Triage Decisions Research Articles

Assessing AI chatbots efficacy in ophthalmic triage and referrals: A comparative study

To evaluate the efficacy of AI chatbots (OpenAI ChatGPT GPT-3.5, Google Bard, and WebMD) compared to human ophthalmology trainees in triage and referrals for common ophthalmic conditions. A single-center study was conducted at MKCG Medical College, Berhampur, Odisha, involving six ophthalmology trainees. The performance of AI chatbots was assessed based on diagnostic accuracy and triage categorization. Key performance indicators included the accuracy of the top three suggested diagnoses and concordance in recommendations for investigations and referrals. Physician respondents identified the correct diagnosis among the top three suggestions in 95% of cases. Google Bard achieved 90% accuracy, ChatGPT 85%, and WebMD 20%. High concordance was observed between physician and AI recommendations for investigations and referrals. AI chatbots demonstrate promising potential in supporting triage and referral decisions for ophthalmic conditions. While human expertise remains crucial, AI tools can augment diagnostic accuracy, improve efficiency, and enhance patient care. Future research should focus on refining AI algorithms, integrating clinical data, and exploring real-world implementation strategies.

Triage ethics in mass casualty incident simulation: A phenomenological exploration.

Disaster scenarios challenge both novice and experienced nurses to navigate complex ethical dilemmas in resource-limited environments. Traditional nursing education often leaves new nurses unprepared for the ethical demands of disaster nursing. Utilitarianism must often guide triage ethics and decision-making. There is a critical need to equip nursing students with these ethical competencies. This study explores nursing students' lived experiences using introductory triage ethics in mass casualty incident simulation (MCIS). A qualitative, interpretive phenomenological approach was employed, analyzing reflective journal responses from senior nursing students following MCIS participation. Participants were senior-year nursing students from a private university in the U.S., engaging in MCIS as part of their curriculum, which simulated resource-limited disaster scenarios requiring real-time ethical decisions. The study received Institutional Review Board (IRB) approval, and participants provided informed consent. Data were anonymized and did not affect academic evaluation. Two major themes emerged: 1. Living in Ethical Dilemmas (subthemes: We Could Have Saved Them If…, Looking Past Obvious Suffering, How Can We Be Prepared?, We Can't Abandon Them). 2. Development of Ethical Competency (subthemes: Knowing You've Done Enough, Finding New Perspectives, There Is No Right Answer). Students grew in moral reasoning and emotional resilience, transitioning from patient-centered care to utilitarian decision-making. Participation in the mass casualty incident simulation allowed nursing students to confront and navigate complex ethical challenges in real time, fostering significant growth in moral reasoning and emotional resilience. Through immersion in simulated ethical dilemmas, students grappled with the tension between patient-centered care and the realities of utilitarian decision-making. This experience catalyzed a deeper understanding of ethical ambiguity and the development of moral clarity, equipping them with foundational skills for ethically navigating crises. Nursing curricula should recognize and incorporate opportunities that facilitate this ethical growth through experiential learning.

Development and validation of prediction models for prehospital triage of military trauma patients.

The introduction of wireless sensors will enable military care providers to continuously and remotely assess/monitor vital signs. Prediction models are needed to use such data adequately and aid military care providers in their on-scene decision-making to optimise prehospital triage and improve patient outcomes. A prospective cohort comprising data from eight Emergency Medical Services and seven inclusive trauma regions was used to develop and validate prediction models that could aid military care providers in their prehospital triage decisions. Healthy (American Society of Anesthesiologists physical status classification 1 or 2) admitted adult trauma patients (aged ≥16 and ≤50 years), who suffered from a trauma mechanism that could occur to military personnel and were transported by ambulance from the scene of injury to a hospital, were included. A full model strategy was used, including prehospital predictors that are expected to be automaticly collectible by wireless sensors or to be incorporated in a personalised device that could run the models. Models were developed to predict early critical-resource use (ECRU), severe head injury (Abbreviated Injury Scale (AIS) ≥4), serious thoracic injury (AIS ≥3) and severe internal bleeding (>20% blood loss). Model performance was evaluated in terms of discrimination and calibration. Prediction models were developed with data from 4625 patients (80.0%) and validated with data from 1157 patients (20.0%). The models had good to excellent discriminative performance for the predicted outcomes in the validation cohort, with an area under the curve of 0.80 (95% CI 0.76 to 0.84) for ECRU, 0.83 (0.76 to 0.91) for severe head injury, 0.75 (0.70 to 0.80) for serious thoracic injury and 0.85 (0.78 to 0.93) for severe internal bleeding. All models showed satisfactory calibration in the validation cohort. The developed models could reliably predict outcomes in a simulated military trauma population and potentially support prehospital care providers in their triage decisions.

