Triage Level Research Articles

Pediatric Emergency Department Discharge Instructions for Spanish-Speaking Families: Are We Getting It Right?

Patients who speak Spanish are less likely to comply with discharge instructions, adhere to appointments, and take medications than English-speaking patients. However, adherence is improved when discharge instructions are provided in Spanish. This study was designed to assess the frequency of providing written discharge instructions in Spanish to patients who speak Spanish and request interpretation services, and to determine factors associated with receiving written discharge instructions in the preferred language in a pediatric emergency department (ED). This was a retrospective cross-sectional study of all discharges of Spanish-speaking patients who requested an interpreter in 1 year from a large urban pediatric ED and an associated community ED. Multivariable logistic regression was used to identify patient and visit level characteristics associated with receiving written discharge instructions in Spanish. Sixty-one percent of 11,545 patient encounters where a Spanish interpreter was requested received written discharge instructions in Spanish. Patients aged 1 to 3 years (adjusted odds ratio [aOR], 2.87; 95% CI, 2.18-3.77) and aged 4 to 12 years (aOR, 2.06; 95% CI, 1.6-2.65), those seen without a trainee (aOR, 1.37; 95% CI, 1.25-1.5), and those with low acuity triage levels (aOR, 1.6; 95% CI, 1.29-1.97) were more likely to receive discharge instruction in Spanish. Female patients were less likely to receive Spanish discharge instructions (aOR, 0.9; 95% CI, 0.83-0.97). Discharged pediatric ED patients often do not receive written instructions in the preferred language. Patient and provider factors are associated with receiving written instructions in Spanish. Quality improvement efforts are needed to ensure appropriate language discharge education.

The presentational flow chart "unwell adult" of the Manchester Triage System-Curse or blessing?

BackgroundThe presentational flow chart “unwell adult” of the Manchester Triage System (MTS) occupies a special role in this triage system, defined as the nonspecific presentation of an emergency patient. Current scientific studies show that a considerable proportion of emergency room patients present with so-called "nonspecific complaints". The aim of the present study is to investigate in detail the initial assessment of emergency patients triaged according to the presentational flow chart "unwell adult".MethodsMonocentric, retrospective observational study.ResultsData on 14,636 emergency department visits between March 12th and August 12th, 2019 were included. During the observation period, the presentational flow chart "unwell adult" was used 1,143 times and it was the third most frequently used presentational flow chart. Patients triaged with this flow chart often had unspecific complaints upon admission to the emergency department. Patients triaged with the “unwell adult” chart were often classified with a lower triage level. Notably, patients who died in hospital during the observation period frequently received low triage levels. The AUC for the MTS flow chart “unwell adult” and hospitalization in general for older patients (age ≥ 65 years) was 0.639 (95% CI 0.578–0.701), and 0.730 (95% CI 0.714–0.746) in patients triaged with more specific charts. The AUC for the MTS flow chart “unwell adult” and admission to ICU for older patients (age ≥65 years) was 0.631 (95% CI 0.547–0.715) and 0.807 (95% CI 0.790–0.824) for patients triaged with more specific flow charts. Comparison of the predictive ability of the MTS for in-hospital mortality in the group triaged with the presentational flow chart “unwell adult” revealed an AUC of 0.682 (95% CI 0.595–0.769) vs. 0.834 (95% CI 0.799–0.869) in the other presentational flow charts.ConclusionThe presentational flow chart "unwell adult" is frequently used by triage nurses for initial assessment of patients. Patient characteristics assessed with the presentational flow chart "unwell adult" differ significantly from those assessed with MTS presentational flow charts for more specific symptoms. The quality of the initial assessment in terms of a well-functioning triage priority assessment tool is less accurate than the performance of the MTS described in the literature.

Validation of Songklanagarind Pediatric Triage Model in the Emergency Department; a Cross-Sectional Study.

