Triage Category Research Articles

HUBUNGAN RESPONSE TIME PERAWAT DENGAN TINGKAT KECEMASAN KELUARGA PASIEN KATEGORI TRIAGE KUNING DI IGD RUMAH SAKIT DAERAH BALUNG

An emergency is a clinical situation that requires immediate medical intervention to save lives and prevent disability. There are many complaints filed by families of patients who feel they do not get service even though they come early, this is due to several factors including internal factors and external factors. The length of service in the emergency room causes the patient's family to be anxious because they do not immediately get treatment. The purpose of this study was to determine the relationship between the nurse's response time and the anxiety level of the family of patients in the yellow triage category in the emergency room of Balung Regional Hospital. This research design is a correlation with a crosssectional approach. The population of this study was 701 respondents with a sample of 70 respondents. The sampling technique used was non probability sampling with purposive sampling approach. The instruments in this study were questionnaires and observation sheets. The results of the Spearman's Rho statistical test with α = 0.05 obtained p value = 0.001 and r = 0.401 which means that response time has a moderate relationship with the patient's family anxiety level. The conclusion is that there is a relationship between response time and the anxiety level of the family of patients in the yellow triage category at the Emergency Room of Balung Regional Hospital.

The diagnostic and triage accuracy of the GPT-3 artificial intelligence model: an observational study

Artificial intelligence (AI) applications in health care have been effective in many areas of medicine, but they are often trained for a single task using labelled data, making deployment and generalisability challenging. How well a general-purpose AI language model performs diagnosis and triage relative to physicians and laypeople is not well understood. We compared the predictive accuracy of Generative Pre-trained Transformer 3 (GPT-3)'s diagnostic and triage ability for 48 validated synthetic case vignettes (<50 words; sixth-grade reading level or below) of both common (eg, viral illness) and severe (eg, heart attack) conditions to a nationally representative sample of 5000 lay people from the USA who could use the internet to find the correct options and 21 practising physicians at Harvard Medical School. There were 12 vignettes for each of four triage categories: emergent, within one day, within 1 week, and self-care. The correct diagnosis and triage category (ie, ground truth) for each vignette was determined by two general internists at Harvard Medical School. For each vignette, human respondents and GPT-3 were prompted to list diagnoses in order of likelihood, and the vignette was marked as correct if the ground-truth diagnosis was in the top three of the listed diagnoses. For triage accuracy, we examined whether the human respondents' and GPT-3's selected triage was exactly correct according to the four triage categories, or matched a dichotomised triage variable (emergent or within 1 day vs within 1 week or self-care). We estimated GPT-3's diagnostic and triage confidence on a given vignette using a modified bootstrap resampling procedure, and examined how well calibrated GPT-3's confidence was by computing calibration curves and Brier scores. We also performed subgroup analysis by case acuity, and an error analysis for triage advice to characterise how its advice might affect patients using this tool to decide if they should seek medical care immediately. Among all cases, GPT-3 replied with the correct diagnosis in its top three for 88% (42/48, 95% CI 75-94) of cases, compared with 54% (2700/5000, 53-55) for lay individuals (p<0.0001) and 96% (637/666, 94-97) for physicians (p=0·012). GPT-3 triaged 70% correct (34/48, 57-82) versus 74% (3706/5000, 73-75; p=0.60) for lay individuals and 91% (608/666, 89-93%; p<0.0001) for physicians. As measured by the Brier score, GPT-3 confidence in its top prediction was reasonably well calibrated for diagnosis (Brier score=0·18) and triage (Brier score=0·22). We observed an inverse relationship between case acuity and GPT-3 accuracy (p<0·0001) with a fitted trend line of -8·33% decrease in accuracy for every level of increase in case acuity. For triage error analysis, GPT-3 deprioritised truly emergent cases in seven instances. A general-purpose AI language model without any content-specific training could perform diagnosis at levels close to, but below, physicians and better than lay individuals. We found that GPT-3's performance was inferior to physicians for triage, sometimes by a large margin, and its performance was closer to that of lay individuals. Although the diagnostic performance of GPT-3 was comparable to physicians, it was significantly better than a typical person using a search engine. The National Heart, Lung, and Blood Institute.

