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Background: Emergency department ( ED) crowding caused by access block is an increasing public health issue and has been associated with impaired healthcare delivery, negative patient outcomes and increased staff workload. Aim To investigate the impact of opening a new ED on patient and healthcare service outcomes. Methods A 24-month time series analysis was employed using deterministically linked data from the ambulance service and three ED and hospital admission databases in Queensland, Australia. Results Total volume of ED presentations increased 18%, while local population growth increased by 3%. Healthcare service and patient outcomes at the two pre-existing hospitals did not improve. These outcomes included ambulance offload time: (Hospital A PRE: 10 min, POST: 10 min, P < 0.001; Hospital B PRE: 10 min, POST: 15 min, P < 0.001); ED length of stay: (Hospital A PRE: 242 min, POST: 246 min, P < 0.001; Hospital B PRE: 182 min, POST: 210 min, P < 0.001); and access block: (Hospital A PRE: 41%, POST: 46%, P < 0.001; Hospital B PRE: 23%, POST: 40%, P < 0.001). Time series modelling indicated that the effect was worst at the hospital furthest away from the new ED. Conclusions An additional ED within the region saw an increase in the total volume of presentations at a rate far greater than local population growth, suggesting it either provided an unmet need or a shifting of activity from one sector to another. Future studies should examine patient decision making regarding reasons for presenting to a new or pre-existing ED. There is an inherent need to take a 'whole of health service area' approach to solve crowding issues. [ABSTRACT FROM AUTHOR]

Hospital bed occupancy serves as an important indicator of healthcare system efficiency, directly impacting patient care quality and staff workload. This study delves into the efficacy of midnight census, a conventional method for assessing bed occupancy, in supporting hospital operational planning. Historically, the midnight census has been utilised to gauge bed occupancy; however, its reliability is debated due to fluctuations throughout the day. This paper presents an analysis of 5.5 years of patient flow data from one of the hospitals in Queensland, Australia, scrutinising the statistical associations between different occupancy levels, e.g., midnight, peak, average, and minimum. The findings shed light on the efficacy of the midnight census and suggest the adoption of an hourly-based occupancy rate for more accurate capacity planning and management.

Objective: To describe and compare characteristics and outcomes of patients who arrive by ambulance to the ED. We aimed to (i) compare patients with a delayed ambulance offload time (AOT) >30 min with those who were not delayed; and (ii) identify predictors of an ED length of stay (LOS) of >4 h for ambulance-arriving patients., Methods: A retrospective, multi-site cohort study was undertaken in Australia using 12 months of linked health data (September 2007-2008). Outcomes of AOT delayed and non-delayed presentations were compared. Logistic regression analysis was undertaken to identify predictors of an ED LOS of >4 h., Results: Of the 40 783 linked, analysable ambulance presentations, AOT delay of >30 min was experienced by 15%, and 63% had an ED LOS of >4 h. Patients with an AOT <30 min had better outcomes for: time to triage; ambulance time at hospital; time to see healthcare professional; proportion seen within recommended triage time frame; and ED LOS for both admitted and non-admitted patients. In-hospital mortality did not differ. Strong predictors of an ED LOS >4 h included: hospital admission, older age, triage category, and offload delay >30 min., Conclusion: Patients arriving to the ED via ambulance and offloaded within 30 min experience better outcomes than those delayed. Given that offload delay is a modifiable predictor of an ED LOS of >4 h, targeted improvements in the ED arrival process for ambulance patients might be useful., (© 2015 The Authors. Emergency Medicine Australasia published by Wiley Publishing Asia Pty Ltd on behalf of Australasian College for Emergency Medicine and Australasian Society for Emergency Medicine.)

Objectives: The study aims to investigate the effect of time of day and ED occupancy on the ability of EDs to admit or discharge patients within 4 h in accordance with the National Emergency Access Target (NEAT), and to compare this with corresponding levels of access block, the measure for ED performance before NEAT., Methods: This is a retrospective analysis of 5 years of ED data from 30 reporting public hospitals in Queensland, Australia. Relationships between these and variations in time of day and occupancy were explored using a Poisson generalised linear model. The main outcome measures are cases of NEAT non-compliance (ED length of stay >4 h for all patients [i.e. admitted and non-admitted] leaving the ED) and access block (ED length of stay >8 h for admitted patients)., Results: NEAT performance is found to be dependent on hospital size, and levels vary significantly for admitted and non-admitted patients. A higher proportion of patients breach NEAT during early mornings and low occupancy periods, a trend not observed with the previous access block metric. NEAT non-compliance is also found to rise between 13.00 hours and 17.00 hours, a period when the proportion of access block cases typically drops., Conclusions: EDs face rising levels of NEAT non-compliance at times when corresponding access block levels have traditionally not been a concern. A higher proportion of patients breach the target during periods that would intuitively not be flagged as flow bottlenecks. The findings support the need for service level analysis and new solutions to guide workflow reform and maximise NEAT compliance., (© 2013 Australasian College for Emergency Medicine and Australasian Society for Emergency Medicine.)

This paper describes a novel approach employing time based clustering of health data for visualization and analysis of patient flow. Clustering inpatient and emergency department patient episodes into hourly slots based on recorded timestamps, and then grouping them on required parameters, the technique provides a powerful tool for visualizing and analyzing interactions and interdependencies between hospital patient flow parameters. To demonstrate the efficacy of the approach, we employ time based clustering to address some typical patient flow related queries and discuss the findings.

Effecting early discharge is a widely recommended strategy for improving patient flow in acute hospitals. This paper analyses the impact of inpatient discharge timing on Emergency Department (ED) flow parameters such as access block and length of stay, while comparing this to the effect on hospital occupancy, to arrive at an understanding of a 'whole of hospital' response to discharge timing. The impact of hospital size is also investigated. The analysis reveals that, on days when the discharge peak lags the peak in inpatient admissions, hospitals of all sizes exhibit increased levels of occupancy, inpatient and ED length of stay, and access block. The findings corroborate the efficacy of early discharge initiatives and 'whole of hospital' flow improvement initiatives for addressing overcrowding and efficiency issues in hospitals.

The ability of hospital staff to get a patient to the right bed at the right time is dependent on bed occupancy, and is a key issue in all acute hospitals. This paper seeks to identify the impact of admission and discharge timing on hospital occupancy with reference to the peak in daily admissions and discharges. Patient admissions data from 23 Queensland public hospitals was classified into categories based on the relative timing of daily admission and discharge curves. We found statistically significant differences in mean and peak occupancy and patient length of stay between categories (one-way univariate ANOVA p&lt;0.0001). The results support early patient discharge initiatives to reduce hospital occupancy rates.