Objectives: No widely used triage instrument accurately assesses patient acuity. The Emergency Severity Index (ESI) promises to facilitate reliable acuity assessment and possibly predict patient ...disposition. However, reliability and validity of ESI scores have not been established in emergency departments (EDs) outside the original research sites, and version 3 (v.3) of the ESI has not been evaluated. The study hypothesis was that scores on the ESI v.3 show good interrater reliability and predict hospital admission, admission site, and death. Methods: The authors conducted an ED‐based cross‐sectional retrospective study of 403 systematically selected ED records of patients who presented to an academic medical center. Twenty‐seven variables were ed, including triage level assigned, admission status, site, and death. Using a standard process, the researchers determined the true triage level. Weighted kappa and Pearson correlation were used to calculate interrater reliability between true triage level and triage score assigned by the registered nurse (RN). The relationships between the true ESI level and admission, admission site, and death were assessed. Results: Interrater reliability between RN ESI level and the true ESI level was kappa = 0.89; Pearson r= 0.83 (p < 0.001). Hospital admission by ESI level was as follows: 1 (80%), 2 (73%), 3 (51%), 4 (6%), and 5 (5%). A higher percentage of ESI level‐1 and level‐2 patients (40%, 12%) were admitted to the intensive care unit than ESI levels 3–5 (2%, 0%, 0%). Admission to telemetry for ESI levels 1‐5 was 20%, 19%, 7%, 1%, and 0%, respectively. Three of four patients who died were ESI level 1 or 2. Conclusions: Scores on the ESI assigned by nurses have excellent interrater reliability and predict hospital admission and location of admission.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
Background: The Emergency Severity Index (ESI) may not recognize high-risk patients with heart failure (HF) efficiently.Aim: This study aimed to compare the diagnostic validity and mistriage rates of ...the ESI plus the Capnometer (Capno) and ESI alone among dyspneic patients with HF.Method: This quasi-experimental group (random assignment) study was conducted within April 2019-February 2020. Patients were randomly assigned to the ESI+Capno and ESI groups. Triage levels, resources used, disposition and door to an electrocardiogram, and physician visit were compared among patients admitted to the Cardiac Care Unit (CCU), the Cardiac Unit (CU), or discharged from the ED. Interobserver agreement (Kappa) was used to assess the reliability of the ESI.Results: In this study, 65 HF patients were assigned to the ESI+Capno (n=36) and ESI (n=29) groups. The undertriage rates were 0% and 10% and the overtriage rates were 10% and 31% in the ESI+Capno and ESI groups, respectively. Sensitivity, specificity, and accuracy to recognize high-risk HF patients were 100%, 60%, and 90% for the ESI+Capno group and 62.5%, 42.86%, and 48.36% for the ESI group.Implications for Practice: The addition of Capno to the ESI increased the validity of triage decisions to recognize high-risk HF patients, compared to the ESI alone. It is recommended that decisions regarding triage HF patients be made after that an End-tidal Co2 is considered into the decision-making process.
Background
Emergency department (ED) triage is performed to prioritize care for patients with critical and time-sensitive illness. Triage errors create opportunity for increased morbidity and ...mortality. Here, we sought to measure the frequency of under- and over-triage of patients by nurses using the Emergency Severity Index (ESI) in Brazil and to identify factors independently associated with each.
Methods
This was a single-center retrospective cohort study. The accuracy of initial ESI score assignment was determined by comparison with a score entered at the close of each ED encounter by treating physicians with full knowledge of actual resource utilization, disposition, and acute outcomes. Chi-square analysis was used to validate this surrogate gold standard, via comparison of associations with disposition and clinical outcomes. Independent predictors of under- and over-triage were identified by multivariate logistic regression.
Results
Initial ESI-determined triage score was classified as inaccurate for 16,426 of 96,071 patient encounters. Under-triage was associated with a significantly higher rate of admission and critical outcome, while over-triage was associated with a lower rate of both. A number of factors identifiable at time of presentation including advanced age, bradycardia, tachycardia, hypoxia, hyperthermia, and several specific chief complaints (i.e., neurologic complaints, chest pain, shortness of breath) were identified as independent predictors of under-triage, while other chief complaints (i.e., hypertension and allergic complaints) were independent predictors of over-triage.