Introduction:An effective triage needs to consider many factors, such as good triage protocol, experienced triage nurses, and patient factors. This study aimed to evaluate the validity of Songklanagarind Pediatric Triage (SPT) for triage of pediatric patients in the emergency department (ED) and identify the factors associated with triage appropriateness. Methods:This study was done in two phases. In the first phase, a team of emergency physicians, a pediatric emergency physician, and a pediatric critical care physician developed SPT model by considering and combining Emergency Severity Index (ESI), Pediatric Assessment Triangle (PAT), Pediatric Canadian Triage and Acuity Scale (PaedCTAS), and Pediatric Septic Shock early warning signs protocol of the hospital as the core concept. In the second phase, a prospective observational study was conducted in the ED of Songklanagarind Hospital, which is a tertiary university hospital in southern Thailand, from September to October 2019 to evaluate the accuracy of the developed triage model. Results: A total of 520 pediatric patients met the inclusion criteria. The pediatric triage model had sensitivity and specificity values of 98.28% and 26.24%, respectively, and positive and negative predictive values of 27.67% and 98.15%, respectively, in prediction of death, hospitalization, and resource utilization. The rates of appropriate triage, over-triage, and under-triage were 68.8%, 28.5%, and 2.7%, respectively. Significant factors associated with appropriateness of triage were underlying disease of the respiratory system (OR = 4.16, 95%CI: 1.75‒9.23), fever (OR = 0.60, 95%CI: 0.41‒0.88), dyspnea (OR: 6.38, 95%CI: 2.51‒16.22), diarrhea (OR = 0.26, 95%CI: 0.09‒0.73), oxygen saturation <95% (OR = 3.18, 95%CI: 1.09‒9.27), accessory muscle use during breathing (OR = 3.67, 95%CI: 1.09‒12.41), and wheezing or rhonchi (OR = 6.96, 95%CI: 3.14‒15.43). Conclusion:SPT showed good correlation of hospital admission rates and resource utilization with pediatric triage level of urgency. However, further efforts are needed to decrease the rates of over- and under-triage.

Developing a stroke alert trigger for clinical decision support at emergency triage using machine learning

BackgroundAcute stroke is an urgent medical condition that requires immediate assessment and treatment. Prompt identification of patients with suspected stroke at emergency department (ED) triage followed by timely activation of code stroke systems is the key to successful management of stroke. While false negative detection of stroke may prevent patients from receiving optimal treatment, excessive false positive alarms will substantially burden stroke neurologists. This study aimed to develop a stroke-alert trigger to identify patients with suspected stroke at ED triage. MethodsPatients who arrived at the ED within 12 h of symptom onset and were suspected of a stroke or transient ischemic attack or triaged with a stroke-related symptom were included. Clinical features at ED triage were collected, including the presenting complaint, triage level, self-reported medical history (hypertension, diabetes, hyperlipidemia, heart disease, and prior stroke), vital signs, and presence of atrial fibrillation. Three rule-based algorithms, ie, Face Arm Speech Test (FAST) and two flavors of Balance, Eyes, FAST (BE-FAST), and six machine learning (ML) techniques with various resampling methods were used to build classifiers for identification of patients with suspected stroke. Logistic regression (LR) was used to find important features. ResultsThe study population consisted of 1361 patients. The values of area under the precision-recall curve (AUPRC) were 0.737, 0.710, and 0.562 for the FAST, BE-FAST-1, and BE-FAST-2 models, respectively. The values of AUPRC for the top three ML models were 0.787 for classification and regression tree with undersampling, 0.783 for LR with synthetic minority oversampling technique (SMOTE), and 0.782 for LR with class weighting. Among the ML models, logistic regression and random forest models in general achieved higher values of AUPRC, in particular in those with class weighting or SMOTE to handle class imbalance problem. In addition to the presenting complaint and triage level, age, diastolic blood pressure, body temperature, and pulse rate, were also important features for developing a stroke-alert trigger. ConclusionsML techniques significantly improved the performance of prediction models for identification of patients with suspected stroke. Such ML models can be embedded in the electronic triage system for clinical decision support at ED triage.

Why do patients leave the Pediatric Emergency Department and what happens to them?

