LO17: A comparative evaluation of ED crowding metrics and associations with patient mortality

Abstract

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...

Impact of Rural and Urban Hospital Closures on Inpatient Mortality

“…12 While shorter LOSs can indicate poor quality of care if hospitals respond to financial pressures by reducing service durations and discharge patients earlier than appropriate, longer LOSs can indicate poor quality of care or of ED crowding that can result in complications that lead to longer LOSs. Despite the recent trend of shortening LOSs, recent body of evidence favors the link between increased LOS and poor quality/mortality (Tran et al, 2018;McRae et al, 2017;Chaou et al, 2016;Pines et al, 2010;Thomas et al, 1997). Reported numerical results are all statistically significant at the 5% level except for stroke only and AMI only subgroups, which are statistically significant at the 10% level.…”