Differentiating properties of occupancy rate and workload to estimate crowding: A Swedish national cross‐sectional study

Abstract

Background Emergency department (ED) crowding causes increased patient morbidity and mortality. ED occupancy rate (OR; patients by treatment beds) is a common measure of crowding, but the comparability of ORs between EDs is unknown. The objective of this investigation was to investigate differences in ORs between EDs using staff‐perceived workload as reference. Methods This was a national cross‐sectional study in Sweden. EDs provided data on census, treatment beds, staffing, and workload (1–6) at 5 time points. A baseline patient turnover was calculated as the average daily census by treatment beds, denoted turnover per treatment bed (TTB), for each ED. A census ratio (CR), current by daily census, was calculated to adjust for differences in the number of treatment beds. Results Data were returned from 37 (51%) EDs. TTB varied considerably (mean = 4, standard deviation = 1.6; range, 2.1–9.2), and the OR was higher in EDs with TTB >4 compared with ≤4, 0.86 versus 0.43 (0.43; 95% confidence interval [CI], 0.27–0.59), but not workload, 2.75 versus 2.52 (0.23; 95% CI, −0.19 to 0.64). After adjusting for confounders, both TTB ( k = − 0.3; 95% CI, −0.49 to −0.14) and OR ( k = 3.4; 95% CI, 1.76–5.03) affected workload. Correlation with workload was better for CR than for OR ( r = 0.75 vs 0.60, respectively). Conclusion OR is affected by patient‐to‐treatment bed ratios that differ significantly between EDs and should be accounted for when measuring crowding. CR is not affected by baseline treatment beds and is a better comparable measure of crowding compared with OR in this national comparator study.

Emergency department crowding and mortality: an observational multicenter study in Sweden

“…Although the definition is universal, the extent of the problem and its consequences may vary between institutions, being more prominent in urban and academic EDs (25,26). A recent study by Af Ugglas et al (27) found variable association between crowding and mortality within Sweden and a previous study by our group showed considerable differences in resource availability in Sweden, specifically treatment beds and staffing in relation to patient volume (15). Our study included a mix of rural, urban and academic centers which may contribute to the lack of association between crowding and mortality.…”

“…There was also a large variation in baseline occupancy rate (Supplementary Table S2). Previous studies from our group and others (15,(28)(29)(30) have shown that crowding may indeed vary between EDs and that scores need to be adjusted to the individual site. In this study, we also found large intra-day variations at all EDs for both mSEAL scores and occupancy rate (Supplementary Tables S2, S3).…”

“…Additional analyzes were performed using the alternative exposures of occupancy rate (occupancy rate; number of present ED patients divided by treatment beds) at patient arrival. To account for baseline differences in occupancy rate between the included study sites ( 15 ) and intra-day variations ( 2 , 12 , 16 ), each crowding metric was stratified by ED and arrival hour. The exposure variable for each patient was categorized by quantile (<85%, 85–90%, 90–95, >95%) since we hypothesized that the effects of crowding on mortality are non-linear which is supported by previous studies by Af Ugglas et al ( 2 ) and McCusker et al ( 17 ).…”

Diagnosing an overcrowded emergency department from its Electronic Health Records

“…This problem persists globally 1 , 6 despite the differences between healthcare policies in different countries 7 , 8 , Sweden being no exception 6 , 9 – 11 . Previous studies showed a high workload for the main Swedish hospitals 12 , pointing out the multifaced nature of operational errors 11 , 13 , negative patient experience of high waiting times 14 , and the decreasing availability of beds followed by an increasing of patients visiting ED 6 .…”

“…This pipeline was designed to analyse a real-world case study that consisted of one year (2019) of hospital production data following patients that visited the Uppsala University Hospital ED. The Uppsala ED constituted an ideal case study because of the reported serious shortcomings and hospital overcrowding in the timespan of the data records 6 , 9 – 11 .…”