The dropout rate is an important indicator of the quality of emergency services. The authors intend to evaluate factors that influence abandonments in the Pediatric Emergency Department (PED) and to assess outcome and destination of the highest triage leveled patients. Retrospective analysis of the abandonment cases occurring between 2014 and 2016 in a tertiary hospital, including sociodemographic characterization, temporal factors, triage level, referral from other health services, waiting time and patient attendance. The outcome of the highest triage level was also accessed. In the three years analyzed, there were 240171 PED visits, with an abandonment rate of 2.57%. PED abandonment was influenced by higher patient attendance and waiting time, a younger age, and less urgent triage level. Only 1.78% of the urgent or emergent patients (level three or two) abandoned the PED. Of these, 44% sought medical care in the next five days, 41% of which in private institutions, 40% in public hospitals and 19% in primary care. Only 0.81% of those were hospitalized. The majority of patients who leave the PED do not correspond to urgent cases. Health education awareness campaigns, clinical counseling platforms (online or phone services) and the improved accessibility of primary health care may optimize the usage of health resources. The reinforcement of the teams in times of greater affluence may contribute to decrease the rate of abandonment.

Malnourished lung cancer patients have poor baseline functional capacity but show greatest improvements with multimodal prehabilitation.

The objective is to characterize the presence of malnutrition, examine the association between malnutrition and baseline functional capacity (FC), and the extent to which patients benefit from preoperative multimodal prehabilitation in patients undergoing lung resection for cancer. Data from 162 participants enrolled in multimodal prehabilitation or control before lung cancer surgery were analyzed. Malnutrition was measured using the Patient-Generated Subjective Global Assessment (PG-SGA) according to triage levels: low-nutrition-risk (PG-SGA 0-3), moderate-nutrition-risk (4-8) and high-nutrition-risk (≥9). Baseline differences in FC, measured by the 6-minute walk test (6MWT), were compared. Factorial analysis of covariance (ANCOVA) was conducted to examine the effect of nutrition status and intervention on mean change in 6MWT preoperatively. 51.2% patients were considered low-nutrition-risk, 37.7% moderate-nutrition-risk, and 11.1% high-nutrition-risk. Low-nutrition-risk patients had significantly higher 6MWT at baseline (mean of 484 m [standard deviation (SD) = 88]) compared with moderate-nutrition-risk (432 m [SD = 107], P = .005) and high-nutrition-risk groups (416 m [SD = 90], P = .022). The adjusted mean change in 6MWT between prehabilitation vs control was 18.1 (95% confidence interval, 3.8 to 32.3) vs 5.6 m (-14.1 to 25.4) in low-nutrition-risk (P = .309), 28.5 (11 to 46) vs -4 m (-31.3 to 23.4) in moderate-nutrition-risk (P = .053), and 58.9 (16.7 to 101.2) vs -39.7 m (-80.2 to 0.826) in high-nutrition-risk group (P = .001). Lung cancer patients at high-nutrition-risk awaiting surgery had significantly lower baseline FC compared with low-nutrition-risk patients but experienced significant improvements in preoperative FC upon receiving multimodal prehabilitation.

Racial/ethnic disparities in emergency department wait times in the United States, 2013-2017.

Previous research shows that Black and Hispanic patients have longer ED wait times than White patients, but these data do not reflect recent changes such as the Affordable Care Act. In addition, previous research does not account for the non-normal distribution of wait times, wherein a sizable subgroup of patients seen promptly and those not seen promptly experience long wait times. We utilized National Hospital Ambulatory Medical Care Survey (NHAMCS) datasets (2013-2017) to examine mean ED wait time comparing visits by Black, Hispanic, and Asian patients to White patients. Using a two-part regression model, we adjusted for patient, hospital, and health system factors, and estimated differences, for each of five triage levels, in (a) likelihood of waiting at least 5 min and (b) difference in wait time among those not seen promptly. Our cohort included 38,800 White, 14,838 Black, 10,619 Hispanic, and 1257 Asian patient visits. Black (triage level 3) and Hispanic (triage levels 3 and 4) patients had longer mean wait times than White patients. Adjusted likelihood of not being seen promptly was lower among Blacks (triage levels 3, 4 and 5), Hispanics (triage level 5) and Asians (triage level 5) compared to Whites. Among those waiting at least 5 min, adjusted wait time was longer among Blacks in triage level 3 (5.2 min, 95% CI, 1.3 to 9.0) and level 4 (2.5 min, 95% CI, 0.2 to 4.9), Hispanics in triage level 4 (4.7 min, 95% CI, 1.7 to 7.7) and Asians in triage level 5 (16.3 min, 95% CI, 0.6 to 31.9) compared to Whites. Minority patients were less likely to wait to be seen, but waited longer if not seen promptly. These data exhibit that ED wait time disparities persist for African American and Hispanic patients and extend this observation to Asian patients.

