58.1 (SD 12.4) years, respectively., Results: With a power of 0.95 and effect sizes of 0.05 and 1.36, the minimum number of samples was calculated. The minimum sample size for each SP-ECG and C-ECG group is 15.36 participants, assuming a 20% dropout rate. As a result, 36 patients with chest pain participated, and 33 of them were analyzed. The timeliness of SP-ECG and C-ECG for the first follow-up ECG was 87.5% and 47.0%, respectively (P=.74). It was 75.0% and 35.2% at the second follow-up, respectively (P=.71)., Conclusions: Continuous ECG monitoring with minimal interference from other examinations is feasible and essential in complex ED situations. However, the precision of SP-ECG has not yet been proved. Nevertheless, the application of SP-ECG is expected to improve overcrowding and human resource shortages in EDs, though more research is needed., Trial Registration: ClinicalTrials.gov NCT04114760; https://clinicaltrials.gov/ct2/show/NCT04114760., (©Suyoung Yoo, Hansol Chang, Taerim kim, Hee yoon, Sung Yeon Hwang, Tae Gun Shin, Min Seob Sim, Ik joon Jo, Jin-Ho Choi, Won Chul Cha. Originally published in the Interactive Journal of Medical Research (https://www.i-jmr.org/), 13.09.2022.)

Objective: We evaluated the performance of diastolic shock index (DSI) and lactate in predicting vasopressor requirement among hypotensive patients with suspected infection in an emergency department., Methods: This was a single-center, retrospective observational study for adult patients with suspected infection and hypotension in the emergency department from 2018 to 2019. The study population was split into derivation and validation cohorts (70/30). We derived a simple risk score to predict vasopressor requirement using DSI and lactate cutoff values determined by Youden index. We tested the score by the area under the receiver operating characteristic curve (AUC). We performed a multivariable regression analysis to evaluate the association between the timing of vasopressor treatment and 28-day mortality., Results: A total of 1,917 patients were included. We developed a score, assigning 1 point each for the high DSI (≥2.0) and high lactate (≥2.5 mmol/L) criteria. The AUCs of the score were 0.741 (95% confidence interval [CI], 0.715-0.768) at hypotension and 0.736 (95% CI, 0.708-0.763) after initial fluid challenge in the derivation cohort and 0.676 (95% CI, 0.631-0.719) at hypotension and 0.688 (95% CI, 0.642-0.733) after initial fluid challenge in the validation cohort, respectively. In patients with scores of 2 points, early vasopressor therapy initiation was significantly associated with decreased 28-day mortality (adjusted odds ratio, 0.37; 95% CI, 0.14-0.94)., Conclusion: A prediction model with DSI and lactate levels might be useful to identify patients who are more likely to need vasopressor administration among hypotensive patients with suspected infection.

Background: A clinical decision support system (CDSS) is recognized as a technology that enhances clinical efficacy and safety. However, its full potential has not been realized, mainly due to clinical data standards and noninteroperable platforms., Objective: In this paper, we introduce the common data model-based intelligent algorithm network environment (CANE) platform that supports the implementation and deployment of a CDSS., Methods: CDSS reasoning engines, usually represented as R or Python objects, are deployed into the CANE platform and converted into C# objects. When a clinician requests CANE-based decision support in the electronic health record (EHR) system, patients' information is transformed into Health Level 7 Fast Healthcare Interoperability Resources (FHIR) format and transmitted to the CANE server inside the hospital firewall. Upon receiving the necessary data, the CANE system's modules perform the following tasks: (1) the preprocessing module converts the FHIRs into the input data required by the specific reasoning engine, (2) the reasoning engine module operates the target algorithms, (3) the integration module communicates with the other institutions' CANE systems to request and transmit a summary report to aid in decision support, and (4) creates a user interface by integrating the summary report and the results calculated by the reasoning engine., Results: We developed a CANE system such that any algorithm implemented in the system can be directly called through the RESTful application programming interface when it is integrated with an EHR system. Eight algorithms were developed and deployed in the CANE system. Using a knowledge-based algorithm, physicians can screen patients who are prone to sepsis and obtain treatment guides for patients with sepsis with the CANE system. Further, using a nonknowledge-based algorithm, the CANE system supports emergency physicians' clinical decisions about optimum resource allocation by predicting a patient's acuity and prognosis during triage., Conclusions: We successfully developed a common data model-based platform that adheres to medical informatics standards and could aid artificial intelligence model deployment using R or Python., (©Junsang Yoo, Jeonghoon Lee, Ji Young Min, Sae Won Choi, Joon-myoung Kwon, Insook Cho, Chiyeon Lim, Mi Young Choi, Won Chul Cha. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 27.07.2022.)