Comparative Analysis of Burn Injuries in Toddler and Preschool Children: Implications for Triage and Outcome Assessment.

This study aims to compare the demographic, clinical characteristics, and outcomes of burn injuries in toddler and preschool children, and to validate the American Burn Association (ABA) Burn Triage Decision Matrix in the Turkish pediatric population. A retrospective analysis was conducted on 684 pediatric burn patients (494 toddlers, 190 preschoolers) admitted to our burn center over a 5-year period. Variables including gender, burn etiology, burn area, depth, treatment modalities, complications, length of hospital stay, and mortality were analyzed. The performance of the ABA Burn Triage Decision Matrix was evaluated in both groups. Scalding was the predominant cause of burns in both groups, with a significant difference in the involvement of anterior trunk (p = 0.027). The mean Total Body Surface Area (TBSA) was comparable between the groups (p = 0.286). There was no significant difference in mortality rates (p = 0.385), treatment modalities, and complications. The ABA Burn Triage Decision Matrix demonstrated consistency in triaging the severity of burn injuries, with a notable discrepancy observed in the moderate risk category of toddler group. This study highlights the distinct characteristics and outcomes of burn injuries in different pediatric age groups. The ABA Burn Triage Decision Matrix's validation suggests its utility in enhancing triage accuracy and resource allocation in pediatric populations, especially in disaster-prone regions.

How large language model-powered conversational agents influence decision making in domestic medical triage contexts

IntroductionEffective delivery of healthcare depends on timely and accurate triage decisions, directing patients to appropriate care pathways and reducing unnecessary visits. Artificial Intelligence (AI) solutions, particularly those based on Large Language Models (LLMs), may enable non-experts to make better triage decisions at home, thus easing the healthcare system's load. We investigate how LLM-powered conversational agents influence non-experts in making triage decisions, further studying different persona profiles embedded via prompting.MethodsWe designed a randomized experiment where participants first assessed patient symptom vignettes independently, then consulted one of the two agent profiles—rational or empathic—for advice, and finally revised their triage ratings. We used linear models to quantify the effect of the agent profile and confidence on the weight of advice. We examined changes in confidence and accuracy of triage decisions, along with participants' perceptions of the agents.ResultsIn a study with 49 layperson participants, we found that persona profiles can be differentiated in LLM-powered conversational agents. However, these profiles did not significantly affect the weight of advice. Notably, less confident participants were more influenced by LLM advice, leading to larger adjustments to initial decisions. AI guidance improved alignment with correct triage levels and boosted confidence in participants' decisions.DiscussionWhile LLM advice improves triage recommendations accuracy, confidence plays an important role in its adoption. Our findings raise design considerations for human-AI interfaces, highlighting two key aspects: encouraging appropriate alignment with LLMs' advice and ensuring that people are not easily swayed in situations of uncertainty.

Outcome of patients with solid malignancies considered for intensive care unit admission: a single-center prospective cohort study.

