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

IntroductionPublished national guidelines on chronic obstructive pulmonary disease (COPD) highlight the importance of oxygen therapy, bronchodilators, corticosteroids, and appropriate antibiotics during acute exacerbations of COPD (AECOPD). We wished to assess how the implementation of local COPD guidelines affects emergency department (ED) staff awareness, knowledge, the use of such guidelines, and patient outcomes, including treatment failure and rates of return to the ED.MethodsThis study was conducted at a tertiary hospital ED. Local COPD guidelines were developed by a quality improvement group. Guidelines were posted in the department, and educational sessions were provided for staff. We conducted a retrospective chart review and looked at 1849 patient visits before and after COPD guideline implementation. All visits with a diagnosis of COPD or AECOPD were included in the study (for a total of 130 patient visits), and data were collected using a standardized abstraction tool. For non-admitted patients, we recorded 30-day return rates and treatment failures occurring within 30 days of presenting to the ED. Pre- and post-implementation outcomes were analyzed with Fisher’s exact tests. We also assessed ED staff awareness, knowledge, and use of COPD guidelines through surveys given out before implementation, and both one and 10 months after the implementation. We calculated proportions and 95% confidence intervals (CI) for our surveys. Pre- and post-implementation survey responses were compared with Fisher's exact tests.ResultsFor ED physicians, the survey response rate was 78%, 79%, and 58% at pre-implementation, one-month follow-up, and 10-month follow-up, respectively. Prior to implementation, 14.3% (95% CI 4.1%-35.5%) were aware and 0% (0%-18.2%) reported using guidelines. One month after implementation, 90.9% (71.0%-98.7%) were aware and 81.8% (60.9%-93.3%) reported using guidelines. At 10 months, 100% (76.1%-100%) were aware and 100% (82%-100%) reported using local guidelines. Similar trends were seen among nurses and respiratory therapists. To assess actual guideline use, 130 visits were evaluated (51 visits prior to implementation and 79 post-implementation). Prior to implementation, 57% (43%-70%) received bronchodilators, systemic steroids, and antibiotics appropriately. Following guideline implementation, 57% (46%-67%) received the respective treatments (p=1.0). For patient-related outcomes, 86 non-admission patient visits were evaluated (35 visits prior to implementation and 51 post-implementation). Prior to guideline implementation, 17% (8%-33%) failed their initial AECOPD treatment, compared to 10% (4%-21%) following guideline implementation (p=0.34). Prior to guideline implementation, 23% (12%-39%) returned to the ED within 30 days in the pre-implementation period while 14% (7%-26%) returned post-implementation (p=0.39).ConclusionOur introduction of local COPD guidelines was successful at increasing self-reported awareness, knowledge, and the use of best practice guidelines among ED staff. At the 10...

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.