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The use of triage systems such as the Manchester Triage System (MTS) is a standard procedure to determine the sequence of treatment in emergency departments (EDs). When using the MTS, time targets for treatment are determined. These are commonly displayed in the ED information system (EDIS) to ED staff. Using measurements as targets has been associated with a decline in meeting those targets. This study investigated the impact of displaying time targets for treatment to physicians on processing times in the ED. We analyzed the effects of displaying time targets to ED staff on waiting times in a prospective crossover study, during the introduction of a new EDIS in a large regional hospital in Germany. The old information system version used a module that showed the time target determined by the MTS, while the new system version used a priority list instead. Evaluation was based on 35,167 routinely collected electronic health records from the preintervention period and 10,655 records from the postintervention period. Electronic health records were extracted from the EDIS, and data were analyzed using descriptive statistics and generalized additive models. We evaluated the effects of the intervention on waiting times and the odds of achieving timely treatment according to the time targets set by the MTS. The average ED length of stay and waiting times increased when the EDIS that did not display time targets was used (average time from admission to treatment: preintervention phase=median 15, IQR 6-39 min; postintervention phase=median 11, IQR 5-23 min). However, severe cases with high acuity (as indicated by the triage score) benefited from lower waiting times (0.15 times as high as in the preintervention period for MTS1, only 0.49 as high for MTS2). Furthermore, these patients were less likely to receive delayed treatment, and we observed reduced odds of late treatment when crowding occurred. Our results suggest that it is beneficial to use a priority list instead of displaying time targets to ED personnel. These time targets may lead to false incentives. Our work highlights that working better is not the same as working faster.

Background: The aim of this study is to evaluate whether self-sampling can increase screening attendance of women who do not attend regular screening in Switzerland. Methods: Participants were proactively recruited in Geneva between September 2011 and November 2015. Women (25–69 years) who had not undergone CC screening in the last 3 years were considered eligible. Through a 1 : 1 ratio randomisation, enrolled participants were invited to either undergo liquid-based cytology, which was performed by a health-care provider (control group, CG) or to take a self-sample for HPV-testing, which was mailed to their home (intervention group, IG). Results: A total of 331 and 336 women were randomised in the CG and in the IG, respectively. Overall, 7.3% (95% CI: 4.9–10.6) women in the CG and 5.7% (95% CI: 3.6–8.7) women in the IG did not undergo the initial screening ( P =0.400). There were 1.95% (95% CI: 0.8–4.3) women in the CG and 5.05% (95% CI: 3.1–8.1) women in the IG with a positive screen who did not attend triage and colposcopy ( P =0.036). Conclusions: The participation in CC screening in women offered self-sampling was not higher than among those offered specimen collection by a clinician. Compliance with further follow-up for women with a positive HPV test on the self-sample requires further attention.

IntroductionThe proliferation of unmanned aerial vehicles (UAV) has the potential to change the situational awareness of incident commanders allowing greater scene safety. The aim of this study was to compare UAV technology to standard practice (SP) in hazard identification during a simulated multi-vehicle motor collision (MVC) in terms of time to identification, accuracy and the order of hazard identification. A prospective observational cohort study was conducted with 21 students randomized into UAV or SP group, based on a MVC with 7 hazards. The UAV group remained at the UAV ground station while the SP group approached the scene. After identifying hazards the time and order was recorded. The mean time (SD, range) to identify the hazards were 3 minutes 41 seconds (1 minute 37 seconds, 1 minute 48 seconds-6 minutes 51 seconds) and 2 minutes 43 seconds (55 seconds, 1 minute 43 seconds-4 minutes 38 seconds) in UAV and SP groups corresponding to a mean difference of 58 seconds (P=0.11). A non-parametric permutation test showed a significant (P=0.04) difference in identification order. Both groups had 100% accuracy in hazard identification with no statistical difference in time for hazard identification. A difference was found in the identification order of hazards. (Disaster Med Public Health Preparedness. 2018;12:631-634).

