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

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

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

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

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

Prevention of drug allergies is important for patient safety. The objective of this study was to evaluate the outcomes of antibiotic allergy-checking clinical decision support system (CDSS), K-CDSTM. 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.

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

BACKGROUND: Emergency departments (ED) nurses experience high mental workloads because of unpredictable work environments; however, research evaluating ED nursing workload using a tool incorporating nurses' perception is lacking. Quantify ED nursing subjective workload and explore the impact of work experience on perceived workload. METHODS: Thirty-two ED nurses at a tertiary academic hospital in the Republic of Korea were surveyed to assess their subjective workload for ED procedures using the National Aeronautics and Space Administration Task Load Index (NASA-TLX). 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.

Corrigendum to "Development and Asian-wide validation of the Grade for Interpretable Field Triage (GIFT) for predicting mortality in pre-hospital patients using the Pan-Asian Trauma Outcomes Study (PATOS)" [The Lancet Regional Health - Western Pacific 34 (2023) 100733].

[This corrects the article DOI: 10.1016/j.lanwpc.2023.100733.].

Inter hospital external validation of interpretable machine learning based triage score for the emergency department using common data model.

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.

Timely accessibility to healthcare resources and heatwave-related mortality in 7 major cities of South Korea: a two-stage approach with principal component analysis.

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.

Survey of Medical Applications of Federated Learning.

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.

Effect of knowledgebase transition of a clinical decision support system on medication order and alert patterns in an emergency department.

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.

Machine learning pre-hospital real-time cardiac arrest outcome prediction (PReCAP) using time-adaptive cohort model based on the Pan-Asian Resuscitation Outcome Study.

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.

Patient Anxiety and Communication Experience in the Emergency Department: A Mobile, Web-Based, Mixed-Methods Study on Patient Isolation During the COVID-19 Pandemic.

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.

Clinical support system for triage based on federated learning for the Korea triage and acuity scale.

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.

A Pilot Study Evaluating LV Diastolic Function with M-Mode Measurement of Mitral Valve Movement in the Parasternal Long Axis View.

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.

Prediction tool for renal adaptation after living kidney donation using interpretable machine learning.

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 m2 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.

EARLY PREDICTION OF UNEXPECTED LATENT SHOCK IN THE EMERGENCY DEPARTMENT USING VITAL SIGNS.

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.

Development and Asian-wide validation of the Grade for Interpretable Field Triage (GIFT) for predicting mortality in pre-hospital patients using the Pan-Asian Trauma Outcomes Study (PATOS).

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).

Using Real-Time Interaction Analysis to Explore Human-Robot Interaction.

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.

Feasibility of patch-type wireless 12-lead electrocardiogram in laypersons.

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.

Healthcare Professionals' Expectations of Medical Artificial Intelligence and Strategies for its Clinical Implementation: A Qualitative Study.

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.