To identify the predictors and outcomes of ICU triage decisions in patients with solid malignancies (SM) and to investigate the usefulness of the National Early Warning Score (NEWS) and quick Sequential Organ Failure Assessment (qSOFA) score at triage. All patients with SM for whom ICU admission was requested between July 2019 and December 2021 in a French university-affiliated hospital were included prospectively. Of the 6262 patients considered for ICU admission, 410 (6.5%) had SM (age, 66 [58-73] years; metastases, 60.1%; and performance status 0-2, 81%). Of these 410 patients, 176 (42.9%) were admitted to the ICU, including 141 (80.1%) subsequently discharged alive. Breast cancer, hemoptysis, and pneumothorax were associated with ICU admission; whereas older age, performance status 3-4, metastatic disease, and request at night were associated with denial of ICU admission. The NEWS, and the qSOFA score in patients with suspected infection, determined at triage performed poorly for predicting hospital mortality (area under the receiver operating characteristics curve, 0.52 and 0.62, respectively). Performance status 3-4 was independently associated with higher 6-month mortality and first-line anticancer treatment with lower 6-month mortality. Hospital mortality was 33.3% in patients admitted to the ICU after refusal of the first request. Patients with SM were frequently denied ICU admission despite excellent in-ICU survival. Poor performance status was associated with ICU admission denial and higher 6-month mortality, but none of the other reasons for denying ICU admission predicted 6-month mortality. Physiological scores had limited usefulness in this setting.

Prognostic Value of Admission Lactate Levels in Critically Ill Patients: A Comparative Study With Sequential Organ Failure Assessment and Acute Physiology and Chronic Health Evaluation II Scores.

Introduction Critical illness refers to life-threatening conditions requiring mechanical or pharmacological intervention to maintain organ function. Prognostic models, such as the Sequential Organ Failure Assessment (SOFA) and Acute Physiology and Chronic Health Evaluation (APACHE) II scores, have been widely used to predict mortality in intensive care unit (ICU) patients. Lactate levels are emerging as a valuable biomarker in this context. This study aims to determine the prognostic value of lactate levels upon admission in critically ill patients and to assess their correlation with SOFA and APACHE II scores. Methods This descriptive cross-sectional study included 200 critically ill patients admitted to the emergency department over one year. Data on patient demographics, clinical findings, and laboratory results were collected, and lactate levels and SOFA and APACHE II scores were measured at the time of admission. Patients were followed throughout their hospital stay, with outcomes classified as survival or mortality. Receiver operating characteristic (ROC) curve analysis was used to evaluate the predictive value of lactate, SOFA, and APACHE II scores. Statistical analysis was performed using IBM SPSS Statistics for Windows, Version 27.0 (Released 2020; IBM Corp., Armonk, New York, United States). Results The mean age of the patients was 56.8±16.9 years; 110 (55%) were men, and 90 (45%) were women. In total, 79 patients (39.5%) were non-survivors, and 121 (60.5%) were survivors. Lactate levels were significantly higher in non-survivors (3.56±1.90 mmol/L) compared to survivors (1.47±0.82 mmol/L) (p<0.001). The SOFA and APACHE II scores were also significantly higher in non-survivors (SOFA: 6.35±3.19; APACHE II: 19.91±8.21) than in survivors (SOFA: 3.14±2.02; APACHE II: 12.45±5.76) (p<0.001). The ROC curve analysis showed that lactate had an area under the curve (AUC) of 0.909, SOFA had an AUC of 0.809, and APACHE II had an AUC of 0.769 for predicting mortality. Conclusions Lactate levels are a highly sensitive predictor of mortality in critically ill patients, with significant correlations to SOFA and APACHE II scores. Lactate, as a single rapid test, provides substantial prognostic information and can aid in early triage and clinical decision-making, particularly in resource-limited settings. A single arterial lactate measurement at admission is an effective tool for predicting patient outcomes in the ICU.

The Impact of Healthcare Pressures on the COVID-19 Hospitalisation Fatality Risk in England.

