Introduction Emergency department (ED) crowding compromises patient outcomes. Existing crowding measures are complex and difficult to use in real-time. This study evaluated readily available single flow variables as crowding measures. Methods Over 2 weeks in a tertiary Canadian ED, we recorded the following potential crowding measures during 168 consecutive two-hour study intervals: total ED patients (census), patients in beds, patients in waiting rooms, patients in treatment areas awaiting MD assessment; number of inpatients boarding, and ED occupancy. We also calculated four complex crowding scores-NEDOCS, EDWIN, ICMED, and a local modification of NEDOCS. We performed ROC analyses to assess the predictive validity of these measures against a reference standard of physician perception of crowding. Results We gathered data for 144 (63.9%) of 168 study intervals. ED census correlated strongly with crowding (AUC = 0.82, 95% CI 0.76-0.89), as did ED occupancy (AUC = 0.75, 95% CI 0.66-0.83). Their performance was similar to NEDOCS (AUC = 0.80) and to the local modification of NEDOCS (AUC = 0.83). Conclusion ED occupancy as a single measure has similar predictive accuracy to complex crowding scores and is easily generalizable to diverse emergency departments. Real-time tracking of this simple indicator could be used to prompt investigation and implementation of crowding interventions. Keywords Crowding • Emergency department RésuméIntroduction L'encombrement des services d'urgence (SU) compromet les résultats pour les patients. Les mesures d'encombrement existantes sont complexes et difficiles à utiliser en temps réel. Cette étude a évalué des variables de débit unique facilement disponibles comme mesures d'encombrement. Les méthodes Pendant deux semaines dans un service d'urgence tertiaire canadien, nous avons enregistré les mesures d'encombrement potentiel suivantes au cours de 168 intervalles d'étude consécutifs de deux heures : nombre total de patients dans le service d'urgence (recensement), patients dans les lits, patients dans les salles d'attente, patients dans les zones * Robin Clouston

Objective There is little consensus on the best way to measure emergency department (ED) crowding. We have previously developed a consensus-based measure, the International Crowding Measure in Emergency Departments. We aimed to externally validate a short form of the International Crowding Measure in Emergency Department (sICMED) against emergency physician’s perceptions of crowding and danger. Methods We performed an observational validation study in seven EDs in five different countries. We recorded sICMED observations and the most senior available emergency physician’s perceptions of crowding and danger at the same time. We performed a times series regression model. Results A total of 397 measurements were analysed. The sICMED showed moderate positive correlations with emergency physician’s perceptions of crowding, r = 0.4110, P < 0.05) and safety (r = 0.4566, P < 0.05). There was considerable variation in the performance of the sICMED between different EDs. The sICMED was only slightly better than measuring occupancy or ED boarding time. Conclusion The sICMED has moderate face validity at predicting clinician’s concerns about crowding and safety, but the strength of this validity varies between different EDs and different countries.

Introduction: Crowding is associated with poor patient outcomes in emergency departments (ED). Measures of crowding are often complex and resource-intensive to score and use in real-time. We evaluated single easily obtained variables to establish the presence of crowding compared to more complex crowding scores. Methods: Serial observations of patient flow were recorded in a tertiary Canadian ED. Single variables were evaluated including total number of patients in the ED (census), in beds, in the waiting room, in the treatment area waiting to be assessed, and total inpatient admissions. These were compared with Crowding scores (NEDOCS, EDWIN, ICMED, three regional hospital modifications of NEDOCS) as predictors of crowding. Predictive validity was compared to the reference standard of physician perception of crowding, using receiver operator curve analysis. Results: 144 of 169 potential events were recorded over 2 weeks. Crowding was present in 63.9% of the events. ED census (total number of patients in the ED) was strongly correlated with crowding (AUC = 0.82 with 95% CI = 0.76 - 0.89) and its performance was similar to that of NEDOCS (AUC = 0.80 with 95% CI = 0.76 - 0.90) and a more complex local modification of NEDOCS, the S-SAT (AUC = 0.83, 95% CI = 0.74 - 0.89). Conclusion: The single indicator, ED census was as predictive for the presence of crowding as more complex crowding scores. A two-stage approach to crowding intervention is proposed that first identifies crowding with a real-time ED census statistic followed by investigation of precipitating and modifiable factors. Real time signalling may permit more standardized and effective approaches to manage ED flow.