Association between delays to patient admission from the emergency department and all-cause 30-day mortality.

BACKGROUND: Delays to timely admission from emergency departments (EDs) are known to harm patients. OBJECTIVE: To assess and quantify the increased risk of death resulting from delays to inpatient admission from EDs, using Hospital Episode Statistics and Office of National Statistics data in England. METHODS: A cross-sectional, retrospective observational study was carried out of patients admitted from every type 1 (major) ED in England between April 2016 and March 2018. The primary outcome was death from all causes within 30 days of admission. Observed mortality was compared with expected mortality, as calculated using a logistic regression model to adjust for sex, age, deprivation, comorbidities, hour of day, month, previous ED attendances/emergency admissions and crowding in the department at the time of the attendance. RESULTS: Between April 2016 and March 2018, 26 738 514 people attended an ED, with 7 472 480 patients admitted relating to 5 249 891 individual patients, who constituted the study's dataset. A total of 433 962 deaths occurred within 30 days. The overall crude 30-day mortality rate was 8.71% (95% CI 8.69% to 8.74%). A statistically significant linear increase in mortality was found from 5 hours after time of arrival at the ED up to 12 hours (when accurate data collection ceased) (p<0.001). The greatest change in the 30-day standardised mortality ratio was an 8% increase, occurring in the patient cohort that waited in the ED for more than 6 to 8 hours from the time of arrival. CONCLUSIONS: Delays to hospital inpatient admission for patients in excess of 5 hours from time of arrival at the ED are associated with an increase in all-cause 30-day mortality. Between 5 and 12 hours, delays cause a predictable dose-response effect. For every 82 admitted patients whose time to inpatient bed transfer is delayed beyond 6 to 8 hours from time of arrival at the ED, there is one extra death.

Calculating the proportion of avoidable attendances at UK emergency departments: analysis of the Royal College of Emergency Medicine's Sentinel Site Survey data.

INTRODUCTION: Avoidable attendances (AAs; defined as non-urgent, self-referred patients who could be managed more effectively and efficiently by other services) have been identified as a contributor to ED crowding. Internationally, AAs have been estimated to constitute 10%-90% of ED attendances, with the UK 2013 Urgent and Emergency Care Review suggesting a figure of 40%. METHODS: This pilot study used data from the Royal College of Emergency Medicine's Sentinel Site Survey to estimate the proportion of AAs in 12 EDs across England on a standard day (20 March 2014). AAs were defined by an expert panel using questions from the survey. All patients attending the EDs were recorded with details of investigations and treatments received, and the proportion of patients meeting criteria for AA was calculated. RESULTS: Visits for 3044 patients were included. Based on these criteria, a mean of 19.4% (95% CI 18.0% to 20.8%) of attendances could be deemed avoidable. The lowest proportion of AAs reported was 10.7%, while the highest was 44.3%. Younger age was a significant predictor of AA with mean age of 38.6 years for all patients attending compared with 24.6 years for patients attending avoidably (p≤0.001). DISCUSSION: The proportion of AAs in this study was lower than many estimates in the literature, including that reported by the 2013 Urgent and Emergency Care Review. This suggests the ED is the most appropriate healthcare setting for many patients due to comprehensive investigations, treatments and capability for urgent referrals.The proportion of AAs is dependent on the defining criteria used, highlighting the need for a standardised, universal definition of an appropriate/avoidable ED attendance. This is essential to understanding how AAs contribute to the overall issue of crowding.

Evaluation of junior doctors' retention of knowledge and skills after simulation training in shockable rhythm cardiac arrest in a low-resource setting in Nepal.

Aims: To test junior doctors' abilities to retain advanced life support psychomotor skills and theoretical knowledge in management of shockable rhythm cardiac arrest. Methods: A repeated measure pre-post study design was used with 43 junior doctors, recruited after notifying them with robust method of attraction through flyers, brochures, email and phone calls. Written and performance tests, initial pre-test, immediate post-training, 30-days post-training and 60-days post-training, using simulation-based scenarios with a low-fidelity manikin were used with recording performance of ALS. Instrumentation: Resuscitation Council UK ALS algorithms and guidelines1 were used in a simulated testing environment. Results: There was a highly significant improvement in knowledge immediately after training (p < 0.00), with a net gain of marks from a mean value of 63.2% before training to 87.7% after training by 24.5% (95% CI 19.4, 29.6).There was a gradual decline of retained knowledge with time from immediate post-training over, 30-days and 60-days post-training (p < 0.00). The simulation pre-training assessments and immediate post-training assessments results were statistically significant (p < .00). The mean difference was 44.1% (95% CI 50.11, 38.10). There was a statistically significant decline of the competency with time (p < .00). Unlike for the knowledge test, the drop was significant on the 30th day (p < .00) with a mean difference of -10.5% (95% CI -13.55, -7.40). Conclusion: The training of junior doctors in shockable rhythm cardiac arrest in a low resource setting, improved knowledge and skills in the participants after training. However, retention of knowledge declined at 30 days and more significantly after 60 days and retention of skill was declined more significantly at 30 days.

'Better together: collaborative working between emergency medicine and critical care': 1 year on, is this framework still relevant?