Differentiating sepsis from similar groups of symptoms at triage level in emergency care.

Conditions that have similar initial presentations as sepsis may make early recognition of sepsis in an emergency room (ER) difficult. We investigated whether selected physiologic and metabolic parameters can be reliably used in the emergency department to differentiate sepsis from other disease states that mimic it, such as dehydration and stroke. Loess regression on retrospective follow-up chart data of patients with sepsis-like symptoms (N = 664) aged 18+ in a large ER in Hungary was used to visualize/identify cutoff points for sepsis risk. A multivariate logistic regression model based on standard triage data was constructed with its corresponding receiver operating characteristic (ROC) curve and compared with another model constructed based on current sepsis guidelines. Age, bicarbonate, HR, lactate, pH, and body temperature had U, V, W, or reverse U-shaped associations with identifiable inflexion points, but the cutoff values we identified were slightly different from guideline cutoff values. In contrast to the guidelines, no inflexion points could be observed for the association of sepsis with SBP, DPB, MAP, and RR and therefore were treated as continuous variables. Compared to the guidelines-based model, the triage data-driven final model contained additional variables (age, pH, bicarbonate) and did not include lactate. The data-driven model identified about 85% of sepsis cases correctly, while the guidelines-based model identified only about 70% of sepsis cases correctly. Our findings contribute to the growing body of evidence for the necessity of finding improved tools to identify sepsis at early time points, such as in the ER.

Impact of COVID-19 Outbreak on Emergency Visits and Emergency Consultations: A Cross-Sectional Study.

BackgroundThis study aimed to determine the effect of the COVID-19 outbreak on emergency department (ED) visits and emergency consultations according to the triage levels indicating the patients' urgency.MethodsA cross-sectional retrospective study was performed in the ED of a tertiary training and research hospital between 1 April and 31 May 2020 in İstanbul, Turkey. The daily count of emergency visits and the count of the emergency consultations during the study period were recorded. The emergency visits and consultations in the same months of the previous year (1 April-31 May 2019) were included as a control group.ResultsApproximately 50% reduction in ED visits and a 30% reduction in emergency consultations were detected. A significant decrease was detected in all triage levels of visits and emergency consultations (p < 0.001). Within total ED visits, a significant increase was found in the red (4.32% vs. 4.74%) and yellow (21.66% vs. 33.16%) triage levels visit rates, while the green (74.01% vs. 62.1%) level was decreased. Within total emergency consultations, anesthesiology (0.83% vs. 1.56%) and cardiology (3.17% vs. 3.75%) consultation rates increased, neurology (2.22% vs. 1.15%), orthopedics (3.53% vs. 3.01%), and ophthalmology (2.89% vs. 1.57%) consultation rates decreased, internal medicine (2.45% vs. 2.49%), and general surgery (4.46% vs. 4.64%) consultation rates did not change.ConclusionsDuring the COVID-19 pandemic, ED visits at all triage levels decreased. While the rate of critical patient visits increased, non-emergency patient visit rates decreased. The total count of consultations decreased, while the total consultation rates increased. The management of the COVID-19 pandemic will be easier by using or developing appropriate triage scores, as well as establishing good interdisciplinary coordination.