As the impact of the SARS-CoV-2 pandemic extends into 2023 and beyond, the treatment and outcomes of infected patients continues to evolve. Unlike earlier in the pandemic there are now further infectious disease pressures placed on hospitals, which influence patient care and triage decisions. The manuscript uses individual patient records linked with associated hospital management information of system pressure characteristics to attribute COVID-19 hospitalisation fatality risks (HFR) to patients and hospitals, using generalised additive mixed effects models. Between 01 September 2022 and 09 October 2023, the COVID-19 hospitalisation fatality risk in England was estimated as 12.71% (95% confidence interval (CI) 12.53%, 12.88%). Staff absences had an adjusted odds ratio of 1.038 (95% CI 1.017, 1.060) associatedwith the HFR when accounting for patient and hospital characteristics. This observational research presents evidence that a range of local hospital effects can have a meaningful impact on the risk of death from COVID-19 once hospitalised and should be accounted for when reporting estimates. We show that both the patient case mix and hospital pressures impact estimates of patient outcomes.

A novel triage framework for emergency department based on machine learning paradigm

AbstractEmergency departments, crucial in managing patient emergencies, are often challenged by overcrowding and diagnostic errors during triage—the process that assesses the urgency of patients' conditions. Traditional triage systems, heavily dependent on human judgement, are prone to errors like under‐triage, where severe conditions are missed, delaying treatment, and over‐triage, where less severe conditions are overly prioritized, causing unnecessary resource use and morbidity. This thesis presents a novel multi‐model machine‐learning framework designed to enhance triage accuracy by evaluating multiple medical conditions concurrently, including chronic illnesses like heart disease. The framework employs various machine‐learning techniques—such as logistic regression, support vector machines, random forests, deep neural networks, and decision trees—to analyze different medical conditions in two comprehensive phases. In the first phase, patients' conditions are categorized, and a multi‐tiered analysis using multiple classifiers refines the assessment by considering probabilistic outcomes from each classifier. The second phase synthesizes these insights into a unified and precise triage decision, distinguishing between patients who require critical care and those needing less urgent hospitalization. This integration of diverse machine learning models allows for a fused and precise triage decision, overcoming traditional triage systems' limitations that usually focus on single conditions and rely on isolated model predictions. A hybrid feature selection method is also utilized to identify critical predictors, enhancing the decision‐making process. The framework is validated through a specially curated dataset that simulates multiple triage scenarios, evaluated by medical experts for efficacy. Comparative analysis with traditional triage methods demonstrates significant improvements in decision accuracy, as evidenced by higher area under curve (AUC) values—0.95 for critical care and 0.90 for hospitalization. The implementation of this data‐driven approach substantially reduces human error, boosts operational efficiency, and aids medical staff in making rapid, informed decisions. This research represents a significant advancement in emergency medical care, illustrating the benefits of integrating sophisticated machine learning techniques to improve patient outcomes and resource management.

Factors affecting the accuracy of prehospital triage application and prehospital scene time in simulated mass casualty incidents

BackgroundThe contemporary management of mass casualty incidents (MCIs) relies on the effective application of predetermined, dedicated response plans based on current best evidence. Currently, there is limited evidence regarding the factors influencing the accuracy of first responders (FRs) in applying the START protocol and the associated prehospital times during the response to MCIs. The objective of this study was to investigate factors affecting FRs’ accuracy in performing prehospital triage in a series of simulated mass casualty exercises. Secondly, we assessed factors affecting triage-to-scene exit time in the same series of exercises.MethodsThis retrospective study focused on simulated casualties in a series of simulated MCIs Full Scale Exercises. START triage was the triage method of choice. For each Full-Scale Exercise (FSEx), collected data included exercise and casualty-related information, simulated casualty vital parameters, simulated casualty anatomic lesions, scenario management times, and responder experience.ResultsAmong the 1090 casualties included in the primary analysis, 912 (83.6%) were correctly triaged, 137 (12.6%) were overtriaged, and 41 (3.7%) were undertriaged. The multinomial regression model indicated that increasing heart rate (RRR = 1.012, p = 0.008), H-AIS (RRR = 1.532, p < 0.001), and thorax AIS (T-AIS) (RRR = 1.344, p = 0.007), and lower ISS (RRR = 0.957, p = 0.042) were independently associated with overtriage. Undertriage was significantly associated with increasing systolic blood pressure (RRR = 1.013, p = 0.005), AVPU class (RRR = 3.104 per class increase), and A-AIS (RRR = 1.290, p = 0.035). The model investigating the factors associated with triage-to-scene departure time showed that the assigned prehospital triage code red (TR = 0.841, p = 0.002), expert providers (TR = 0.909, p = 0.015), and higher peripheral oxygen saturation (TR = 0.998, p < 0.001) were associated with a reduction in triage-to-scene departure time. Conversely, increasing ISS was associated with a longer triage-to-scene departure time (TR = 1.004, 0.017).ConclusionsUnderstanding the predictors influencing triage and scene management decision-making by healthcare professionals responding to a mass casualty may facilitate the development of tailored training pathways regarding mass casualty triage and scene management.