Validation of the Emergency Department Work Index in a Pediatric Freestanding Community Emergency Department.

The Emergency Department Work Index (EDWIN) is a validated overcrowding score shown to correlate well with staff assessment of adult emergency department (ED) overcrowding and the potential need for diversion. It derives from the number of staffed ED beds, attending physicians on duty, patients within each triage category, and admitted patients. To date, no study has validated EDWIN in a pediatric community ED setting. We aim to determine if EDWIN correlates with established overcrowding measures and provider perception of overcrowding within a freestanding, community-based pediatric ED. In this prospective observational study at a freestanding, community-based pediatric ED, EDWIN was calculated hourly over 8 weeks throughout the year. EDWIN was compared with other objective and previously established ED metrics of overcrowding, including rates of patients who left without being seen (LWBS), average time from arrival to ED room, average length of stay (LOS), ED occupancy rates, and number of patients in the waiting room. Furthermore, EDWIN was compared with provider perception of overcrowding by surveying providers 6 times a day during the study period using novel, real-time, longitudinal, electronic health record-based survey distribution methodology. Spearman correlation coefficients were calculated to characterize the associations between EDWIN vs provider perception and EDWIN vs ED metrics. ANOVA and Tukey HSD were used to compare means of ED metrics of overcrowding across EDWIN severity categories. Five hundred eleven provider perception survey responses were collected from July 2022 through January 2023. EDWIN directly correlated with all measures of overcrowding, including provider perception of crowdedness (rho = 0.67), LWBS rates (rho = 0.44), average time from arrival to ED room (rho = 0.74), average LOS (rho = 0.70), ED occupancy rates (rho = 0.68), and number of patients in the waiting room (rho = 0.65). All findings were statistically significant (P < 0.05). Our findings suggest that EDWIN is an accurate tool to measure overcrowding in a freestanding, community-based pediatric ED.

Emergency Patient Triage Improvement through a Retrieval-Augmented Generation Enhanced Large-Scale Language Model

Objectives Emergency medical triage is crucial for prioritizing patient care in emergency situations, yet its effectiveness can vary significantly based on the experience and training of the personnel involved. This study aims to evaluate the efficacy of integrating Retrieval Augmented Generation (RAG) with Large Language Models (LLMs), specifically OpenAI's GPT models, to standardize triage procedures and reduce variability in emergency care. Methods We created 100 simulated triage scenarios based on modified cases from the Japanese National Examination for Emergency Medical Technicians. These scenarios were processed by the RAG-enhanced LLMs, and the models were given patient vital signs, symptoms, and observations from emergency medical services (EMS) teams as inputs. The primary outcome was the accuracy of triage classifications, which was used to compare the performance of the RAG-enhanced LLMs with that of emergency medical technicians and emergency physicians. Secondary outcomes included the rates of under-triage and over-triage. Results The Generative Pre-trained Transformer 3.5 (GPT-3.5) with RAG model achieved a correct triage rate of 70%, significantly outperforming Emergency Medical Technicians (EMTs) with 35% and 38% correct rates, and emergency physicians with 50% and 47% correct rates (p < 0.05). Additionally, this model demonstrated a substantial reduction in under-triage rates to 8%, compared with 33% for GPT-3.5 without RAG, and 39% for GPT-4 without RAG. Conclusions The integration of RAG with LLMs shows promise in improving the accuracy and consistency of medical assessments in emergency settings. Further validation in diverse medical settings with broader datasets is necessary to confirm the effectiveness and adaptability of these technologies in live environments.