Background: Recently, the demand for mechanical ventilation (MV) has increased with the COVID-19 pandemic; however, the conventional approaches to MV training are resource intensive and require on-site training. Consequently, the need for independent learning platforms with remote assistance in institutions without resources has surged., Objective: This study aimed to determine the feasibility and effectiveness of an augmented reality (AR)-based self-learning platform for novices to set up a ventilator without on-site assistance., Methods: This prospective randomized controlled pilot study was conducted at Samsung Medical Center, Korea, from January to February 2022. Nurses with no prior experience of MV or AR were enrolled. We randomized the participants into 2 groups: manual and AR groups. Participants in the manual group used a printed manual and made a phone call for assistance, whereas participants in the AR group were guided by AR-based instructions and requested assistance with the head-mounted display. We compared the overall score of the procedure, required level of assistance, and user experience between the groups., Results: In total, 30 participants completed the entire procedure with or without remote assistance. Fewer participants requested assistance in the AR group compared to the manual group (7/15, 47.7% vs 14/15, 93.3%; P=.02). The number of steps that required assistance was also lower in the AR group compared to the manual group (n=13 vs n=33; P=.004). The AR group had a higher rating in predeveloped questions for confidence (median 3, IQR 2.50-4.00 vs median 2, IQR 2.00-3.00; P=.01), suitability of method (median 4, IQR 4.00-5.00 vs median 3, IQR 3.00-3.50; P=.01), and whether they intended to recommend AR systems to others (median 4, IQR 3.00-5.00 vs median 3, IQR 2.00-3.00; P=.002)., Conclusions: AR-based instructions to set up a mechanical ventilator were feasible for novices who had no prior experience with MV or AR. Additionally, participants in the AR group required less assistance compared with those in the manual group, resulting in higher confidence after training., Trial Registration: ClinicalTrials.gov NCT05446896; https://beta.clinicaltrials.gov/study/NCT05446896., (©Sejin Heo, Suhyeon Moon, Minha Kim, Minsu Park, Won Chul Cha, Meong Hi Son. Originally published in JMIR Serious Games (https://games.jmir.org), 22.07.2022.)

The study aims to measure the effectiveness of an AI-based traumatic intracranial hemorrhage prediction model in the decisions of emergency physicians regarding ordering head computed tomography (CT) scans. We developed a deep-learning model for predicting traumatic intracranial hemorrhages (DEEPTICH) using a national trauma registry with 1.8 million cases. For simulation, 24 cases were selected from previous emergency department cases. For each case, physicians made decisions on ordering a head CT twice: initially without the DEEPTICH assistance, and subsequently with the DEEPTICH assistance. Of the 528 responses from 22 participants, 201 initial decisions were different from the DEEPTICH recommendations. Of these 201 initial decisions, 94 were changed after DEEPTICH assistance (46.8%). For the cases in which CT was initially not ordered, 71.4% of the decisions were changed (p < 0.001), and for the cases in which CT was initially ordered, 37.2% (p < 0.001) of the decisions were changed after DEEPTICH assistance. When using DEEPTICH, 46 (11.6%) unnecessary CTs were avoided (p < 0.001) and 10 (11.4%) traumatic intracranial hemorrhages (ICHs) that would have been otherwise missed were found (p = 0.039). We found that emergency physicians were likely to accept AI based on how they perceived its safety., (© 2022. The Author(s).)