Prioritization for the conservation of Mexico’s cloud forests

Cloud forests are ecosystems with a restricted distribution and high biodiversity, but they are highly threatened due to land use change. The objective of this study is to evaluate and prioritize existing cloud forest fragments to achieve their long-term conservation, combining threat levels and the potential response capacity of various conservation instruments in Mexico, through a triage tool. Threat levels were calculated based on human disturbance coverage, estimated specifically for Mexico. The response capacity was estimated using the presence of the different conservation instruments in each fragment. Once the triage level per fragment was obtained, these were analyzed by ecoregion. The results showed that the area of primary cloud forest, has been reduced between 53–73%, and only 31.6% (including primary and secondary forest) is under some protection scheme. We identified a group of fragments on the Pacific slope that require special attention due to the small coverage and their high level of priority. The ecoregions: Sierra Madre del Sur of Guerrero and Oaxaca, Los Altos de Chiapas, Sierra Madre Oriental and Central Mexico corresponding to the largest concentration of cloud forest in the trans-Mexican volcanic belt,70% of which are listed as a priority for emergency protection.

Benchmarking Triage Capability of Symptom Checkers Against That of Medical Laypersons: Survey Study.

BackgroundSymptom checkers (SCs) are tools developed to provide clinical decision support to laypersons. Apart from suggesting probable diagnoses, they commonly advise when users should seek care (triage advice). SCs have become increasingly popular despite prior studies rating their performance as mediocre. To date, it is unclear whether SCs can triage better than those who might choose to use them.ObjectiveThis study aims to compare triage accuracy between SCs and their potential users (ie, laypersons).MethodsOn Amazon Mechanical Turk, we recruited 91 adults from the United States who had no professional medical background. In a web-based survey, the participants evaluated 45 fictitious clinical case vignettes. Data for 15 SCs that had processed the same vignettes were obtained from a previous study. As main outcome measures, we assessed the accuracy of the triage assessments made by participants and SCs for each of the three triage levels (ie, emergency care, nonemergency care, self-care) and overall, the proportion of participants outperforming each SC in terms of accuracy, and the risk aversion of participants and SCs by comparing the proportion of cases that were overtriaged.ResultsThe mean overall triage accuracy was similar for participants (60.9%, SD 6.8%; 95% CI 59.5%-62.3%) and SCs (58%, SD 12.8%). Most participants outperformed all but 5 SCs. On average, SCs more reliably detected emergencies (80.6%, SD 17.9%) than laypersons did (67.5%, SD 16.4%; 95% CI 64.1%-70.8%). Although both SCs and participants struggled with cases requiring self-care (the least urgent triage category), SCs more often wrongly classified these cases as emergencies (43/174, 24.7%) compared with laypersons (56/1365, 4.10%).ConclusionsMost SCs had no greater triage capability than an average layperson, although the triage accuracy of the five best SCs was superior to the accuracy of most participants. SCs might improve early detection of emergencies but might also needlessly increase resource utilization in health care. Laypersons sometimes require support in deciding when to rely on self-care but it is in that very situation where SCs perform the worst. Further research is needed to determine how to best combine the strengths of humans and SCs.

Radiographic findings of SARS-CoV-2 infection.

Study objectiveThe 2019–20 coronavirus pandemic is caused by the severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2), which causes coronavirus disease 2019 (COVID‐19). This study was undertaken to identify and compare findings of chest radiography and computed tomography among patients with SARS‐CoV‐2 infection.MethodsThis retrospective study was undertaken at a tertiary care center. Eligible subjects included consecutive patients age 18 and over with documented SARS‐CoV‐2 infection between March and July 2020. The primary outcome measures were results of chest radiography and computed tomography among patients with documented SARS‐CoV‐2 infection.ResultsAmong 724 subjects, most were admitted to a medical floor (46.4%; N = 324) or admitted to an ICU (10.9%; N = 76). A substantial number of subjects were intubated during the emergency department visit or inpatient hospitalization (15.3%; N = 109). The majority of patients received a chest radiograph (80%; N = 579). The most common findings were normal, bilateral infiltrates, ground‐glass opacities, or unilateral infiltrate. Among 128 patients who had both chest radiography and computed tomography, there was considerable disagreement between the 2 studies (52.3%; N = 67; 95% confidence interval: 43.7% to 61.0%).). The presence of bilateral infiltrates (infiltrates or ground‐glass opacities) was associated with clinical factors including older age, ambulance arrivals, more urgent triage levels, higher heart rate, and lower oxygen saturation. Bilateral infiltrates were associated with poorer outcomes, including higher rate of intubation, greater number of inpatient days, and higher rate of death.ConclusionsCommon radiographic findings of SARS‐CoV‐2 infection include infiltrates or ground‐glass opacities. There was considerable disagreement between chest radiography and computed tomography. Computed tomography was more accurate in defining the extent of involved lung parenchyma. The presence of bilateral infiltrates was associated with morbidity and mortality.

