Background Interpretation of chest radiographs (CRs) by emergency department (ED) physicians is inferior to that by radiologists. Recent studies have investigated the effect of deep learning-based assistive technology on CR interpretation (DLCR), although its relevance to ED physicians remains unclear. This study aimed to investigate whether DLCR supports CR interpretation and the clinical decision-making of ED physicians. Methods We conducted a prospective interventional study using a web-based performance assessment system. Study participants were recruited through the official notice targeting board for certified emergency physicians and residents working at the present ED. Of the eight ED physicians who volunteered to participate in the study, seven ED physicians were included, while one participant declared withdrawal during performance assessment. Seven physicians’ CR interpretations and clinical decision-making were assessed based on the clinical data from 388 patients, including detecting the target lesion with DLCR. Participant performance was evaluated by area under the receiver operating characteristic curve (AUROC), sensitivity, specificity, and accuracy analyses; decision-making consistency was measured by kappa statistics. ED physicians with < 24 months of experience were defined as ‘inexperienced’. Results Among the 388 simulated cases, 259 (66.8%) had CR abnormality. Their median value of abnormality score measured by DLCR was 59.3 (31.77, 76.25) compared to a score of 3.35 (1.57, 8.89) for cases of normal CR. There was a difference in performance between ED physicians working with and without DLCR (AUROC: 0.801, P < 0.001). The diagnostic sensitivity and accuracy of CR were higher for all ED physicians working with DLCR than for those working without it. The overall kappa value for decision-making consistency was 0.902 (95% confidence interval [CI] 0.884–0.920); concurrently, the kappa value for the experienced group was 0.956 (95% CI 0.934–0.979), and that for the inexperienced group was 0.862 (95% CI 0.835–0.889). Conclusions This study presents preliminary evidence that ED physicians using DLCR in a clinical setting perform better at CR interpretation than their counterparts who do not use this technology. DLCR use influenced the clinical decision-making of inexperienced physicians more strongly than that of experienced physicians. These findings require prospective validation before DLCR can be recommended for use in routine clinical practice.
Intermittent manual measurement of vital signs may not rapidly predict sepsis development in febrile patients admitted to the emergency department (ED). We aimed to evaluate the predictive performance of a wireless monitoring device that continuously measures heart rate (HR) and respiratory rate (RR) and a machine learning analysis in febrile but stable patients in the ED. We analysed 468 patients (age, ≥18 years; training set, n = 277; validation set, n = 93; test set, n = 98) having fever (temperature >38 °C) and admitted to the isolation care unit of the ED. The AUROC of the fragmented model with device data was 0.858 (95% confidence interval [CI], 0.809–0.908), and that with manual data was 0.841 (95% CI, 0.789–0.893). The AUROC of the accumulated model with device data was 0.861 (95% CI, 0.811–0.910), and that with manual data was 0.853 (95% CI, 0.803–0.903). Fragmented and accumulated models with device data detected clinical deterioration in febrile patients at risk of septic shock 9 h and 5 h 30 min earlier, respectively, than those with manual data. Continuous vital sign monitoring using a wearable device could accurately predict clinical deterioration and reduce the time to recognise potential clinical deterioration in stable ED patients with fever.
Background Natural language processing has been established as an important tool when using unstructured text data; however, most studies in the medical field have been limited to a retrospective analysis of text entered manually by humans. Little research has focused on applying natural language processing to the conversion of raw voice data generated in the clinical field into text using speech-to-text algorithms. Objective In this study, we investigated the promptness and reliability of a real-time medical record input assistance system with voice artificial intelligence (RMIS-AI) and compared it to the manual method for triage tasks in the emergency department. Methods From June 4, 2021, to September 12, 2021, RMIS-AI, using a machine learning engine trained with 1717 triage cases over 6 months, was prospectively applied in clinical practice in a triage unit. We analyzed a total of 1063 triage tasks performed by 19 triage nurses who agreed to participate. The primary outcome was the time for participants to perform the triage task. Results The median time for participants to perform the triage task was 204 (IQR 155, 277) seconds by RMIS-AI and 231 (IQR 180, 313) seconds using manual method; this difference was statistically significant (P<.001). Most variables required for entry in the triage note showed a higher record completion rate by the manual method, but in the recording of additional chief concerns and past medical history, RMIS-AI showed a higher record completion rate than the manual method. Categorical variables entered by RMIS-AI showed less accuracy compared with continuous variables, such as vital signs. Conclusions RMIS-AI improves the promptness in performing triage tasks as compared to using the manual input method. However, to make it a reliable alternative to the conventional method, technical supplementation and additional research should be pursued.
Objectives Only 5% to 10% of patients who visit the emergency department (ED) with isolated dizziness without neurologic abnormalities may have central lesions; however, it is important to distinguish central lesions through brain imaging. This study was conducted to create a nomogram to provide an objective medical basis for selectively performing magnetic resonance imaging (MRI) among patients with isolated dizziness. Methods This retrospective observational study enrolled patients who visited the ED of a tertiary hospital with isolated dizziness and underwent diffusion‐weighted MRI and subsequently consulted with the departments of neurology, neurosurgery, or otorhinolaryngology. Multivariable logistic regression analysis was performed to identify risk factors in patients diagnosed with central lesions to create a nomogram with the significant variables. Results Of the 1,078 patients who were screened, 119 were diagnosed with central lesions. Significant variables in the multivariable logistic regression analysis were albumin levels (odds ratio [OR] = 0.339, 95% confidence interval [CI] = 0.188 to 0.610, p = 0.0003), inorganic phosphate levels (OR = 0.891, 95% CI = 0.832 to 0.954, p = 0.0010), history of ischemic stroke (OR = 3.170, 95% CI = 1.807 to 5.560, p < 0.0001), presyncope (OR = 3.152, 95% CI = 1.184 to 8.389, p = 0.0216), and nystagmus (OR = 0.365, 95% CI = 0.237 to 0.561, p < 0.0001). The area under the receiver operating characteristic curve of the nomogram created with these variables was 0.7315 (95% CI = 0.6842 to 0.7788, p < 0.0001). Conclusions Albumin, inorganic phosphate, previous stroke, presyncope, and nystagmus were associated with the predictive diagnosis of central lesions among patients admitted to the ED with isolated dizziness. The novel nomogram created using these variables can help in objectively determining the need for MRI in patients presenting with isolated dizziness to the ED.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.