Impact of spatial separation for respiratory patients on emergency department flow process intervals and length of stay

Spatial separation in emergency departments (EDs) is empirically practised as part of transmission-based precaution. Despite its potential benefits in segregating potentially infectious patients, the effects of spatial separation on patient flow remain uncertain. To explore the impact of spatial separation on ED patient flow and to identify specific clinical factors and flow process intervals (FPIs) influencing ED length of stay (EDLOS). This was a retrospective study of data extracted from patients' electronic medical records from January 1st to March 31st, 2022 conducted at the ED of a tertiary hospital in Kuala Lumpur, Malaysia. During this period, patients were separated into respiratory areas (RA) and non-respiratory areas (NRA) based on Centers for Disease Control and Prevention recommendations. The study obtained ethics approval from the institution's ethics board. A total of 1054 patients were included in the study, 275 allocated to RA and 779 to NRA. Patients in RA had a significantly longer median EDLOS compared with NRA (9h 29min vs 7h 6min, P < 0.001, d= 0.41). A lower proportion of patients in RA achieved an EDLOS ≤8h compared to NRA (41.8% vs 58.3%, P < 0.001). Independent factors affecting EDLOS were: triage category; re-triaging; hypertension; performing biomedical imaging; medical, surgical, and critical care consultations; and disposition plan. Bottlenecks significantly prolonging EDLOS were decision-to-departure, ultrasound interval, and referral-to-consultation. Spatial separation prolongs FPIs and EDLOS. Addressing inpatient access block and streamlining specialty review and biomedical imaging processes may reduce RA EDLOS.

External validation of a paediatric Smart triage model for use in resource limited facilities.

Models for digital triage of sick children at emergency departments of hospitals in resource poor settings have been developed. However, prior to their adoption, external validation should be performed to ensure their generalizability. We externally validated a previously published nine-predictor paediatric triage model (Smart Triage) developed in Uganda using data from two hospitals in Kenya. Both discrimination and calibration were assessed, and recalibration was performed by optimizing the intercept for classifying patients into emergency, priority, or non-urgent categories based on low-risk and high-risk thresholds. A total of 2539 patients were eligible at Hospital 1 and 2464 at Hospital 2, and 5003 for both hospitals combined; admission rates were 8.9%, 4.5%, and 6.8%, respectively. The model showed good discrimination, with area under the receiver-operator curve (AUC) of 0.826, 0.784 and 0.821, respectively. The pre-calibrated model at a low-risk threshold of 8% achieved a sensitivity of 93% (95% confidence interval, (CI):89%-96%), 81% (CI:74%-88%), and 89% (CI:85%-92%), respectively, and at a high-risk threshold of 40%, the model achieved a specificity of 86% (CI:84%-87%), 96% (CI:95%-97%), and 91% (CI:90%-92%), respectively. Recalibration improved the graphical fit, but new risk thresholds were required to optimize sensitivity and specificity.The Smart Triage model showed good discrimination on external validation but required recalibration to improve the graphical fit of the calibration plot. There was no change in the order of prioritization of patients following recalibration in the respective triage categories. Recalibration required new site-specific risk thresholds that may not be needed if prioritization based on rank is all that is required. The Smart Triage model shows promise for wider application for use in triage for sick children in different settings.

Patient stratification based on the risk of severe illness in emergency departments through collaborative machine learning models

ObjectivesEmergency department (ED) overcrowding presents a global challenge that inhibits prompt care for critically ill patients. Traditional 5-level triage system that heavily rely on the judgment of the triage staff could fail to detect subtle symptoms in critical patients, thus leading to delayed treatment. Unlike previous rivalry-focused approaches, our study aimed to establish a collaborative machine learning (ML) model that renders risk scores for severe illness, which may assist the triage staff to provide a better patient stratification for timely critical cares. MethodsThis retrospective study was conducted at a tertiary teaching hospital. Data were collected from January 2015 to October 2022. Demographic and clinical information were collected at triage. The study focused on severe illness as the outcome. We developed artificial neural network (ANN) models, with or without utilizing the Taiwan Triage and Acuity Scale (TTAS) score as one of the predictors. The model using the TTAS score is termed a machine-human collaborative model (ANN-MH), while the model without it is referred to as a machine-only model (ANN-MO). The predictive power of these models was assessed using the area under the receiver-operating-characteristic (AUROC) and the precision-recall curves (AUPRC); their sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and F1 score were compared. ResultsThe study analyzed 668,602 ED visits from 2015 to 2022. Among them, 278,724 visits from 2015 to 2018 were used for model training and validation, while 320,201 visits from 2019 to 2022 were for testing model performance. Approximately 2.6% of visits were by severely ill patients, whose TTAS scores ranged from 1 to 5. The ANN-MH model achieved a testing AUROC of 0.918 and AUPRC of 0.369, while for the ANN-MO model the AUROC and AUPRC were 0.909 and 0.339, respectively. Based on these metrics, the ANN-MH model outperformed the ANN-MO model, and both surpassed human triage classification. Subgroup analyses further highlighted the models' capability to identify higher-risk patients within the same triage level. ConclusionsThe traditional 5-level triage system often falls short, leading to under-triage of critical patients. Our models include a score-based differentiation within a triage level to offer advanced risk stratification, thereby promoting patient safety.