External validation of the preHEART score and comparison with current clinical risk scores for prehospital risk assessment in patients with suspected NSTE-ACS

BackgroundEmergency Medical Services (EMS) studies have shown that prehospital risk stratification and triage decisions in patients with suspected non-ST-elevation acute coronary syndrome (NSTE-ACS) can be improved using clinical risk scores...

Consensus and controversies on post-acute care decision making and referral to geriatric rehabilitation: A national survey

BackgroundTransitioning older hospital patients to the appropriate type of post-acute care has become an urgent clinical issue within the context of changing demographics and limited duration of hospital stay. ObjectiveConsensus on assessments that guide post-acute care decision making would benefit potential patients and support cooperation between settings. DesignA national web-based questionnaire focusing on professional contributions, patient involvement and the use of triage items and measures. ParticipantsHospital and geriatric rehabilitation professionals in the Netherlands participated as respondent groups, representing ‘sending’ and ‘receiving’ professionals. MethodsA comprehensive questionnaire was used with open, multiple choice and closed questions, exploring in detail how assessment of hospital patients in need of a post-acute care decision was performed. Descriptive statistics were applied together with deductive coding of qualitative data. ResultsA total of 104 hospital liaison nurses (66.7 %) and 52 GR professionals (33.3 %) participated. Respondents were reasonably satisfied with the current triage practice. Hospital liaison nurses valued their operational responsibility for triage. Geriatric rehabilitation professionals wanted active involvement in decision making and deemed hospital paramedic expertise sub-optimally applied. ‘Too little involvement’ of patients and families was felt by 50.0 % of the GR respondents versus 15.5 % of hospital respondents. The importance of half (47.8 %) of the triage items was rated differently between respondent groups. When discussing complex cases between sending and receiving professionals, views were felt as complementary. ConclusionsBoth sending and receiving professionals expressed moderate satisfaction with post-acute care decision making, whereas their views on triage assessments differed according to setting and role. The patients’ voice may be insufficiently heard in triage decisions. Shared expertise and a consensual approach can develop when triage consultation is facilitated by both hospitals and PAC facilities. This study offers ingredients to reach a multi-professional view on post-acute care decision making and referral to geriatric rehabilitation.

Professional competences of emergency department nurses working in the earthquake region on triage management in children and the difficulties experienced by nurses in the decision-making process: A qualitative study

Limited resources in disasters may negatively affect the care of paediatric patients and cause complications to be prevented. The aim of this study was to investigate the professional competence of nurses regarding triage management in children during the Kahramanmaraş earthquake and the difficulties they experienced in the decision-making process. This study used a qualitative exploratory research design with a phenomenological approach. In-depth interviews were conducted with the participants individually and through focus group discussions using a semi-structured interview form. A total of 23 participants working in the emergency department during and after the earthquake were included in this study, which was conducted using a purposive sampling technique. Data were analyzed using content analysis procedures. Max Qualitative Data Analysis (2020) programme was used to code the data. As a result of data analysis, 4 main themes and 15 sub-themes emerged. The four main themes were “Professional Competencies of Nurses During Disasters,” “Triage Decision in the Emergency Department,” “Expectations of Nurses Working in a Disaster Area” and “Participation in Disaster Education”. The results of this study showed that nurses are exposed to difficulties arising from many individuals, psychological, professional, environmental, and educational variables in the early period of disaster. Participants' views revealed a strong relationship between being professionally competent, providing appropriate care to patients and emotional exhaustion.