Disposition Decision Support by Laboratory Based Outcome Prediction.

Disposition is one of the main tasks in the emergency department. However, there is a lack of objective and reliable disposition criteria, and diagnosis-based risk prediction is not feasible at early time points. The aim was to derive a risk score (TRIAL) based on routinely collected baseline (TRIage level and Age) and Laboratory data—supporting disposition decisions by risk stratification based on mortality. We prospectively included consecutive patients presenting to the emergency department over 18 weeks. Data sets of routinely collected baseline (triage level and age) and laboratory data were used for multivariable logistic regression to develop the TRIAL risk score predicting mortality. Routine laboratory variables and disposition cut-offs were chosen beforehand by expert consensus. Risk stratification was based on low risk (<1%), intermediate risk (1–10%), and high risk (>10%) of in-hospital mortality. In total, 8687 data sets were analyzed. Variables identified to develop the TRIAL risk score were triage level (Emergency Severity Index), age, lactate dehydrogenase, creatinine, albumin, bilirubin, and leukocyte count. The area under the ROC curve for in-hospital mortality was 0.93. Stratification according to the TRIAL score showed that 67.5% of all patients were in the low-risk category. Mortality was 0.1% in low-risk, 3.5% in intermediate-risk, and 26.2% in high-risk patients. The TRIAL risk score based on routinely available baseline and laboratory data provides prognostic information for disposition decisions. TRIAL could be used to minimize admission in low-risk and to maximize observation in high-risk patients.

Predicting waiting and treatment times in emergency departments using ordinal logistic regression models.

Since providing timely care is the primary concern of emergency departments (EDs), long waiting times increase patient dissatisfaction and adverse outcomes. Especially in overcrowded ED environments, emergency care quality can be significantly improved by developing predictive models of patients' waiting and treatment times to use in ED operations planning. Retrospective data on 37,711 patients arriving at the ED of a large urban hospital were examined. Ordinal logistic regression models were proposed to identify factors causing increased waiting and treatment times and classify patients with longer waiting and treatment times. According to the proposed ordinal logistic regression model for waiting time prediction, age, arrival mode, and ICD-10 encoded diagnoses are all significant predictors. The model had 52.247% accuracy. The model for treatment time showed that in addition to age, arrival mode, and diagnosis, triage level was also a significant predictor. The model had 66.365% accuracy. The model coefficients had negative signs in the corresponding models, indicating that waiting times are negatively related to treatment times. By predicting patients' waiting and treatment times, ED workloads can be assessed instantly. This enables ED personnel to be scheduled to better manage demand supply deficiencies, increase patient satisfaction by informing patients and relatives about expected waiting times, and evaluate performances to improve ED operations and emergency care quality.

The effect of overcrowding in emergency departments on the admission rate according to the emergency triage level.

Overcrowding in emergency departments is a serious public health issue. Recent studies have reported that overcrowding in emergency departments affects not only the quality of emergency care but also clinical decisions about admission. However, no studies have examined the characteristics of the patient groups whose admission rate is influenced by such overcrowding. This retrospective cohort study was conducted in a single emergency department between January 1 and December 31, 2018. Patients over 19 years old were enrolled and divided into three groups according to the degree of overcrowding—high, low, and non—based on the total number of patients in the emergency department. An emergency triage tool (the Korean Triage and Acuity Scale) was used, which categorizes patients into five different levels. We analyzed whether the degree of change in the admission rate according to the extent of overcrowding differed for each triage group. There were 73,776 patients in this study. In the analysis of all patient groups, the admission rate increased as the degree of overcrowding rose (the adjusted odds ratio for admission was 1.281 (1.225–1.339) in the high overcrowding group versus the non-overcrowding group). The analysis of the patients in each triage level showed an increase in the admission rate associated with the overcrowding, which was greater in the patient groups with a lower triage level (adjusted odds ratios for admission in the high overcrowding group versus non-overcrowding group: Korean Triage and Acuity Scale level 3 = 1.215 [1.120–1.317], level 4 = 1.294 [1.211–1.382], and level 5 = 1.954 [1.614–2.365]).