Validation of the Korean Version of the Clinical Frailty Scale-Adjusted Korean Triage and Acuity Scale for Older Patients in the Emergency Department.

Background and Objectives: The Clinical Frailty Scale (CFS), used to screen for prehospital frailty in patients aged >65 years, is simple, time-efficient, and has been validated in emergency departments (EDs). In this study, we analyzed whether the Korean Triage and Acuity Scale (KTAS) classification by level in older patients determined to have frailty based on the Korean version of the CFS increases the triage performance of the current KTAS. Materials and Methods: The primary outcome was 30-day in-hospital mortality, and secondary outcomes were hospital and intensive care unit (ICU) admissions. This study retrospectively analyzed prospectively collected data from three ED centers. Patients with a CFS score ranging from five (mildly frail) to nine (terminally ill) were categorized into the frailty group. We upgraded the KTAS classification of the frailty group by one level of urgency and defined this as the CFS-KTAS. Results: The cutoff values for predicting admission were three and two for the KTAS and CFS-KTAS, respectively. A significant difference was observed in the area under the receiver operating characteristic (AUROC) curve between the KTAS and CFS-KTAS. To predict ICU admission, the cutoff score was two for both scales. A significant difference was observed in the AUROC curve between the KTAS and CFS-KTAS. For predicting in-hospital mortality, the cutoff score was two for both scales. A significant difference was observed in the AUROC curve between the KTAS and CFS-KTAS. Conclusions: This study showed that the CFS-adjusted KTAS has a more useful prognostic value than the KTAS alone for predicting hospital outcomes in older patients.

Budućnost trijaže

Introduction. Triage is the assessment of the patient’s condition in order to determine the urgency of treatment. It is usually performed by a nurse, often using a five-level protocol.Aim. To conduct a comparative analysis of the accuracy of categorization and diagnosis between ChatGPT (a chatbot which uses machine learning algorithms) and traditional medical triage, as well as to provide recommendations on how artificial intelligence can improve the work of medical professionals in patient triage.Methods. The literature selected for comparison is “Emergency Nursing: 5-Tier Triage Protocols”. The most common diagnoses for which patients present to the emergency department were selected for research. Then, triage categories were selected and case presentations were created. These cases were presented to ChatGPT, and its responses were comparedwith the literature.Results. ChatGPT correctly categorizes triage cases in 43.33% of cases, with an average category difference of 0.7. Although it made mistakes in 1 or 2 categories in some cases, it assigned diagnoses to a higher category for patient safety.Discussion. Comparison with other studies shows that errors occur in up to 40% of nurse decisions due to various factors such as inexperience, speed of work, and a large number of patients, which could be reduced by additional artificial intelligence assistance. It is necessary to take into account factors that artificial intelligence cannot take over and that it can only be a help, not a substitute for medical personnel.Conclusion. ChatGPT has potential for usage in medical triage, but with improvements in specialized training of models on medical data and terminology to improve the accuracy and reliability of the model.

