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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.


Background
Emergency department (ED) crowding occurs when resources do not match the demand for care and has been shown to increase morbidity and mortality and decrease the quality of ED care. [1][2][3][4] There is no consensus on how to measure ED crowding, but several metrics have been suggested and shown to be associated with decreased quality of care. 4 Occupancy rate (OR), that is, the number of patients in the ED divided by the number of treatment beds, is one of the most widely used measures of crowding because of its face validity and ease of use. 1,5 Because of this, it has been promoted over other more complex measures with similar ability to identify crowding. 4,6 An OR above 1.0, indicating more patients than treatment beds at a given time, is generally used as a marker of crowding. 6,7 However, such a fixed cutoff assumes that different EDs are affected similarly by a change in OR that, among other things, requires that the number of treatment beds in relation to the census of patients are comparable between EDs. These potential differences in endogenous ED factors was one of the reasons why less emphasis was put on volume and census measures at the 2014 benchmarking summit. 8 To our knowledge, differences in census to treatment bed ratios have not been investigated, and neither is there a regulated minimum ratio for the number of treatment beds in the ED. In addition, there are several different operational models for EDs, such as fast-track, 9 rapid assessment zones, 10 and vertical split flow, 11 which use ED space and treatment beds differently.

Importance
With no legislated minimum number of beds and different operational models, the comparability of crude OR as a measure of crowding between EDs is unclear. A staff-perceived crowding measure might not suffer from this limitation because it can be assumed to be relative to the normal state at each ED. Staff perception has been used as a reference for crowding in several previous studies with consistent results. [12][13][14] Our hypothesis was that baseline differences in the number of treatment beds in relation to daily census lead to differences in OR between EDs, which could make crude OR a less reliable crowding measure.

Goals of this investigation
In the present study, we aimed to investigate how the number of treatment beds differ in Swedish EDs and how this affects crowding estimated by OR compared with the staff perception of crowding.

The Bottom Line
Current emergency department crowding measures do not always reflect staff-perceived workload, partly because of differences in the numbers of beds. Looking at 37 Swedish hospitals, census ratio (current/daily census) correlated better with workload than simple occupancy rate (current patients/treatment beds).

Study design and data collection
As described previously, 15  previously shown to be a valid approximation of workload. 14 We did not collect data on scheduling models but collected data on healthcare staff to account for different scheduling models. Each ED also provided information on the annual and average daily census in the previous year (2017) and the number of treatment beds. Data were recorded on a paper report form by a senior staff member and subsequently submitted in a digital form to the study coordinator. This study was carried out in accordance with the Declaration of Helsinki. 16 This study was approved for all sites by the regional ethics review board in Linköping, Sweden (permit reference: 2018/50-31). Informed consent was waived because no identifiable personal data were collected. (very high workload) and used as a reference standard for crowding. 14 EDs were classified by their hospital status (academic, community, and rural) in Sweden, where academic centers are the only centers with tertiary, highly specialized care within neurosurgery, cardiothoracic surgery, transplantations, and advanced burn care. To assess baseline differences in treatment beds between EDs, we calculated turnover per treatment bed (TTB) for each ED by dividing the average daily census with the current number of treatment beds in the ED. We dichotomized EDs to above and below the mean TTB to investigate its impact on OR and workload. In a post hoc analysis, we hypothesized that the census ratio (CR), defined as the number of present patients divided by the daily census, may differ less between EDs because it removes the baseline differences in the number of ED beds.

Statistical analysis
Annual census is reported as a median with interquartile range (IQR).
OR and workload are reported as means with standard deviations

RESULTS
Complete data were returned from 37 EDs (51%, n = 72). Of 7 university hospitals with tertiary care, 5 (75%) reported data, and the geo- where N p = number of patients, N b = number of treatment beds, and u p = average daily census

LIMITATIONS
The response rate among the invited EDs was only 50%, which limits the generalizability of the results. We believe that this was somewhat mitigated by the diversity in geographical location and size and type of the participating EDs, making them reasonably lurepresentative of the healthcare system in Sweden. The study was conducted during a single day in the spring, and seasonal differences could therefore not be analyzed. 27 Several studies have found seasonal variation in specific populations in the ED 28,29 ; however, the importance of seasonal variation on crowding is uncertain. [30][31][32] We were unable to adjust for patient case mix at each time point, which may influence staff assessments of workload. Furthermore, the assessment of workload was made by 1 senior staff member at each ED, which may limit the generalizability to all staff. It was not possible to ask all working staff at each time point, and previous work by our group indicates that assessments by senior staff have similar predictive values as the average assessments of all staff. 14 We used workload as the reference standard for crowding in this study because of its potential to incorporate many contributing factors of crowding. When evaluating crowding operationally, any 1 measure of crowding should not be used in isolation but in conjunction with other operational metrics.

DISCUSSION
In this national cross-sectional study, we found considerable differences in baseline patient turnover, measured as TTB, between EDs, and this affects OR but not staff-assessed workload. EDs with low TTB had lower average ORs compared with EDs with high TTB. This is unsurprising because more beds are likely to be occupied at any given time in EDs with fewer beds in relation to the daily inflow of patients. However, the large variation in TTB (from 2.1 to 9.2 patients per bed per day) suggests considerable differences in organization and operations between EDs in Sweden. The impact of TTB on measured OR was unchanged when adjusting for possible confounding variables, indicating that TTB is an important factor to consider when using OR as a measure of crowding.
Because of its simplicity, OR has become a popular measure of crowding, but our findings indicate that crude OR alone may not be ideal for comparing crowding between EDs. By definition, 2 EDs are equally full at equal ORs. 6 However, a given OR may not have equal effects on the level of crowding and the quality of care at different EDs because of the different operational models or patient population differences. [9][10][11]15 Furthermore, there is evidence that the effect of crowding on patient outcome is non-linear, 1-3 with more of the negative effects at the highest crowding levels. The observed differences in TTB between EDs in our study and the non-linear relation between crowding and quality of care may explain some of the mixed results from studies using OR as a measure of crowding. 1 35 In our study, the correlation between treatment beds and census was good (r = 0.77) but not perfect, indicating variability in treatment bed ratios that CR may better account for and still be easy to use. However, further validation is needed to investigate the generalizability of our results.
Interestingly, there was no difference in average workload between EDs with low versus high TTB, which indicates that staff may account for baseline differences in ED beds when they assess workload.
Whether this is attributed to different ED operational models or patient populations was not studied. There may also be a psychological blunting effect of working in a constantly crowded ED, which may lead to lower scores of perceived workload and/or perhaps a selection of individuals who stay and work in EDs with high degrees of crowding.

CONCLUSION
Differences in the number of treatment beds in relation to the number of presenting patients differ substantially between EDs in Sweden and affect OR but not necessarily staff-assessed crowding. These differences should be accounted for when using OR as a comparator of crowding between EDs and propose the CR as a simple and readily available alternative by dividing the current census by the daily average census.

ACKNOWLEDGMENT
We acknowledge Erika Hörlin for her invaluable assistance in data collection.