Conclusions
Despite rigorous and ongoing training of ESI users, a large number of patients in this cohort were under- or over-triaged. Advanced age, vital sign derangements, and specific chief complaints—all subject to limited guidance by the ESI algorithm—were particularly under-appreciated.
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IZUM, KILJ, NUK, OILJ, PILJ, PNG, SAZU, UL, UM, UPUK, VSZLJ
The term Artificial Intelligence (AI) was first coined in the 1960s and has made significant progress up to the present day. During this period, numerous AI applications have been developed. GPT-4 ...and Gemini are two of the best-known of these AI models. As a triage system The Emergency Severity Index (ESI) is currently one of the most commonly used for effective patient triage in the emergency department. The aim of this study is to evaluate the performance of GPT-4, Gemini, and emergency medicine specialists in ESI triage against each other; furthermore, it aims to contribute to the literature on the usability of these AI programs in emergency department triage.
Our study was conducted between February 1, 2024, and February 29, 2024, among emergency medicine specialists in Turkey, as well as with GPT-4 and Gemini. Ten emergency medicine specialists were included in our study but as a limitation the emergency medicine specialists participating in the study do not frequently use the ESI triage model in daily practice. In the first phase of our study, 100 case examples related to adult or trauma patients were extracted from the sample and training cases found in the ESI Implementation Handbook. In the second phase of our study, the provided responses were categorized into three groups: correct triage, over-triage, and under-triage. In the third phase of our study, the questions were categorized according to the correct triage responses.
In the results of our study, a statistically significant difference was found between the three groups in terms of correct triage, over-triage, and under-triage (p < 0.001). GPT-4 was found to have the highest correct triage rate with an average of 70.60 (±3.74), while Gemini had the highest over-triage rate with an average of 35.2 (±2.93) (p < 0.001). The highest under-triage rate was observed in emergency medicine specialists (32.90 (±11.83)). In the ESI 1–2 class, Gemini had a correct triage rate of 87.77%, GPT-4 had 85.11%, and emergency medicine specialists had 49.33%.
In conclusion, our study shows that both GPT-4 and Gemini can accurately triage critical and urgent patients in ESI 1&2 groups at a high rate. Furthermore, GPT-4 has been more successful in ESI triage for all patients. These results suggest that GPT-4 and Gemini could assist in accurate ESI triage of patients in emergency departments.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ
•The pandemic had an impact on the ESI triage of trauma patients.•During the early phase of the pandemic in 2020, COVID-19 induced over-triage of trauma patients.•Trauma patients assigned ESI level 2 ...varied significantly in vital signs, resource utilization, and clinical outcomes based on age.
The ESI algorithm is widely used to triage patients in the emergency room. However, few studies have assessed the reliability of ESI to accurately triage trauma patients. The aim of this study was to compare vital signs, resource utilization, and patient outcomes among trauma patients during the pandemic in 2020 vs. the previous year prior to the pandemic.
This retrospective study was conducted over a 24-month period at an urban adult level one trauma center. Demographic and clinical characteristics, resource utilization, and patient outcomes were extracted from the electronic medical records and trauma registry. Trauma patients assigned ESI level 2 were stratified by age (<65 years and ≥ 65 years) and year (2019 vs. 2020) for data analysis.
A total of 3,788 trauma patients were included in the study. Males represented 68.4% (2,591) of patients and the median age was 50 years (IQR: 31, 69). The majority of patients were assigned ESI level 2 (2,162, 57.1%) and had a blunt mechanism of injury (3,122, 82.4%). In 2020, patients <65 years of age utilized less resources compared to 2019 (p < 0.001). Likewise, patients >65 years of age required less lab tests OR: 0.1, 95% CI: (0.05 – 0.4), IV fluids OR: 0.2, 95% CI: (0.2 -0.3), IV medications OR: 0.6, 95% CI: (0.4 - 0.7), and specialty consultations OR: 0.4, 95% CI: (0.3 -0.5) compared to 2019 (p < 0.0001). Within 2020, vital signs and resources utilized between younger and elderly patients varied significantly (p < 0.01). Correspondingly, the clinical outcomes between younger and elderly patients within 2020, differed significantly (p < 0.01).
