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Background and Aims: Policy makers and health system managers are seeking evidence on the risks involved for patients associated with after‐hours care. This study of approximately 1 million patients who were admitted to the 25 largest public hospitals in Queensland Australia sought to quantify mortality and readmission differences associated with after‐hours hospital admission. Methods: Logistic regression was used to assess whether there were any differences in mortality and readmissions based on the time inpatients were admitted to hospital (after‐hours versus within hours). Patient and staffing data, including the variation in physician and nursing staff numbers and seniority were included as explicit predictors within patient outcome models. Results: After adjusting for case‐mix confounding, statistically significant higher mortality was observed for patients admitted on weekends via the hospital's emergency department compared to within hours. This finding of elevated mortality risk after‐hours held true in sensitivity analyses which explored broader definitions of after‐hours care: an "Extended" definition comprising a weekend extending into Friday night and early Monday morning; and a "Twilight" definition comprising weekends and weeknights. There were no significant differences in 30‐day readmissions for emergency or elective patients admitted after‐hours. Increased mortality risks for elective patients was found to be an evening/weekend effect rather than a day‐of‐week effect. Workforce metrics that played a role in observed outcome differences within hours/after‐hours were more a time of day rather than day of week effect, i.e. staffing impacts differ more between day and night than the weekday versus weekend. Conclusion: Patients admitted after‐hours have significantly higher mortality than patients admitted within hours. This study confirms an association between mortality differences and the time patients were admitted to hospital, and identifies characteristics of patients and staffing that affect those outcomes. [ABSTRACT FROM AUTHOR]

Introduction: In Australia, aeromedical retrieval provides a vital link for rural communities with limited health services to definitive care in urban centers. Yet, there are few studies of aeromedical patient experiences and outcomes, or clear measures of the service quality provided to these patients.Study Objective: This study explores whether a previously developed quality framework could usefully be applied to existing air ambulance patient journeys (ie, the sequences of care that span multiple settings; prehospital and hospital-based pre-flight, flight transport, after-flight hospital in-patient, and disposition). The study aimed to use linked data from aeromedical, emergency department (ED), and hospital sources, and from death registries, to document and analyze patient journeys.Methods: A previously developed air ambulance quality framework was used to place patient, prehospital, and in-hospital service outcomes in relevant quality domains identified from the Institutes of Medicine (IOM) and Dr. Donabedian models. To understand the aeromedical patients' journeys, data from all relevant data sources were linked by unique patient identifiers and the outcomes of the resulting analyses were applied to the air ambulance quality framework.Results: Overall, air ambulance referral pathways could be classified into three categories: Intraregional (those retrievals which stayed within the region), Out of Region, and Into Region. Patient journeys and service outcomes varied markedly between referral pathways. Prehospital and in-hospital service variables and patient outcomes showed that the framework could be used to explore air ambulance service quality.Conclusion: The air ambulance quality framework can usefully be applied to air ambulance patient experiences and outcomes using linked data analysis. The framework can help guide prehospital and in-hospital performance reporting. With variations between regional referral pathways, this knowledge will aid with planning within the local service. The study successfully linked data from aeromedical, ED, in-hospital, and death sources and explored the aeromedical patients' journeys. [ABSTRACT FROM AUTHOR]

Preventing unplanned hospitalisations, including readmissions and re-presentations to the emergency department, is an important strategy for addressing the growing demand for hospital care. Significant successes have been reported from interventions put in place by hospitals to reduce their incidence. However, there is limited use of data-driven algorithms in hospital services to identify patients for enrolment into these intervention programs. Here we present the results of a study aiming to develop algorithms deployable at scale as part of a state government's initiative to address rehospitalizations and which fills several gaps identified in the state-of-the-art literature. To the best of our knowledge, our study involves the largest-ever sample size for developing risk models. Logistic regression, random forests and gradient boosted techniques were explored as model candidates and validated retrospectively on five years of data from 27 hospitals in Queensland, Australia. The models used a range of predictor variables sourced from state-wide Emergency Department(ED), inpatient, hospital-dispensed medications and hospital-requested pathology databases. The investigation leads to several findings: (i) the advantage of looking at a longer patient data history, (ii) ED and inpatient datasets alone can provide useful information for predicting hospitalisation risk and the addition of medications and pathology test results leads to trivial performance improvements, (iii) predicting readmissions to the hospital was slightly easier than predicting re-presentations to ED after an inpatient stay, which was slightly easier again than predicting re-presentations to ED after an EDstay, (iv) a gradient boosted approach (XGBoost) was systematically the most powerful modelling approach across various tests. [ABSTRACT FROM AUTHOR]