Evaluation of a new prehospital major incident triage tool: Informing current UK practice

During mass casualty incidents, the first Ambulance responders on scene face critical decisions about who receives medical treatment first. Effective clinical management of such incidents relies on triage, which prioritises patients based on injury severity, available resources, and the number of casualties. Various decision-making tools have been developed to assist emergency responders with triage decisions. However, there have been concerns raised about the efficacy of existing triage tools, which led to the development of a new pre-hospital triage tool for major incidents. The current study evaluated two versions of the new tool alongside the existing one used by UK emergency responders through a live exercise simulating two different major incidents. Participants offered valuable insights for the tools enhancement, notably in managing non-injured casualties and paediatric patients, and reducing cognitive load when making decisions. These findings shaped the final version of the new tool, deployed to UK Ambulance responders in Spring 2023. Findings demonstrate the importance of considering the visual presentation of triage tools as well as the inclusion of accurate physiological parameters to facilitate responder decision-making when categorising patients in mass casualty triage. Practical implications and suggestions for future research are discussed.

Evaluation of a Musculoskeletal Digital Assessment Routing Tool (DART): Crossover Noninferiority Randomized Pilot Trial.

Musculoskeletal conditions account for 16% of global disability, resulting in a negative effect on patients and increasing demand for health care use. Triage directing patients to appropriate level intervention improving health outcomes and efficiency has been prioritized. We developed a musculoskeletal digital assessment routing tool (DART) mobile health (mHealth) system, which requires evaluation prior to implementation. Such innovations are rarely rigorously tested in clinical trials-considered the gold standard for evaluating safety and efficacy. This pilot study is a precursor to a trial assessing DART performance with a physiotherapist-led triage assessment. The study aims to evaluate trial design, assess procedures, and collect exploratory data to establish the feasibility of delivering an adequately powered, definitive randomized trial, assessing DART safety and efficacy in an NHS primary care setting. A crossover, noninferiority pilot trial using an integrated knowledge translation approach within a National Health Service England primary care setting. Participants were patients seeking assessment for a musculoskeletal condition, completing a DART assessment and the history-taking element of a face-to-face physiotherapist-led triage in a randomized order. The primary outcome was agreement between DART and physiotherapist triage recommendation. Data allowed analysis of participant recruitment and retention, randomization, blinding, study burden, and potential barriers to intervention delivery. Participant satisfaction was measured using the System Usability Scale. Over 8 weeks, 129 patients were invited to participate. Of these, 92% (119/129) proceeded to eligibility assessment, with 60% (78/129) meeting the inclusion criteria and being randomized into each intervention arm (39/39). There were no dropouts and data were analyzed for all 78 participants. Agreement between physiotherapist and DART across all participants and all primary triage outcomes was 41% (32/78; 95% CI 22-45), intraclass correlation coefficient 0.37 (95% CI 0.16-0.55), indicating that the reliability of DART was poor to moderate. Feedback from the clinical service team led to an adjusted analysis yielding of 78% (61/78; 95% CI 47-78) and an intraclass correlation coefficient of 0.57 (95% CI 0.40-0.70). Participant satisfaction was measured quantitively using amalgamated System Usability Scale scores (n=78; mean score 84.0; 90% CI +2.94 to -2.94), equating to an "excellent" system. There were no study incidents, and the trial burden was acceptable. Physiotherapist-DART agreement of 78%, with no adverse triage decisions and high patient satisfaction, was sufficient to conclude DART had the potential to improve the musculoskeletal pathway. Study validity was enhanced by the recruitment of real-world patients and using an integrated knowledge translation approach. Completion of a context-specific consensus process is recommended to provide definitive definitions of safety criteria, range of appropriateness, noninferiority margin, and sample size. This pilot demonstrated an adequately powered definitive trial is feasible, which would provide evidence of DART safety and efficacy, ultimately informing potential for DART implementation. ClinicalTrials.gov NCT04904029; http://clinicaltrials.gov/ct2/show/NCT04904029. RR2-10.2196/31541.