Leveraging graph neural networks for supporting automatic triage of patients

Patient triage is crucial in emergency departments, ensuring timely and appropriate care based on correctly evaluating the emergency grade of patient conditions. Triage methods are generally performed by human operator based on her own experience and information that are gathered from the patient management process. Thus, it is a process that can generate errors in emergency-level associations. Recently, Traditional triage methods heavily rely on human decisions, which can be subjective and prone to errors. A growing interest has recently been focused on leveraging artificial intelligence (AI) to develop algorithms to maximize information gathering and minimize errors in patient triage processing. We define and implement an AI-based module to manage patients’ emergency code assignments in emergency departments. It uses historical data from the emergency department to train the medical decision-making process. Data containing relevant patient information, such as vital signs, symptoms, and medical history, accurately classify patients into triage categories. Experimental results demonstrate that the proposed algorithm achieved high accuracy outperforming traditional triage methods. By using the proposed method, we claim that healthcare professionals can predict severity index to guide patient management processing and resource allocation.

#2768 Evaluation of vascular access via a TRIAGE system for hemodialysis: a tool to assess clinical risk in hemodialysis patients

Abstract Background and Aims The type and efficacy of vascular access (VA) in hemodialysis (HD) play pivotal roles in determining the quality of treatment and, eventually, the clinical outcome of patients. Low dialysis quality due to suboptimal vascular access functioning is a risk factor for increased cardiovascular mortality in HD. In a prior study involving 754 patients undergoing chronic HD, we demonstrated that a VA TRIAGE system effectively identified vascular accesses at a higher risk of complications both in patients with arteriovenous fistula (AVF) and in patients with central venous catheters (CVC). The TRIAGE categorization is generated monthly through a scoring system recording several parameters of each session, like blood flow, VA pressure, end HD circuit clots, external VA examination, and KT/V. Based on threshold values, VA performance is classified as Green (G), Yellow (Y), or Red). In this study, we aimed to assess whether the TRIAGE system, by identifying poorly functioning VA, could also identify patients at a higher risk of cardiovascular events. Method In this interventional prospective, blinded multicentre study, each centre utilized the VA TRIAGE electronic spreadsheet without knowledge of the generated triage. After six months of system implementation, a two-year follow-up (01/01/2020 - 01/12/2021) was conducted to record cardiovascular events (myocardial infarction, cerebrovascular accident, peripheral vasculopathy, unstable angina, atrial fibrillation, heart failure). External reviewers assessed the records, and a minimum of three months of VA follow-up was necessary for patient enrolment. Results From 18 HD centres, 757 patients were enrolled, with an average age of 64.5 ± 15.5 years; 27% were diabetics, undergoing HD for 24.4 ± 32.4 months; 369 (48.7%) had AVF, and 388 (51.3%) had a permanent CVC. Over 11.4±5.6 months of follow-up (range 3-23) and 108, 537 HD sessions recorded in the TRIAGE system, 100 cardiovascular-related events occurred during 16.3 ± 2.2 months of follow-up (range 3-23). The VA TRIAGE was: 60% Green, 35% Yellow, and 5% Red. The Green Triage group demonstrated a lower incidence of cardiovascular events independent of VA type (25% vs. 75%; P: 0.01) and a higher duration free from cardiovascular events (AVF Green vs. AVF Yellow-Red: log-rank test: 0.04). Conclusion Our VA TRIAGE system not only identifies poorly functioning vascular accesses but also effectively assesses the clinical risk in patients undergoing HD. This outcome, in our view, is linked to the diminished quality of dialysis resulting from lower VA function in the yellow and red Triage groups compared to the green Triage group. Integrating the VA TRIAGE system with additional clinical parameters enhances its capacity to assess the clinical risk of HD patients. This approach offers a more comprehensive evaluation, potentially improving the identification of patients at higher cardiovascular risk.

Association between stringency of lockdown measures and emergency department visits during the COVID-19 pandemic: A Dutch multicentre study.