Background Management of patients with suspected coronary artery disease (CAD) and inconclusive stress imaging test findings may result in invasive coronary angiography (ICA). Coronary computed tomographic angiography (CCTA) may be useful in defining the risk of CAD and adverse outcomes in this patient population, as well as in reducing the need for ICA. Methods We prospectively enrolled 199 sequential patients referred by cardiologists for CCTA after either inconclusive or nondiagnostic stress imaging tests. Before CCTA, physicians identified a “planned catheterization” group of patients who would undergo invasive angiography if CCTA were not available. After CCTA testing, patients were followed for ≥2 years. We established the added diagnostic value of the CCTA and its prognostic power in prediction of intermediate-term follow-up events in this patient population as compared to available historical and clinical predictors of CAD, stress ECG, and stress imaging test results using a multivariable Cox proportional hazards survival analysis. Results Both observed data and results of the multivariable model for the prediction of obstructive CAD (>50% stenosis), or major cardiac events (death MI or revascularization), demonstrated that clinical, stress ECG, and imaging results were weakly predictive, whereas CCTA was found to be a strong independent and incremental predictor of the absence of either significant CAD or MACE in this population. None of the 93 patients with normal CCTA scans had MACE events, whereas 18 patients with evidence of CAD on the CCTA results underwent revascularization. Overall, physicians planned ICA in 125 patients (63.0%); after CCTA, ICA was performed in only 32 (16.0%) cases over 2 years. In this population with no other highly effective noninvasive clinical tools for diagnostic and prognostic estimation, the overall negative predictive value of CCTA for either CAD > 50% or MACE for 2 years was 99%. Conclusion Observations from this prospective study demonstrate the significant added diagnostic value and prognostic potential of CCTA in patients with suspected CAD and either inconclusive or nondiagnostic stress test results in real-world settings. Normal CCTA results are associated with excellent intermediate-term prognosis in this clinical subset, and invasive angiography can be safely avoided in the majority of these patients when the results of CCTA are available.

The purpose of this pilot study was to determine whether the addition of educational interventions to required clinical hours promotes confidence in triage decision making among nursing students enrolled in a final capstone course. An experimental design was implemented with randomization of students (n = 14) to 1 of 3 intervention groups or the control group. The Triage Decision Making Inventory was used as a pretest-posttest. Educational strategies implemented included an Advanced Cardiac Life Support course and simulations with debriefing. Interventions were in addition to required clinical hours. A mixed analysis of variance was used to examine the 4 groups by time, with all groups exhibiting higher scores on the Triage Decision Making Inventory from the pretest to the posttest (F (3, 10) = 4.51, P = .03 (η2 = .575). Students who received both the simulations and the Advanced Cardiac Life Support course demonstrated a significant difference across time. As nursing education evolves with the integration of technology, the combination of multiple pedagogies also can enhance confidence in triage decision making among experienced and novice nurses in emergency settings.

To predict hospital admission at the time of ED triage using patient history in addition to information collected at triage. This retrospective study included all adult ED visits between March 2014 and July 2017 from one academic and two community emergency rooms that resulted in either admission or discharge. A total of 972 variables were extracted per patient visit. Samples were randomly partitioned into training (80%), validation (10%), and test (10%) sets. We trained a series of nine binary classifiers using logistic regression (LR), gradient boosting (XGBoost), and deep neural networks (DNN) on three dataset types: one using only triage information, one using only patient history, and one using the full set of variables. Next, we tested the potential benefit of additional training samples by training models on increasing fractions of our data. Lastly, variables of importance were identified using information gain as a metric to create a low-dimensional model. A total of 560,486 patient visits were included in the study, with an overall admission risk of 29.7%. Models trained on triage information yielded a test AUC of 0.87 for LR (95% CI 0.86-0.87), 0.87 for XGBoost (95% CI 0.87-0.88) and 0.87 for DNN (95% CI 0.87-0.88). Models trained on patient history yielded an AUC of 0.86 for LR (95% CI 0.86-0.87), 0.87 for XGBoost (95% CI 0.87-0.87) and 0.87 for DNN (95% CI 0.87-0.88). Models trained on the full set of variables yielded an AUC of 0.91 for LR (95% CI 0.91-0.91), 0.92 for XGBoost (95% CI 0.92-0.93) and 0.92 for DNN (95% CI 0.92-0.92). All algorithms reached maximum performance at 50% of the training set or less. A low-dimensional XGBoost model built on ESI level, outpatient medication counts, demographics, and hospital usage statistics yielded an AUC of 0.91 (95% CI 0.91-0.91). Machine learning can robustly predict hospital admission using triage information and patient history. The addition of historical information improves predictive performance significantly compared to using triage information alone, highlighting the need to incorporate these variables into prediction models.