How organisational factors and clinical decision support system affect nurses’ knowledge for decisions in triage

ABSTRACT This paper delves into heuristic decision-making by nurses during the triage process, aiming to elucidate how organisational factors influence nurses’ decision-making regarding the assignment of priority codes to patients, and to assess the effectiveness of Clinical Decision Support Systems (CDSS) in this context. Drawing on an experimental dataset of 25 triage cases evaluated by 35 nurses via interviews, the study was conducted in two Spanish Emergency Departments using CDSS. Findings indicate that organisational factors predominantly influence decisions in cases with complete and coherent information. However, in cases where information is incoherent or missing, individual nurse characteristics guide decision-making. Furthermore, it suggests that CDSS should be tailored to nurses’ clinical reasoning to serve as effective support for individual decision-making processes.

Factors Associated with Patient Triage Accuracy in the Emergency Department

Triage in the Emergency Department (ED) aims to provide timely emergency interventions in accordance with the urgency of the patient's condition. Triage decisions may be related to both nursing and patient factors. This study aimed to identify factors associated with the accuracy of patient triage in the ED of Arifin Achmad Regional General Hospital in Riau Province, Indonesia. The study utilized a quantitative approach with a descriptive analytic design using a cross-sectional approach. The study population were adult patients aged 18 years and above who came to the ED, with a sample size of 80 patients selected through consecutive sampling. Triage data were collected using an observation sheet, and triage accuracy was assessed by comparing nursing triage decisions with those of the attending ED physician, considered as the gold standard. Data analysis included univariate and bivariate analyses using Fisher's exact test, as well as multivariate analysis with logistic regression. The majority of respondents were adults aged 18-59 years (n=56, 70.0%), male (n=55, 68.8%), non-trauma emergency status (n=65, 81.2%), self-transported (n=66, 82.5%), and 68 respondents (85%) had accurate triage results. Fisher's exact test showed significant associations between age, gender, mode of transportation, type of emergency, and triage accuracy, with all p-values &lt;0.005. The most significant factors associated with triage accuracy were male gender (OR 8.237, 95% CI= 1.875-36.199, p-value 0.005) and adult age (OR 5.270, 95% CI= 1.267-21.927, p-value 0.022). In conclusion, there were significant associations between age, gender, mode of transportation, type of emergency, and triage accuracy, with male gender and adult age being the most significant factors. Regular monitoring and evaluation of triage implementation are necessary to improve triage accuracy and patient outcomes in the ED.

Impact of triage nurses' recognition of acute coronary syndrome on patients' clinical outcomes: A retrospective study