The COVID-19 outbreak disrupted regular health care, including the Emergency Department (ED), and resulted in insufficient ICU capacity. Lockdown measures were taken to prevent disease spread and hospital overcrowding. Little is known about the relationship of stringency of lockdown measures on ED utilization. This study aimed to compare the frequency and characteristics of ED visits during the COVID-19 outbreak in 2020 to 2019, and their relation to stringency of lockdown measures. A retrospective multicentre study among five Dutch hospitals was performed. The primary outcome was the absolute number of ED visits (year 2018 and 2019 compared to 2020). Secondary outcomes were age, sex, triage category, way of transportation, referral, disposition, and treating medical specialty. The relation between stringency of lockdown measures, measured with the Oxford Stringency Index (OSI) and number and characteristics of ED visits was analysed. The total number of ED visits in the five hospitals in 2019 was 165,894, whereas the total number of visits in 2020 was 135,762, which was a decrease of 18.2% (range per hospital: 10.5%-30.7%). The reduction in ED visits was greater during periods of high stringency lockdown measures, as indicated by OSI. The number of ED visits in the Netherlands has significantly dropped during the first year of the COVID-19 pandemic, with a clear association between decreasing ED visits and increasing lockdown measures. The OSI could be used as an indicator in the management of ED visits during a future pandemic.

Review of a large trauma registry in Addis Ababa, Ethiopia: insights into prehospital care and provider training for trauma quality improvement

BackgroundInjury is a major cause of death and disability in Ethiopia. ALERT Hospital, one of only three designated trauma centers in the country, has employed a basic trauma registry since...

Applications and Performance of Machine Learning Algorithms in Emergency Medical Services: A Scoping Review.

The aim of this study was to summarize the literature on the applications of machine learning (ML) and their performance in Emergency Medical Services (EMS). Four relevant electronic databases were searched (from inception through January 2024) for all original studies that employed EMS-guided ML algorithms to enhance the clinical and operational performance of EMS. Two reviewers screened the retrieved studies and extracted relevant data from the included studies. The characteristics of included studies, employed ML algorithms, and their performance were quantitively described across primary domains and subdomains. This review included a total of 164 studies published from 2005 through 2024. Of those, 125 were clinical domain focused and 39 were operational. The characteristics of ML algorithms such as sample size, number and type of input features, and performance varied between and within domains and subdomains of applications. Clinical applications of ML algorithms involved triage or diagnosis classification (n = 62), treatment prediction (n = 12), or clinical outcome prediction (n = 50), mainly for out-of-hospital cardiac arrest/OHCA (n = 62), cardiovascular diseases/CVDs (n = 19), and trauma (n = 24). The performance of these ML algorithms varied, with a median area under the receiver operating characteristic curve (AUC) of 85.6%, accuracy of 88.1%, sensitivity of 86.05%, and specificity of 86.5%. Within the operational studies, the operational task of most ML algorithms was ambulance allocation (n = 21), followed by ambulance detection (n = 5), ambulance deployment (n = 5), route optimization (n = 5), and quality assurance (n = 3). The performance of all operational ML algorithms varied and had a median AUC of 96.1%, accuracy of 90.0%, sensitivity of 94.4%, and specificity of 87.7%. Generally, neural network and ensemble algorithms, to some degree, out-performed other ML algorithms. Triaging and managing different prehospital medical conditions and augmenting ambulance performance can be improved by ML algorithms. Future reports should focus on a specific clinical condition or operational task to improve the precision of the performance metrics of ML models.

The effect of the COVID-19 pandemic on pediatric emergency department utilization in three regions in Switzerland.