In 2021 the Royal College of Emergency Medicine and the Faculty of Intensive Care Medicine collaborated to launch the 'Better together' framework to improve outcomes for critically unwell patients in the resuscitation room. One year on from the launch, it remains more relevant than ever.

Possible futures of acute medical care in the NHS: a multispecialty approach.

Changing population demographics and needs are resulting in a continual rise in acute medical admissions. This review draws on the observations of the NHS GIRFT programme across England. Fundamental aspects of acute medical care are not universally provided, resulting in preventable hospitalisation and over-use of emergency departments. Such aspects include care outside hospitals; appropriately sized, staffed, located and configured acute medical units; multispeciality same-day emergency care (SDEC) pathways; multidisciplinary care on wards; and readmission prevention. 'Hospital at home' services are developing, and require local evaluation. SDEC is expanding. Digital technologies make it possible to provide acute care in and across more settings. Addressing the fundamentals of acute medical care, evaluating new service opportunities, strong clinical and managerial partnerships, better data for analytics, and a multispeciality, multiprofessional approach will enable a better level of care to be achieved.

Forecasting the Requirement for Nonelective Hospital Beds in the National Health Service of the United Kingdom: Model Development Study.

BACKGROUND: Over the last decade, increasing numbers of emergency department attendances and an even greater increase in emergency admissions have placed severe strain on the bed capacity of the National Health Service (NHS) of the United Kingdom. The result has been overcrowded emergency departments with patients experiencing long wait times for admission to an appropriate hospital bed. Nevertheless, scheduling issues can still result in significant underutilization of bed capacity. Bed occupancy rates may not correlate well with bed availability. More accurate and reliable long-term prediction of bed requirements will help anticipate the future needs of a hospital's catchment population, thus resulting in greater efficiencies and better patient care. OBJECTIVE: This study aimed to evaluate widely used automated time-series forecasting techniques to predict short-term daily nonelective bed occupancy at all trusts in the NHS. These techniques were used to develop a simple yet accurate national health system-level forecasting framework that can be utilized at a low cost and by health care administrators who do not have statistical modeling expertise. METHODS: Bed occupancy models that accounted for patterns in occupancy were created for each trust in the NHS. Daily nonelective midnight trust occupancy data from April 2011 to March 2017 for 121 NHS trusts were utilized to generate these models. Forecasts were generated using the three most widely used automated forecasting techniques: exponential smoothing; Seasonal Autoregressive Integrated Moving Average; and Trigonometric, Box-Cox transform, autoregressive moving average errors, and Trend and Seasonal components. The NHS Modernisation Agency's recommended forecasting method prior to 2020 was also replicated. RESULTS: The accuracy of the models varied on the basis of the season during which occupancy was forecasted. For the summer season, percent root-mean-square error values for each model remained relatively stable across the 6 forecasted weeks. However, only the trend and seasonal components model (median error=2.45% for 6 weeks) outperformed the NHS Modernisation Agency's recommended method (median error=2.63% for 6 weeks). In contrast, during the winter season, the percent root-mean-square error values increased as we forecasted further into the future. Exponential smoothing generated the most accurate forecasts (median error=4.91% over 4 weeks), but all models outperformed the NHS Modernisation Agency's recommended method prior to 2020 (median error=8.5% over 4 weeks). CONCLUSIONS: It is possible to create automated models, similar to those recently published by the NHS, which can be used at a hospital level for a large national health care system to predict nonelective bed admissions and thus schedule elective procedures.

Variability in COVID-19 in-hospital mortality rates between national health service trusts and regions in England: A national observational study for the Getting It Right First Time Programme.

BACKGROUND: A key first step in optimising COVID-19 patient outcomes during future case-surges is to learn from the experience within individual hospitals during the early stages of the pandemic. The aim of this study was to investigate the extent of variation in COVID-19 outcomes between National Health Service (NHS) hospital trusts and regions in England using data from March-July 2020. METHODS: This was a retrospective observational study using the Hospital Episode Statistics administrative dataset. Patients aged ≥ 18 years who had a diagnosis of COVID-19 during a hospital stay in England that was completed between March 1st and July 31st, 2020 were included. In-hospital mortality was the primary outcome of interest. In secondary analysis, critical care admission, length of stay and mortality within 30 days of discharge were also investigated. Multilevel logistic regression was used to adjust for covariates. FINDINGS: There were 86,356 patients with a confirmed diagnosis of COVID-19 included in the study, of whom 22,944 (26.6%) died in hospital with COVID-19 as the primary cause of death. After adjusting for covariates, the extent of the variation in-hospital mortality rates between hospital trusts and regions was relatively modest. Trusts with the largest baseline number of beds and a greater proportion of patients admitted to critical care had the lowest in-hospital mortality rates. INTERPRETATION: There is little evidence of clustering of deaths within hospital trusts. There may be opportunities to learn from the experience of individual trusts to help prepare hospitals for future case-surges.

Better data, better planning: the College of Emergency Medicine sentinel sites project.

This article describes the College of Emergency Medicine's initial attempt to gather high quality data from its own 'sentinel sites' rather than relying on more comprehensive national data of dubious quality. Such information is essential to inform and guide the planning of urgent and emergency care services in the future.