The COVID-19 pandemic affected the triage of trauma patients. During 2020, patients utilized less resources compared to the previous year. Additionally, younger and elderly patients had different vital signs, resource utilization, and clinical outcomes although both being assigned ESI level 2. Younger trauma patients may have been over-triaged in 2020 due to the COVID-19 pandemic.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ
OBJECTIVES: Artificial intelligence companies have been increasing their initiatives recently to improve the results of chatbots, which are software programs that can converse with a human in natural ...language. The role of chatbots in health care is deemed worthy of research. OpenAI's ChatGPT is a supervised and empowered machine learning-based chatbot. The aim of this study was to determine the performance of ChatGPT in emergency medicine (EM) triage prediction.
METHODS: This was a preliminary, cross-sectional study conducted with case scenarios generated by the researchers based on the emergency severity index (ESI) handbook v4 cases. Two independent EM specialists who were experts in the ESI triage scale determined the triage categories for each case. A third independent EM specialist was consulted as arbiter, if necessary. Consensus results for each case scenario were assumed as the reference triage category. Subsequently, each case scenario was queried with ChatGPT and the answer was recorded as the index triage category. Inconsistent classifications between the ChatGPT and reference category were defined as over-triage (false positive) or under-triage (false negative).
RESULTS: Fifty case scenarios were assessed in the study. Reliability analysis showed a fair agreement between EM specialists and ChatGPT (Cohen's Kappa: 0.341). Eleven cases (22%) were over triaged and 9 (18%) cases were under triaged by ChatGPT. In 9 cases (18%), ChatGPT reported two consecutive triage categories, one of which matched the expert consensus. It had an overall sensitivity of 57.1% (95% confidence interval CI: 34-78.2), specificity of 34.5% (95% CI: 17.9-54.3), positive predictive value (PPV) of 38.7% (95% CI: 21.8-57.8), negative predictive value (NPV) of 52.6 (95% CI: 28.9-75.6), and an F1 score of 0.461. In high acuity cases (ESI-1 and ESI-2), ChatGPT showed a sensitivity of 76.2% (95% CI: 52.8-91.8), specificity of 93.1% (95% CI: 77.2-99.2), PPV of 88.9% (95% CI: 65.3-98.6), NPV of 84.4 (95% CI: 67.2-94.7), and an F1 score of 0.821. The receiver operating characteristic curve showed an area under the curve of 0.846 (95% CI: 0.724-0.969, P < 0.001) for high acuity cases.
CONCLUSION: The performance of ChatGPT was best when predicting high acuity cases (ESI-1 and ESI-2). It may be useful when determining the cases requiring critical care. When trained with more medical knowledge, ChatGPT may be more accurate for other triage category predictions.
Patients with active cancer account for a growing percentage of all emergency department (ED) visits and have a unique set of risks related to their disease and its treatments. Effective triage for ...this population is fundamental to facilitating their emergency care.
We evaluated the validity of the Emergency Severity Index (ESI; version 4) triage tool to predict ED-relevant outcomes among adult patients with active cancer.
We conducted a prespecified analysis of the observational cohort established by the National Cancer Institute–supported Comprehensive Oncologic Emergencies Research Network’s multicenter (18 sites) study of ED visits by patients with active cancer (N = 1075). We used a series of χ2 tests for independence to relate ESI scores with 1) disposition, 2) ED resource use, 3) hospital length of stay, and 4) 30-day mortality.
Among the 1008 subjects included in this analysis, the ESI distribution skewed heavily toward high acuity (>95% of subjects had an ESI level of 1, 2, or 3). ESI was significantly associated with patient disposition and ED resource use (p values < 0.05). No significant associations were observed between ESI and the non-ED based outcomes of hospital length of stay or 30-day mortality.