Assessment of triage accuracy by Nurses, and delay care of acute myocardial infarction patients admitted to Emergency Department: retrospective analysis from Shifa International Hospital, Pakistan

Background: Coronary vascular disease (CVD) is the premier cause of fatality in the world. In Pakistan, 30 to 40% of all deaths occur due to CVD. The emergency department triage is carried out to prioritize the care of critical patients. Errors during triage may lead to mortality and morbidity of the patient. The current study's objective is to determine the triage process of acute myocardial infarction patients and its associated accuracy and delays during the acute myocardial infarction process of care in the emergency department.&#x0D; Methods: In this descriptive study, data were collected retrospectively from Shifa International Hospital. The consent was taken from participating Registered nurses (RNs) who were involved in the triage process. Nurses with experience of less than 1 year in the emergency department were not part of the study, while only those patients with symptoms indicative of Acute Myocardial Infarction and age of 21 years or older were included in the current study. EMR system was used on a daily basis as a method to capture data for the study. The actual clock time in minutes from arrival until triage and obtain ECG as greater than 10 minutes was identified as a delay.&#x0D; Results: The 8 R.N. participated in the current study with a mean age and experience of 28.11 years and 4.77 years. The patients' age was 22-74 years, which consist of 58.9% male and 41.1% female. Out of 224 patients, 20.53% of patients were smokers, 39.3% were diabetic, 44.6% were CVD, and 78.5% were reported for chest pain. Delay care such as the mean triage and ECG time recorded was 6.75, and 7.30 min, the mean E.R. physician and Cardiology resident assessment was 11 min and 25.19 min, respectively, which were significantly found according to the recommended guidelines of AHA. The triage accuracy in the current study was recorded as 80.35%.&#x0D; Conclusion: In Pakistan, no proper triage system is developed, and no time limits and guidelines are defined for the completion of the triage process. In the current study, the triage level designations, ECG delay, E.R. and cardiology resident assessment delay were found insignificant, and triage designation was found inaccurate with 19.6% of patients, which lead to delay the re-perfusion therapy. In patients with AMI symptoms, triage accuracy and quick ECG helps E.R. physician assessment to take a quick better decision for cardiac care. It helps patients to get re-perfusion therapy on time for acute myocardial infarction.

Estimation of unregistered patients who left without being seen: At an urban mid-sized Canadian community emergency department.

To determine whether changes to the appearance of an emergency department (ED) waiting room influenced the number of patients who left without being seen (LWBS). Retrospective analysis using National Ambulatory Care Reporting System data collected at the time of patient registration. The ED of Belleville General Hospital, a mid-sized secondary care community hospital in Ontario with a catchment population of 125 000. All unscheduled patients registering at the hospital to be seen in the ED from July 1 to December 31, 2016 (control period), and from July 1 to December 31, 2017 (study period). The volume of patients registering by Canadian Triage and Acuity Scale (CTAS) level to be seen in the ED during the study period compared with the volume of patients registering during the control period, and the number of LWBS during the 2 time periods. The average number of patients registered per month was significantly greater in the study period than in the control period (t 10 = -5.53, P < .01). A total increase of 1881 registrations was recorded in the study period, or 10.47% (increase per month ranged from 9.59% to 11.66%). The proportion of patients with less acute triage scores decreased in the study period; however, the differences in CTAS levels between the 2 years was not statistically significant (χ 2 = 1.05, P = .90). The number of LWBS according to CTAS level was lower in all categories in the study period, including those in the less acute levels, decreasing from 60 in CTAS 5 in 2016 to 45 in 2017, and 585 in CTAS 4 in 2016 to 330 in 2017. Overall, the distribution of LWBS by CTAS level was significantly different between the control and study periods (P < .01). The number of patients registering is influenced by the apparent high or low occupancy of the waiting area at the time of registration.

Characteristics of Emergency Department Visits by Homeless Young Adults in the U.S.