To determine the validity of the Manchester Triage System (MTS) in emergency care for the general population of patients attending the emergency department, for children and elderly, and for commonly used MTS flowcharts and discriminators across three different emergency care settings. This was a prospective observational study in three European emergency departments. All consecutive patients attending the emergency department during a 1-year study period (2010-2012) were included. Validity of the MTS was assessed by comparing MTS urgency as determined by triage nurses with patient urgency according to a predefined 3-category reference standard as proxy for true patient urgency. 288,663 patients were included in the analysis. Sensitivity of the MTS in the three hospitals ranged from 0.47 (95%CI 0.44-0.49) to 0.87 (95%CI 0.85-0.90), and specificity from 0.84 (95%CI 0.84-0.84) to 0.94 (95%CI 0.94-0.94) for the triage of adult patients. In children, sensitivity ranged from 0.65 (95%CI 0.61-0.70) to 0.83 (95%CI 0.79-0.87), and specificity from 0.83 (95%CI 0.82-0.83) to 0.89 (95%CI 0.88-0.90). The diagnostic odds ratio ranged from 13.5 (95%CI 12.1-15.0) to 35.3 (95%CI 28.4-43.9) in adults and from 9.8 (95%CI 6.7-14.5) to 23.8 (95%CI 17.7-32.0) in children, and was lowest in the youngest patients in 2 out of 3 settings and in the oldest patients in all settings. Performance varied considerably between the different emergency departments. Validity of the MTS in emergency care is moderate to good, with lowest performance in the young and elderly patients. Future studies on the validity of triage systems should be restricted to large, multicenter studies to define modifications and improve generalizability of the findings.

To identify factors known prior to triage that might have predicted hospital admission for patients triaged by the Canadian Triage Acuity Scale (CTAS) as level 5 (CTAS 5, nonurgent) and to determine whether inappropriate triage occurred in the admitted CTAS 5 patients. We reviewed the triage records of patients triaged as CTAS 5 at the emergency departments (EDs) of three tertiary care hospitals between April 2002 and September 2009. Two triage nurses unaware of the study objective independently assigned the CTAS level in 20% of randomly selected CTAS 5 patients who were admitted. We used the kappa statistic (κ) to measure the agreement among the raters in CTAS level between the assessment of the research nurses and the original triage assessment and regression analysis to identify independent predictors of admission to hospital. Of the 37,416 CTAS 5 patients included in this study, 587 (1.6%) were admitted. Agreement on CTAS assignment in CTAS 5 patients who were admitted was κ -0.9, (95% confidence interval [CI] -0.96 to -0.84). Age over 65 (odds ratio [OR] 5.46, 95% CI 4.57 to 6.53) and arrival by ambulance (OR 7.42, 95% CI 6.15 to 8.96) predicted hospital admission in CTAS 5 patients. Most of the CTAS 5 patients who were subsequently admitted to hospital may have qualified for a higher triage category. Two potential modifiers, age over 65 and arrival by ambulance, may have improved the prediction of admission in CTAS 5 patients. However, the consistent application of existing CTAS criteria may also be important to prevent incorrect triage.

BackgroundPublic health mitigation strategies in British Columbia during the pandemic included stay-at-home orders and closure of non-essential services. While most primary physicians’ offices were closed, hospitals prepared for a pandemic surge and emergency departments (EDs) stayed open to provide care for urgent needs. We sought to determine whether ED paediatric presentations prior and during the COVID-19 pandemic changed and review acuity compared with seasonal adjusted prior year.MethodsWe analysed records from 18 EDs in British Columbia, Canada, serving 60% of the population. We included children 0–16 years old and excluded those with no recorded acuity or discharge disposition and those left without being seen by a physician. We compared prepandemic (before the first COVID-19 case), early pandemic (after first COVID-19 case) and peak pandemic (during public health emergency) periods as well as a similar time from the previous year.ResultsA reduction of 57% and 70% in overall visits was recorded in the children’s hospital ED and the general hospitals EDs, respectively. Average daily visits declined significantly during the peak-pandemic period (167.44±40.72) compared with prepandemic period (543.53±58.8). Admission rates increased mainly due to the decrease in the rate of visits with lower acuity. Children with complaints of ‘fever’ and ‘gastrointestinal’ symptoms had both the largest overall volume and per cent reduction in visits between peak-pandemic and prior year (79% and 74%, respectively).ConclusionPaediatric emergency medicine attendances were reduced to one-third of normal numbers during the 2020 COVID-19 lockdown in British Columbia, Canada, with the reduction mainly seen in minor illnesses that do not usually require admission.