Objective: To determine whether after‐hours presentation to EDs is associated with differences in 7‐day and 30‐day mortality. The influence of patient case‐mix and workforce staffing differences are also explored. Methods: We conducted a retrospective observational study of 3.7 million ED episodes across 30 public hospitals in Queensland, Australia during May 2013–September 2015 using routinely collected hospital data linked to hospital staffing data and the death registry. Episodes were categorised as within/after‐hours using time of presentation. Staffing was derived from payroll records and explored by defining 11 staffing ratios. Results: Weekend presentation was slightly more associated (7‐day mortality odds ratio 1.05, 95% confidence interval [CI] 1.01–1.10) or no more associated (30‐day mortality odds ratio 1.01, 95% CI 0.98–1.03) with death than weekday presentation. When weeknights are included in the 'after‐hours' period, odds ratios are smaller, so that after‐hours presentation is no more associated (7‐day mortality odds ratio 1.03, 95% CI 0.99–1.08) or less associated (30‐day mortality odds ratio 0.95, 95% CI 0.93–0.97) with death. No significant after‐hours patient case‐mix differences were observed between weekday and weekend presentations for 7‐day mortality. In other combinations of outcome and after‐hours definition, some differences (especially measures relating to severity of presenting condition) were found. Staffing ratios were not strongly associated with any within/after‐hours differences in ED mortality. Conclusions: After‐hours presentation on the weekend to an ED is associated with higher 7‐day mortality even after controlling for case‐mix. [ABSTRACT FROM AUTHOR]

Objective Despite significant workflow reform to comply with the federally mandated National Emergency Access Target ( NEAT), Australian public hospitals continue to face significant barriers in achieving good ED patient flow. This study was undertaken to identify and analyse the impact of individual waypoints on an ED patient's journey and identify which waypoints act as bottlenecks to a hospital's 4 h ED disposition performance. Methods This study involves retrospective analysis and simulation employing 2 years of ED administrative data from a sample of two major and two large metropolitan hospitals in Queensland, Australia. The main outcome measures included waypoint wait times (Treatment Delay and Departure Delay), ED length of stay ( EDLOS) and compliance with the NEAT target, measured for all (overall NEAT) and admitted (Admitted NEAT) patients. Variations in outcome measures were analysed as functions of hour of day, day of week, departure status and triage category. Simulations identified the impact of potential ED workflow changes in the context of NEAT performance. Results Departure Delay accounted for 60 and 20% of EDLOS across large and major metropolitan hospitals, respectively. Higher gains in NEAT compliance are associated with improvements in departure delay rather than treatment delay. Simulation identified that halving Departure Delay improves Admitted NEAT by up to 22 and 4% at large and major metropolitan hospitals, respectively. Conclusions The results reinforces the need for a whole-of-hospital effort to address flow bottlenecks, and identify moving a patient from emergency to inpatient care as the critical bottleneck in ED system performance. [ABSTRACT FROM AUTHOR]

Objectives To investigate the effect of hospital occupancy levels on inpatient and ED patient flow parameters, and to simulate the impact of shifting discharge timing on occupancy levels. Methods Retrospective analysis of hospital inpatient data and ED data from 23 reporting public hospitals in Queensland, Australia, across 30 months. Relationships between outcome measures were explored through the aggregation of the historic data into 21 912 hourly intervals. Main outcome measures included admission and discharge rates, occupancy levels, length of stay for admitted and emergency patients, and the occurrence of access block. The impact of shifting discharge timing on occupancy levels was quantified using observed and simulated data. Results The study identified three stages of system performance decline, or choke points, as hospital occupancy increased. These choke points were found to be dependent on hospital size, and reflect a system change from 'business-as-usual' to 'crisis'. Effecting early discharge of patients was also found to significantly ( P < 0.001) impact overcrowding levels and improve patient flow. Conclusions Modern hospital systems have the ability to operate efficiently above an often-prescribed 85% occupancy level, with optimal levels varying across hospitals of different size. Operating over these optimal levels leads to performance deterioration defined around occupancy choke points. Understanding these choke points and designing strategies around alleviating these flow bottlenecks would improve capacity management, reduce access block and improve patient outcomes. Effecting early discharge also helps alleviate overcrowding and related stress on the system. [ABSTRACT FROM AUTHOR]