PurposeHomeless young adults often seek health care at emergency departments (EDs) after they are no longer able to ignore a pressing health problem. However, a dearth of literature examines homeless young adults' ED visits. This study aimed to increase understanding of ED visits among homeless young adults within the U.S. MethodsData for this study were obtained from the 2011–2015 National Hospital Ambulatory Medical Care Survey. The sample for this study included all ED visits made by homeless and housed young adults aged 18–29 years (unweighted N = 25,068). ResultsED visits by homeless young adults were distinguished in terms of gender, region, payment sources, and triage level. Furthermore, ED visits made by homeless young adults were more likely related to mental health and suicide. Homeless young adults' ED visits were characterized by a longer average length of stay and were less likely to be referred to a physician or clinic for follow-up, given medication, or have a procedure performed in the ED. ConclusionsThis study highlighted differences in ED utilization for homeless young adults. The findings of this study suggest a need to further examine the characteristics of ED services received by homeless young adults to better understand differences in ED service receipt related to housing status. This knowledge can inform efforts to reduce costs through improving access to housing and outpatient mental health care and reducing stigma among health care professionals to ensure continuity of care.

Surge Capacity and Mass Casualty Incidents Preparedness of Emergency Departments in a Metropolitan City: a Regional Survey Study.

BackgroundEmergency departments (EDs) generally receive many casualties in disaster or mass casualty incidents (MCI). Some studies have conceptually suggested the surge capacity that ED should have; however, only few studies have investigated measurable numbers in one community. This study investigated the surge capacity of the specific number of accommodatable patients and overall preparedness at EDs in a metropolitan city.MethodsThis cross-sectional study officially surveyed surge capacity and disaster preparedness for all regional and local emergency medical centers (EMC) in Seoul with the Seoul Metropolitan Government's public health division. This study developed survey items on space, staff, stuff, and systems, which are essential elements of surge capacity. The number of patients acceptable for each ED was investigated by triage level in ordinary and crisis situations. Multivariate linear regression analysis was performed on hospital resource variables related to surge capacity.ResultsIn the second half of 2018, a survey was conducted targeting 31 EMC directors in Seoul. It was found that all regional and local EMCs in Seoul can accommodate 848 emergency patients and 537 non-emergency patients in crisis conditions. In ordinary situations, one EMC could accommodate an average of 1.3 patients with Korean Triage and Acuity Scale (KTAS) level 1, 3.1 patients with KTAS level 2, and 5.7 patients with KTAS level 3. In situations of crisis, this number increased to 3.4, 7.8, and 16.2, respectively. There are significant differences in surge capacity between ordinary and crisis conditions. The difference in surge capacity between regional and local EMC was not significant. In both ordinary and crisis conditions, only the total number of hospital beds were significantly associated with surge capacity.ConclusionIf the hospital's emergency transport system is ideally accomplished, patients arising from average MCI can be accommodated in Seoul. However, in a huge disaster, it may be challenging to handle the current surge capacity. More detailed follow-up studies are needed to prepare a surge capacity protocol in the community.

PatientFlowNet: A Deep Learning Approach to Patient Flow Prediction in Emergency Departments

Emergency Department (ED) crowding is a major public health challenge since it can seriously impact patient outcomes; and accurate prediction of patient flow in EDs is essential for improving operational efficiency and quality of care. We present a deep learning framework to predict patient flow rates in EDs, namely the rates of arrival, treatment, and discharge for patients across all triage levels. Our model detects short-term and long-term temporal dependencies within the time-series data of a given patient-flow variable, as well as dependencies between time-series data of different patient-flow variables. We implement this framework as a convolutional neural network, which we call PatientFlowNet. Our proposed model learns simultaneously from multiple flow variables over a long temporal window and predicts future values of arrival, treatment, and discharge rates in the ED. We benchmark our model against state-of-the-art methods on data from EDs in three different hospitals. Results show that PatientFlowNet achieves superior prediction accuracy, compared to the baseline methods, and yields a mean absolute error that is 4.8% lower than the leading baseline. Furthermore, we provide a visual and interpretable representation of the learned dependencies by our model, between patient-flow variables in EDs.