Background and objectives: Ocular ultrasound is a core application of point-of-care ultrasound (POCUS) to assist physicians in promptly identifying various ocular diseases at the bedside; however, hands-on POCUS training is challenging during a pandemic. Materials and Methods: A randomized controlled non-inferiority trial was conducted in an academic emergency department from October 2020 to April 2021. Thirty-two participants were randomly assigned to one of two groups. Group H (hands-on learning group) participated individually in a hands-on session with a standardized patient for 30 min, whereas Group O (online learning group) learned training materials and video clips for 20 min. They scanned four eyeballs of two standardized patients sequentially following the ocular POCUS scan protocol. Repeated POCUS scans were performed 2 weeks later to assess skill maintenance. Both groups completed the pre- and post-surveys and knowledge tests. Two emergency medicine faculty members blindly evaluated the data and assigned a score of 0−25. The primary endpoint was the initial total score of scan quality evaluated using non-inferiority analysis (generalized estimating equation). The secondary endpoints were total scores for scan quality after 2 weeks, scan time, and knowledge test scores. Results: The least squares means of the total scores were 21.7 (0.35) for Group O and 21.3 (0.25) for Group H, and the lower bound of the 95% confidence interval (CI) was greater than the non-inferiority margin of minus 2 (95% CI: −0.48−1.17). The second scan scores were not significantly different from those of the first scan. The groups did not differ in scanning time or knowledge test results; however, Group H showed higher subjective satisfaction with the training method (p < 0.001). Conclusion: This study showed that basic online ocular ultrasound education was not inferior to hands-on education, suggesting that it could be a useful educational approach in the pandemic era.

Providing timely intervention to critically ill patients is a challenging task in emergency departments (ED). Our study aimed to predict early critical interventions (CrIs), which can be used as clinical recommendations. This retrospective observational study was conducted in the ED of a tertiary hospital located in a Korean metropolitan city. Patient who visited ED from January 1, 2016, to December 31, 2018, were included. Need of six CrIs were selected as prediction outcomes, namely, arterial line (A-line) insertion, oxygen therapy, high-flow nasal cannula (HFNC), intubation, Massive Transfusion Protocol (MTP), and inotropes and vasopressor. Extreme gradient boosting (XGBoost) prediction model was built by using only data available at the initial stage of ED. Overall, 137,883 patients were included in the study. The areas under the receiver operating characteristic curve for the prediction of A-line insertion was 0·913, oxygen therapy was 0.909, HFNC was 0.962, intubation was 0.945, MTP was 0.920, and inotropes or vasopressor administration was 0.899 in the XGBoost method. In addition, an increase in the need for CrIs was associated with worse ED outcomes. The CrIs model was integrated into the study site's electronic medical record and could be used to suggest early interventions for emergency physicians., (© 2022. The Author(s).)

Background: Sepsis is diagnosed in millions of people every year, resulting in a high mortality rate. Although patients with sepsis present multimorbid conditions, including cancer, sepsis predictions have mainly focused on patients with severe injuries., Objective: In this paper, we present a machine learning-based approach to identify the risk of sepsis in patients with cancer using electronic health records (EHRs)., Methods: We utilized deidentified anonymized EHRs of 8580 patients with cancer from the Samsung Medical Center in Korea in a longitudinal manner between 2014 and 2019. To build a prediction model based on physical status that would differ between sepsis and nonsepsis patients, we analyzed 2462 laboratory test results and 2266 medication prescriptions using graph network and statistical analyses. The medication relationships and lab test results from each analysis were used as additional learning features to train our predictive model., Results: Patients with sepsis showed differential medication trajectories and physical status. For example, in the network-based analysis, narcotic analgesics were prescribed more often in the sepsis group, along with other drugs. Likewise, 35 types of lab tests, including albumin, globulin, and prothrombin time, showed significantly different distributions between sepsis and nonsepsis patients (P<.001). Our model outperformed the model trained using only common EHRs, showing an improved accuracy, area under the receiver operating characteristic (AUROC), and F1 score by 11.9%, 11.3%, and 13.6%, respectively. For the random forest-based model, the accuracy, AUROC, and F1 score were 0.692, 0.753, and 0.602, respectively., Conclusions: We showed that lab tests and medication relationships can be used as efficient features for predicting sepsis in patients with cancer. Consequently, identifying the risk of sepsis in patients with cancer using EHRs and machine learning is feasible., (©Donghun Yang, Jimin Kim, Junsang Yoo, Won Chul Cha, Hyojung Paik. Originally published in JMIR Medical Informatics (https://medinform.jmir.org), 15.06.2022.)