Mortality and readmission differences associated with after-hours hospital admission: A population-based cohort study in Queensland Australia.

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.

The impact of acute pneumococcal disease on health state utility values: a systematic review.

PURPOSE: Streptococcus pneumoniae infections remain a significant source of morbidity and mortality worldwide. The purpose of this review was to summarize the impact of pneumococcal disease on health state utilities (HSU) in the acute phase of illness. METHODS: We searched MEDLINE, EMBASE, EconLit, the Health Technology Assessment Database, the National Health Economic Evaluation Database, and Tufts Cost-Effectiveness Registry (up to January 2020) for primary studies. Eligible studies elicited HSU estimates using preference-based instruments for the acute phase of infection of pneumococcal syndromes including acute otitis media, pneumonia/lower respiratory tract infections, bacteremia/sepsis, and meningitis. Two reviewers independently conducted screening, data extraction and quality appraisal. RESULTS: We screened 10,178 studies, of which 26 met our inclusion criteria. Cohort sizes ranged from 8 to 2060 respondents. The most frequently studied syndrome was pneumonia (n = 17), followed by acute otitis media (n = 9), meningitis (n = 7) and bacteremia/sepsis (n = 4). Overall, each syndrome was associated with a substantial impact on HSU. Bacteremia/sepsis (range: - 0.331 to 0.992) and meningitis (range: - 0.330 to 0.977) were generally associated with the lowest HSU, followed by pneumonia (range: - 0.054 to 0.998) and acute otitis media (range: 0.064 to 0.970). HSU estimates varied considerably by treatment setting, elicitation method and type of respondent. The only study to compare pneumococcal infections to non-pneumococcal infections in the same population revealed significantly lower HSU estimates among pneumococcal infections. CONCLUSIONS: Pneumococcal syndromes are associated with decreased HSU estimates. Given the considerable heterogeneity in methods and source populations as well as study quality, care should be taken to select the most appropriate estimates.

After-hours emergency department care: Does time or day of arrival affect survival?

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.

The National Emergency Access Target (NEAT) and the 4-hour rule: time to review the target.

OBJECTIVE: We explored the relationship between the National Emergency Access Target (NEAT) compliance rate, defined as the proportion of patients admitted or discharged from emergency departments (EDs) within 4 hours of presentation, and the risk-adjusted in-hospital mortality of patients admitted to hospital acutely from EDs. DESIGN, SETTING AND PARTICIPANTS: Retrospective observational study of all de-identified episodes of care involving patients who presented acutely to the EDs of 59 Australian hospitals between 1 July 2010 and 30 June 2014. MAIN OUTCOME MEASURE: The relationship between the risk-adjusted mortality of inpatients admitted acutely from EDs (the emergency hospital standardised mortality ratio [eHSMR]: the ratio of the numbers of observed to expected deaths) and NEAT compliance rates for all presenting patients (total NEAT) and admitted patients (admitted NEAT). RESULTS: ED and inpatient data were aggregated for 12.5 million ED episodes of care and 11.6 million inpatient episodes of care. A highly significant (P < 0.001) linear, inverse relationship between eHSMR and each of total and admitted NEAT compliance rates was found; eHSMR declined to a nadir of 73 as total and admitted NEAT compliance rates rose to about 83% and 65% respectively. Sensitivity analyses found no confounding by the inclusion of palliative care and/or short-stay patients. CONCLUSION: As NEAT compliance rates increased, in-hospital mortality of emergency admissions declined, although this direct inverse relationship is lost once total and admitted NEAT compliance rates exceed certain levels. This inverse association between NEAT compliance rates and in-hospital mortality should be considered when formulating targets for access to emergency care.

Early discharge and its effect on ED length of stay and access block.

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.

Impact of influenza across 27 public emergency departments in Australia: a 5-year descriptive study.

OBJECTIVE: To describe the incidence, characteristics and outcomes of patients with influenza-like symptoms presenting to 27 public hospital emergency departments (EDs) in Queensland, Australia. METHODS: A descriptive retrospective study covering 5 years (2005-9) of historical data from 27 hospital EDs was undertaken. State-wide hospital ED Information System data were analysed. Annual comparisons between influenza and non-influenza cases were made across the southern hemisphere influenza season (June-September) each year. RESULTS: Influenza-related presentations increased significantly over the 5 years from 3.4% in 2005 to 9.4% in 2009, reflecting a 276% relative increase. Differences over time regarding characteristics of patients with influenza-like symptoms, based on the influenza season, occurred for admission rate (decreased over time from 28% in 2005 to 18% in 2009), length of stay (decreased over time from a median of 210 min in 2005 to 164 min in 2009) and access block (increased over time from 33% to 41%). Also, every year there was a significantly (p<0.001) higher percentage of access block in the influenza cohort than in the non-influenza cohort. CONCLUSIONS: Although there was a large increase over time in influenza-related ED presentations, most patients were discharged home from the ED. Special consideration of health service delivery management (eg, establishing an 'influenza clinic border protection and public rollout of vaccination, beginning with those most at risk') for this group of patients is warranted but requires evaluation. These results may inform planning for service delivery models during the influenza season.

Predicting emergency department admissions.

OBJECTIVE: To develop and validate models to predict emergency department (ED) presentations and hospital admissions for time and day of the year. METHODS: Initial model development and validation was based on 5 years of historical data from two dissimilar hospitals, followed by subsequent validation on 27 hospitals representing 95% of the ED presentations across the state. Forecast accuracy was assessed using the mean average percentage error (MAPE) between forecasts and observed data. The study also determined a daily sample size threshold for forecasting subgroups within the data. RESULTS: Presentations to the ED and subsequent admissions to hospital beds are not random and can be predicted. Forecast accuracy worsened as the forecast time intervals became smaller: when forecasting monthly admissions, the best MAPE was approximately 2%, for daily admissions, 11%; for 4-hourly admissions, 38%; and for hourly admissions, 50%. Presentations were more easily forecast than admissions (daily MAPE ∼7%). When validating accuracy at additional hospitals, forecasts for urban facilities were generally more accurate than regional forecasts (accuracy is related to sample size). Subgroups within the data with more than 10 admissions or presentations per day had forecast errors statistically similar to the entire dataset. The study also included a software implementation of the models, resulting in a data dashboard for bed managers. CONCLUSIONS: Valid ED prediction tools can be generated from access to de-identified historic data, which may be used to assist elective surgery scheduling and bed management. The paper provides forecasting performance levels to guide similar studies.