BackgroundTriage nurses' pivotal role in initial assessment makes their competence crucial. However, the specific impact on Acute Coronary Syndrome (ACS) recognition and outcomes remains unclear in Jordanian healthcare. Understanding this relationship could enhance ACS management and patient outcomes in Jordanian Emergency Departments (EDs). ObjectiveTo assess how triage nurses' recognition of ACS diagnosis affects patient outcomes in Jordan. MethodsThis retrospective study examined records of 150 ACS patients admitted to the coronary care unit (CCU). Data extraction assessed triage nurses' accuracy in recognizing ACS symptoms and the timelines for diagnosis procedures (e.g., electrocardiogram [ECG]) and treatment provision (e.g., thrombolytic) based on triage decisions. It also evaluated the impact on treatment outcomes, including length of stay in the ED and hospital. Multiple linear regression analyses quantified the influence of under-triage on treatment outcomes. ResultsThe sample comprised 150 patients. Most were female (78.7%), aged 45-59.9 years (37.3%). ACS classifications: unstable angina (52.0%), STEMI (38.0%), NSTEMI (10.0%). The study included a cohort of ACS patients, with findings indicating varying degrees of under-triage by triage nurses. Analysis of timelines revealed significant delays in diagnosis and treatment initiation for patients subjected to under-triage. Multiple linear regression analyses demonstrated a robust association between under-triage and prolonged time to essential treatment outcomes, including delays in physician assessment, ECG performance, thrombolytic administration, and extended ED length of stay. ConclusionTriage nurses' knowledge and competency are crucial determinants of accurate ACS recognition and subsequent clinical outcomes for patients presenting to the ED in Jordan. Investing in ongoing education and training programs for triage nurses may lead to improved ACS recognition rates and better patient outcomes in Jordanian healthcare settings.

Variation in Triage to Pediatric vs Adult ICUs Among Adolescents and Young Adults With Asthma Exacerbations

BackgroundOver 90,000 children and adults in the United States are hospitalized with an asthma exacerbation annually, and between 5-34% of these hospitalizations include an admission to an intensive care unit (ICU). It is unclear how adolescent and young adults with severe asthma exacerbations are triaged in the inpatient setting between pediatric (PICUs) and adult ICUs. Using a large multicenter United States cohort, we characterized how hospitals triage adolescents and young adults with asthma exacerbations between PICUs and adult ICUs. Research QuestionHow do hospitals across the United States triage adolescents and young adults with asthma exacerbations between PICUs and adult ICUs? Study Design and MethodsThis was a retrospective cohort study from 2016 to 2022 using the enhanced-claims PINC AI database. Participants were patients aged 12-26 hospitalized with asthma exacerbation and admitted to a PICU or adult ICU. We used nested hierarchical multivariable regression models to quantify changes in the intraclass correlation coefficient (a measure of variation in triage decisions attributable to hospital of admission after accounting for covariables). ResultsAnalyses included 3,946 admissions from 93 hospitals. Stratified by age, the percent of patients admitted to PICUs dropped by 26.9% between age 17 to age 18. In the nested models, the ICC had a large decrease going from the empty model (28.7%) to the age-adjusted model (4.5%) but was similar between the age-adjusted and fully adjusted model (3.4%). InterpretationAmong adolescents and young adults with asthma exacerbations, age ≤18 years was a strong determinant of PICU triage. Further research is needed to understand differences in asthma care and outcomes between PICUs and adult ICUs, as well as how intermediate care units affect triage decision-making from wards and the emergency department.

Accuracy of nurse-based Cipto Triage Method in the emergency department

Background The Cipto Triage Method (CTM) is a method developed in Indonesia for assessing the clinical acuity of emergency department patients. To evaluate the accuracy of the CTM, this study compared triage decisions using the CTM performed by nurses to the triage decisions performed by emergency medical officers (EMO). Methods This cross-sectional diagnostic study was performed in a single-centre referral hospital in Jakarta, Indonesia. Adult patients who attended the emergency department (ED) met the inclusion criteria. Trauma cases, pregnancy, and psychiatric disorders were excluded. All eligible patients underwent triage assessment based on CTM by a triage nurse and were then referred to the ED treatment zone for triage assessment performed by an EMO. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and likelihood ratio were calculated. Results Between April and July 2022, 902 participants were randomly selected. The overall accuracy of the triage assessment of the index test was 84.7%, and the PPV of resuscitation and urgent triage was very good (90.5% and 84.5%, respectively). In the non-urgent category, the PPV was acceptable (72%), but the NPV was very good (92.2%). Conclusions Nurse-based triage assessment using CTM in the ED had very good accuracy in determining clinical acuity.