The COVID-19 pandemic was associated with a decrease in emergency department (ED) visits. However, contradictory, and sparse data regarding children could not yet answer the question, how pediatric ED utilization evolved throughout the pandemic. Our objectives were to investigate the impact of the pandemic in three language regions of Switzerland by analyzing trends over time, describe regional differences, and address implications for future healthcare. We conducted a retrospective, longitudinal cohort study at three Swiss tertiary pediatric EDs (March 1st, 2018-February 28th, 2022), analyzing the numbers of ED visits (including patients` age, triage categories, and urgent vs. non-urgent cases). The impact of COVID-19 related non-pharmaceutical interventions (NPIs) on pediatric ED utilization was assessed by interrupted time series (ITS) modelling. Based on 304'438 ED visits, we found a drop of nearly 50% at the onset of NPIs, followed by a gradual recovery. This primarily affected children 0-4years, and both non-urgent and urgent cases. However, the decline in urgent visits appeared to be more pronounced in two centers compared to a third, where also hospitalization rates did not decrease significantly during the pandemic. A subgroup analysis showed a significant decrease in respiratory and gastrointestinal diseases, and an increase in the proportion of trauma patients during the pandemic. The COVID-19 pandemic had substantial effects on number and reasons for pediatric ED visits, particularly among children 0-4years. Despite equal regulatory conditions, the utilization dynamics varied markedly between the three regions, highlighting the multifactorial modification of pediatric ED utilization during the pandemic. Furthermore, future policy decisions should take regional differences into account.

Otolaryngology outcomes of infants with conductive hearing loss identified through universal newborn hearing screening

ObjectiveInfants and children diagnosed with a conductive hearing loss (CHL) are often referred for otolaryngology assessment. Although this is also a regular occurrence for infants diagnosed with a CHL through Universal Newborn Hearing Screening (UNHS), less is known about these infants and their outcomes. Using a cohort of infants diagnosed with CHL through UNHS and referred to otolaryngology, this study aimed to investigate the relationship between specific demographic or clinical characteristics and 1) triage category 2) middle ear diagnosis and intervention and, 3) service-related factors at otolaryngology. MethodsRetrospective analysis through clinical chart review was performed on all infants born between January 2014 and December 2017 who referred on UNHS, diagnosed with a CHL and referred to the Queensland Children's Hospital. Descriptive analysis and Chi squared analysis was conducted on data from 95 records. ResultsAnalysis between all infants referred from UNHS and those who referred, diagnosed with CHL and then referred to otolaryngology suggest that bilateral referrals/medical exclusion, preterm and infants with ≥1 risk factors are more readily associated with referral to otolaryngology for CHL. Nearly all (92.86 %) infants who were referred to otolaryngology had a primary diagnosis of OM and most infants (89.66 %) received grommets as an intervention. The average age of first appointment at otolaryngology was 427 days, the average age of intervention was 579 days and the average occasions of service at otolaryngology was 6.72. ConclusionThis paper provides a snapshot into the journey and outcomes of infants referred from UNHS, diagnosed with CHL, and referred to otolaryngology. Further investigation in both general and UNHS populations is needed to better understand and apply these findings.

Assessment of Medical Test Overuse and Its Impact on Pediatric Emergency Department Outcomes in Upper Respiratory Tract Infections in a University Hospital in Lithuania.

Medical overuse poses potential risks to patients and contributes to increasing healthcare costs, pediatric emergency departments (PED) in particular. Often, upper respiratory tract infection (URTI) cases are viral-induced and self-limiting, and they do not require specific investigations or treatment. We conducted a retrospective study from 1 December 2021 to 31 January 2022, thereby aiming to identify the common tests and factors influencing specific diagnostic and treatment decisions for URTI in PED. In total, 307 (74.9%) URTI cases underwent complete blood count (CBC) tests, 312 (76.1%) were subjected to C-reactive protein (CRP) tests, and 110 (26.8%) received urinalysis tests. Patients with a longer duration of fever and a physician's suspicion of bacterial infection were more likely to receive CBC, CRP, and/or urinalysis tests (p < 0.05). Moreover, 75.1% of the cases were classified as viral URTIs, 9.8% were bacterial URTIs, and 15.1% were unspecified. Notably, 86 (20.1%) children received antibiotics and antibiotic prescription correlated with age, tonsillitis diagnosis, CRP values higher than 30 mg/L, and a CBC of p < 0.05. Patients triaged in the second or third categories were three times more likely to be observed for 24 h compared to patients with URTI and the fourth triage category (p < 0.05). This study highlights the need for interventions to improve the appropriateness of emergency service utilization, thereby emphasizing the importance of judicious decision making in managing pediatric URTIs.

