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

An emergency department (ED) is a complex scene where various diseases and processes are intertwined. Annually, over 4.8 million patients visit EDs in Korea, and 137.8 million visit EDs in the United States [1,2]. Moreover, the number of patients and the severity of their complaints are increasing due to aging of the population and advances in emergency medicine [3]. When resources are not sufficient, the increased load on EDs results in a poor quality of care, which leads to a suboptimal outcome [4]. Triage systems have been developed where demand is greater than supply [5]. The purpose of triage in an ED is to prioritize patients to allocate clinical resources as beds

Objective Coronavirus disease 2019 (COVID-19) has notably altered the emergency department isolation protocol, imposing stricter requirements on probable infectious disease patients that enter the department. This has caused adverse effects, such as an increased rate of leave without being seen (LWBS). This study describes the effect of fever/respiratory symptoms as the main cause of isolation regarding LWBS after the COVID-19 pandemic.Methods We retrospectively analyzed emergency department visits before (March to July 2019) and after (March to July 2020) the COVID-19 pandemic. Patients were grouped based on existing fever or respiratory symptoms, with the LWBS rate as the primary outcome. Logistic regression analysis was used to identify the risk factors of LWBS. Logistic regression was performed using interaction terminology (fever/respiratory symptom patient [FRP] × post–COVID-19) to determine the interaction between patients with FRPs and the COVID-19 pandemic period.Results A total of 60,290 patients were included (34,492 in the pre–COVID-19, and 25,298 in the post–COVID-19 group). The proportion of FRPs decreased significantly after the pandemic (P < 0.001), while the LWBS rate in FRPs significantly increased from 2.8% to 19.2% (P < 0.001). Both FRPs (odds ratio, 1.76; 95% confidence interval, 1.59–1.84 (P < 0.001) and the COVID-19 period (odds ratio, 2.29; 95% confidence interval, 2.15–2.44; P < 0.001) were significantly associated with increased LWBS. Additionally, there was a significant interaction between the incidence of LWBS in FRPs and the COVID-19 pandemic period (P < 0.001).Conclusion The LWBS rate has increased in FRPs after the COVID-19 pandemic; additionally, the effect observed was disproportionate compared with that of nonfever/respiratory symptom patients.

Objectives: The outlook of artificial intelligence for healthcare (AI4H) is promising. However, no studies have yet discussed the issues from the perspective of stakeholders in Korea. This research aimed to identify stakeholders’ requirements for AI4H to accelerate the business and research of AI4H.Methods: We identified research funding trends from the Korean National Science and Technology Knowledge Information Service (NTIS) from 2015 and 2019 using “healthcare AI” and related keywords. Furthermore, we conducted an online survey with members of the Korean Society of Artificial Intelligence in Medicine to identify experts’ opinions regarding the development of AI4H. Finally, expert interviews were conducted with 13 experts in three areas (hospitals, industry, and academia).Results: We found 160 related projects from the NTIS. The major data type was radiology images (59.4%). Dermatology-related diseases received the most funding, followed by pulmonary diseases. Based on the survey responses, radiology images (23.9%) were the most demanding data type. Over half of the solutions were related to diagnosis (33.3%) or prognosis prediction (31%). In the expert interviews, all experts mentioned healthcare data for AI solutions as a major issue. Experts in the industrial field mainly mentioned regulations, practical efficacy evaluation, and data accessibility.Conclusions: We identified technology, regulatory, and data issues for practical AI4H applications from the perspectives of stakeholders in hospitals, industry, and academia in Korea. We found issues and requirements, including regulations, data utilization, reimbursement, and human resource development, that should be addressed to promote further research in AI4H.

To develop an ultrafast 3D gradient echo-based MRI method with constant TE and high tolerance to B 0 inhomogeneity, dubbed ERASE (equal-TE rapid acquisition with sequential excitation), and to introduce its use in BOLD functional MRI (fMRI). Theory and Methods: Essential features of ERASE, including spin behavior, were characterized, and a comparison study was conducted with conventional EPI. To demonstrate high tolerance to B 0 inhomogeneity, in vivo imaging of the mouse brain with a fiber-optic implant was performed at 9.4 T, and human brain imaging (including the orbitofrontal cortex) was performed at 3 T and 7 T. To evaluate the performance of ERASE in BOLD-fMRI, the characteristics of SNR and temporal SNR were analyzed for in vivo rat brains at 9.4 T in comparison with multislice gradientecho EPI. Percent signal changes and t-scores are also presented. Results: For both mouse brain and human brain imaging, ERASE exhibited a high tolerance to magnetic susceptibility artifacts, showing much lower distortion and signal dropout, especially in the regions involving large magnetic susceptibility effects. For BOLD-fMRI, ERASE provided higher temporal SNR and t-scores than EPI, but exhibited similar percent signal changes in in vivo rat brains at 9.4 T. Conclusion: When compared with conventional EPI, ERASE is much less sensitive, not only to EPI-related artifacts such as Nyquist ghosting, but also to B 0 inhomogeneity including magnetic susceptibility effects. It is promising for use in BOLD-fMRI, providing higher temporal SNR and t-scores with constant TE when compared with EPI, although further optimization is needed for human fMRI. K E Y W O R D S constant echo time, echo-planar imaging, magnetic susceptibility artifacts, quadratic-phase encoding, spatiotemporal encoding, ultrafast imaging How to cite this article: Ryu J-K, Jung WB, Yu J, et al. An equal-TE ultrafast 3D gradient-echo imaging method with high tolerance to magnetic susceptibility artifacts: Application to BOLD functional MRI.

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.

Background and objectives: The aim of this study is to describe the temporal change in alert override with a minimally interruptive clinical decision support (CDS) on a Next-Generation electronic medical record (EMR) and analyze factors associated with the change. Materials and Methods: The minimally interruptive CDS used in this study was implemented in the hospital in 2016, which was a part of the new next-generation EMR, Data Analytics and Research Window for Integrated kNowledge (DARWIN), which does not generate modals, ‘pop-ups’ but show messages as in-line information. The prescription (medication order) and alerts data from July 2016 to December 2017 were extracted. Piece-wise regression analysis and linear regression analysis was performed to determine the temporal change and factors associated with it. Results: Overall, 2,706,395 alerts and 993 doctors were included in the study. Among doctors, 37.2% were faculty (professors), 17.2% were fellows, and 45.6% trainees (interns and residents). The overall override rate was 61.9%. There was a significant change in an increasing trend at month 12 (p < 0.001). We found doctors’ positions and specialties, along with the number of alerts and medication variability, were significantly associated with the change. Conclusions: In this study, we found a significant temporal change of alert override. We also found factors associated with the change, which had statistical significance.

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.