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Objective We sought to compare strengths of association among multiple emergency department (ED) input, throughput and output metrics and the outcome of 72-h ED re-visits. Methods This database analysis used healthcare administrative data from three urban, university-affiliated EDs in Calgary, Canada, calendar years 2010-2014. We used data from all patients presenting to participating EDs during the study period, and the primary analysis was performed on patients discharged from the ED. Regression models quantified the association between input, throughput and output metrics and the risk of return ED visit within 72 h of discharge from the index ED encounter. Strength of association between the crowding metrics and 72-h ED re-visits was compared using Akaike's Information Criterion. ResultsThe findings of this study are based on data from 845,588 patient encounters ending in discharge. The input metric with the strongest association with 72-h re-visits was median ED waiting time. The throughput metric with the strongest association with 72-h re-visits was the ED occupancy. The output metric with the strongest association with 72-h re-visits was the median inpatient boarding time. Conclusion Input, throughput and output metrics are all associated with 72-h re-visits. Delays in any of these operational phases have detrimental effects on patient outcomes. ED waiting time, ED occupancy, and boarding times are the most meaningful input, throughput and output metrics. These should be the preferred metrics for quantifying ED crowding in research and quality improvement efforts, and for clinicians to monitor ED crowding in real time. Keywords Emergency department crowding • Administration • Health services RésuméObjectif Nous avons cherché à comparer la force de l'association entre plusieurs paramètres d'entrée, de débit et de sortie des services d'urgence (SU) et l'issue des nouvelles visites aux SU après 72 heures. Méthodes Cette analyse de base de données a utilisé des données administratives sur les soins de santé de trois services d'urgence urbains affiliés à une université à Calgary, au Canada, pour les années civiles 2010-2014. Nous avons utilisé les This work was presented in part at the 2019 Canadian Association of Emergency Physicians Annual Scientific Meeting.

which 3440 were transferred to the EP (67.4%), 2958 of EP assessed callers (86.0%) had a family doctor, but only one-quarter of such patients could contact their family doctor. Overall, 2301/3440 "red" callers did not attend an ED (67.0%) compared to 2508/4770 in the control period (52.6%), for an absolute reduction of 14.4% (95% CI 12.2 to 16.4%, p < 0.0001). In callers for those <17 years old there was a 20.3% (95% CI 16.5 to 24.1%) reduction in ED visits compared to the control group: 771/1520 (50.7%) vs 364/1067 (30.4%). 40% of callers attending an ED (458/1139) were advised to try non-ED follow up by the MD and 108 (9.5%) were admitted, with no difference in 30-day mortality between groups. Age and CTAS distribution were similar between the two groups and the non MD-transferred cohort. Mean caller satisfaction was excellent (4.7/5.0). Conclusion: EP supplementation of a RN advice service has the potential to reduce ED visits by almost 15% while providing excellent safety and satisfaction. Keywords: input mitigation, telemedicine, emergency department crowding Introduction: Over 700 different input, throughput and output metrics have been used to quantify ED crowding. Of these, only ED length-of-stay (ED LOS) has been shown to be associated with mortality. No comparative evaluation of ED crowding metrics has been performed to determine which ones have the strongest association with patient mortality. The objective of this study was to compare the strength of association of common ED input, throughput and output metrics to patient mortality. Methods: Administrative data from five years of ED visits (2011)(2012)(2013)(2014) at three urban EDs were linked to develop a database of over 900,000 ED visits with patient demographics, electronic time stamps for care processes, dispositions and outcomes. The data were randomly divided into three partitions of equal size. Here we report the findings from one partition of 253,938 ED visits. The remaining two data partitions will be used to validate these findings. Commonly-used crowding metrics were quantified and aggregated by day or by shift (0800-1600, 1600-2400, 2400-0800), and the shift-specific metrics assigned to each patient. The primary outcome was 7-day all-cause mortality. Multilevel logistic regression models were developed for 7-day mortality, with selected ED crowding metrics and a common set of confounders as predictors. The strength of association between the crowding metrics and mortality was compared using Akaike's Information Criterion (AIC) and the Bayesian Information Criterion (BIC): ED crowding metrics with lower AIC and BIC have stronger associations with 7-day mortality. Results: Of 909,000 ED encounters, 124,679 (16.5%) arrived by EMS, 149,233(19.7%) were admitted, and 3,808 patients (0.5%) died within 7 days of ED arrival. Of input metrics, the model with ED wait-time was better (i.e. had a smaller AIC and BIC) than models for daily census, ED occupancy or LWBS proportion for predicting 7-day mortality. Of throughput metrics, the model with me...

BackgroundSpatial scan statistics have been used for the identification of geographic clusters of elevated numbers of cases of a condition such as disease outbreaks. These statistics accompanied by the appropriate distribution can also identify geographic areas with either longer or shorter time to events. Other authors have proposed the spatial scan statistics based on the exponential and Weibull distributions.ResultsWe propose the log-Weibull as an alternative distribution for the spatial scan statistic for time to events data and compare and contrast the log-Weibull and Weibull distributions through simulation studies. The effect of type I differential censoring and power have been investigated through simulated data. Methods are also illustrated on time to specialist visit data for discharged patients presenting to emergency departments for atrial fibrillation and flutter in Alberta during 2010–2011. We found northern regions of Alberta had longer times to specialist visit than other areas.ConclusionsWe proposed the spatial scan statistic for the log-Weibull distribution as a new approach for detecting spatial clusters for time to event data. The simulation studies suggest that the test performs well for log-Weibull data.

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