Radiation Biological Toximetry Using Circulating Cell-Free DNA (cfDNA) for Rapid Radiation/Nuclear Triage.

Optimal triage biodosimetry would include risk stratification within minutes, and it would provide useful triage despite heterogeneous dosimetry, cytokine therapy, mixed radiation quality, race, and age. For regulatory approval, the U.S. Food and Drug Administration (FDA) Biodosimetry Guidance requires suitability for purpose and a validated species-independent mechanism. Circulating cell-free DNA (cfDNA) concentration assays may provide such triage information. To test this hypothesis, cfDNA concentrations were measured in unprocessed monkey plasma using a branched DNA (bDNA) technique with a laboratory developed test. The cfDNA levels, along with hematopoietic parameters, were measured over a 7-day period in Rhesus macaques receiving total body radiation doses ranging from 1 to 6.5 Gy. Low-dose irradiation (0-2 Gy) was easily distinguished from high-dose whole-body exposures (5.5 and 6.5 Gy). Fold changes in cfDNA in the monkey model were comparable to those measured in a bone marrow transplant patient receiving a supralethal radiation dose, suggesting that the lethal threshold of cfDNA concentrations may be similar across species. Average cfDNA levels were 50 ± 40 ng/mL [±1 standard deviation (SD)] pre-irradiation, 120 ± 13 ng/mL at 1 Gy; 242 ± 71 ng/mL at 2 Gy; 607 ± 54 at 5.5 Gy; and 1585 ± 351 at 6.5 Gy (±1 SD). There was an exponential increase in cfDNA concentration with radiation dose. Comparison of the monkey model with the mouse model and the Guskova model, developed using Chernobyl responder data, further demonstrated correlation across species, supporting a similar mechanism of action. The test is available commercially in a Clinical Laboratory Improvement Amendments (CLIA) ready form in the U.S. and the European Union. The remaining challenges include developing methods for further simplification of specimen processing and assay evaluation, as well as more accurate calibration of the triage category with cfDNA concentration cutoffs.

NURSING'S ROLE IN RISK SCREENING AND CLASSIFICATION IN EMERGENCY SERVICES IN BRAZIL: AN INTEGRATIVE REVIEW

Nurses are health professionals who work on the front line of triage and risk classification in urgency and emergency sectors. Unfortunately, in some cases, other specialties from other hospital sectors try to go against the nurses' choices. Furthermore, there are some reports of verbal and physical violence against these professionals. The nurse is essential for screening and risk classification in the hospital environment, he is the one who has the first contact with both the patient and the companion, the humanized vision of these professionals makes all the difference in the entire patient process, be it screening care and clinical internship.

Trends in reported GHB-related presentations to Sydney emergency departments between 2012 and 2021.

In overdose, gamma-hydroxybutyrate (GHB) and its precursors can cause decreased levels of consciousness, coma and death. Here, we aim to describe reported exposure to GHB at four EDs in Sydney, New South Wales (NSW), Australia. We searched the ED databases of four Sydney metropolitan hospitals for presentations relating to GHB exposure between 2012 and 2021. We calculated annual number of presentations stratified by hospital, age, sex, mode of arrival and triage category. A total of 3510 GHB-related presentations to ED were recorded across the four hospitals. Data for all hospitals were only available from 2015 onwards and between 2015 and 2021; there was a 114% increase in annual presentations (from 228 to 487). Males represented 68.7% of all presentations and the median age was 31 years (range 16-74 years). There was an increase in the proportion of female presentations between 2012 and 2021 (from 27.9% to 37.9%) along with the severity of presentation over the same period, with the proportion of presentations with a triage category 1 increasing from 19.7% to 34.5%. Increases in recorded absolute number and severity of GHB-related presentations to Sydney EDs are a major public health concern. There may also be shifts in the demographics of those with GHB-related presentations. Renewed efforts are required to understand the drivers of these increases to optimally target harm reduction approaches.