ESI scores among ED patients with active cancer indicate higher acuity than the general ED population and are predictive of disposition and ED resource use. These findings show that the ESI is a valid triage tool for use in this population for outcomes directly relevant to ED care.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ
Objective: To assess the emergency department length of stay (EDLOS) and mortality in each Emergency Severity Index (ESI) triage level. In addition to identifying the cut-off point of EDLOS to ...predict 72-hour in-hospital mortality among adult non-traumatic patients in the ED of a university hospital.Material and Methods: A cross-sectional study was conducted by retrieving patient data from the hospital information system; from January 1, 2014, to December 31, 2018. Patient characteristics, EDLOS, and in-hospital mortality rates were analyzed using the R program. The cut-off values of EDLOS, via the area under the curve for the best prediction of 72-hour in-hospital mortality in patients at different ESI levels, were analyzed by multivariate analysis. Statistical significance was defined as a p-value of ≤0.05.Results: Data from 71,247 patients with 123,356 visits were enrolled. EDLOS significantly decreased across ESI levels and the in-hospital mortality rates were highest in ESI 1, followed by ESI 2 and ESI 3. The predictive ability of EDLOS was the highest for ESI 4, followed by ESI 3. The cut-off point of EDLOS at 3.58 hours showed the best sensitivity, which was a significant risk factor for mortality after adjusting for other significant variables. An EDLOS longer than 4 hours was a significant factor for poor survival in patients with ESI 2 and ESI 3.Conclusion: A practical cut-off point of 4 hours EDLOS can be used to predict 72-hour in-hospital mortality. Healthcare providers in the ED should consider EDLOS as a safety indicator for quality assurance.
Background: Swift diagnosis and treatment of cardiac patients can avert unnecessary hospitalizations. Emergency departments routinely assess patients using the Emergency Severity Index (ESI) method. ...This study compares the effects of two triage methods, cardiac triage, and ESI, on the admission time of acute coronary syndrome patients. Methods: This intervention study aimed to enhance the quality of therapeutic interventions through an intervention design featuring a control group. The research sample comprised all patients referred to the Sayad Shirazi Educational and Medical Center triage unit in Gorgan, Iran. All patients were randomly allocated into two groups: the control group (23 patients) and the intervention group (46 patients), utilizing a simple random allocation method. The control group underwent triage using the Emergency Severity Index, whereas the intervention group received cardiac triage. Triage forms and time-related indices were completed for both groups. Statistical analysis was conducted using descriptive statistics, the Shapiro-Wilk, and the Mann-Whitney tests to compare these characteristics between the two groups, utilizing SPSS version 18. Results: Significant statistical differences were observed between the two groups in several aspects: the average time from the emergency department to the cardiac intensive care unit (p < 0.001), the average duration of presence of a cardiac specialist physician (p < 0.001), the average time from arrival to triage room exit (p < 0.001), and the average hospitalization time (p < 0.001). These time intervals were shorter in the cardiac triage group. Conclusion: Implementing specialized cardiac triage for cardiac patients plays a pivotal role in reducing response times. Cardiac triage can furnish the medical team with more comprehensive information, thereby improving the management of these patients in the emergency department.
Triage is critical to mitigating the effect of increased volume by determining patient acuity, need for resources, and establishing acuity-based patient prioritization. The purpose of this ...retrospective study was to determine whether historical EHR data can be used with clinical natural language processing and machine learning algorithms (KATE) to produce accurate ESI predictive models.
The KATE triage model was developed using 166,175 patient encounters from two participating hospitals. The model was tested against a random sample of encounters that were correctly assigned an acuity by study clinicians using the Emergency Severity Index (ESI) standard as a guide.
At the study sites, KATE predicted accurate ESI acuity assignments 75.7% of the time compared with nurses (59.8%) and the average of individual study clinicians (75.3%). KATE’s accuracy was 26.9% higher than the average nurse accuracy (P <.001). On the boundary between ESI 2 and ESI 3 acuity assignments, which relates to the risk of decompensation, KATE’s accuracy was 93.2% higher, with 80% accuracy compared with triage nurses 41.4% accuracy (P <.001).
KATE provides a triage acuity assignment more accurate than the triage nurses in this study sample. KATE operates independently of contextual factors, unaffected by the external pressures that can cause under triage and may mitigate biases that can negatively affect triage accuracy. Future research should focus on the impact of KATE providing feedback to triage nurses in real time, on mortality and morbidity, ED throughput, resource optimization, and nursing outcomes.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ
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