Dataset Information

Mobile Stroke Unit in the UK Healthcare System: Avoidance of Unnecessary Accident and Emergency Admissions.

ABSTRACT: BACKGROUND:Acute stroke patients are usually transported to the nearest hospital regardless of their required level of care. This can lead to increased pressure on emergency departments and treatment delay. OBJECTIVE:The aim of the study was to explore the benefit of a mobile stroke unit (MSU) in the UK National Health Service (NHS) for reduction of hospital admissions. METHODS:Prospective cohort audit observation with dispatch of the MSU in the East of England Ambulance Service area in Southend-on-Sea was conducted. Emergency patients categorized as code stroke and headache were included from June 5, 2018, to December 18, 2018. Rate of avoided admission to the accident and emergency (A&E) department, rate of admission directly to target ward, and stroke management metrics were assessed. RESULTS:In 116 MSU-treated patients, the following diagnoses were made: acute stroke, n = 33 (28.4%); transient ischaemic attacks, n = 13 (11.2%); stroke mimics, n = 32 (27.6%); and other conditions, n = 38 (32.8%). Pre-hospital thrombolysis was administered to 8 of 28 (28.6%) ischaemic stroke patients. Pre-hospital diagnosis avoided hospital admission for 29 (25.0%) patients. As hospital treatment was indicated, 35 (30.2%) patients were directly triaged to the stroke unit, 1 patient (0.9%) even directly to the catheter laboratory. Thus, only 50 (43.1%) patients required transfer to the A&E department. Moreover, the MSU enabled thrombolysis with a median dispatch-to-needle time of 42 min (interquartile range, 40-60). CONCLUSION:This first deployment of an MSU in the UK NHS demonstrated improved triage decision-making for or against hospital admission and admission to the appropriate target ward, thereby reducing pressure on strained A&E departments.

SUBMITTER: Grunwald IQ

PROVIDER: S-EPMC7592923 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Mobile Stroke Unit in the UK Healthcare System: Avoidance of Unnecessary Accident and Emergency Admissions.

Grunwald Iris Q IQ Phillips Daniel J DJ Sexby David D Wagner Viola V Lesmeister Martin M Bachhuber Monika M Mathur Shrey S Guyler Paul P Fisher James J Perera Saman S Helwig Stefan A SA Schottek Andrea A Ewart Ian I Menon Nisha N Inam Ul Haq Muhammad M Grün Daniel D Merzou Fatma F Howard Caroline C Mapplebeck Sarah S Dommett David D Alam Sajid S Chakrabarti Annie A Gerry Stephen S Wiltshire Chris C Bailey Marcus M Bertsch Thomas T Foster Theresa T Davis Tom T Reith Wolfgang W Fassbender Klaus K Walter Silke S

Cerebrovascular diseases (Basel, Switzerland) 20200826 4

<h4>Background</h4>Acute stroke patients are usually transported to the nearest hospital regardless of their required level of care. This can lead to increased pressure on emergency departments and treatment delay.<h4>Objective</h4>The aim of the study was to explore the benefit of a mobile stroke unit (MSU) in the UK National Health Service (NHS) for reduction of hospital admissions.<h4>Methods</h4>Prospective cohort audit observation with dispatch of the MSU in the East of England Ambulance Se ...[more]

PMID: 32846413

Similar Datasets

Project description:BackgroundThere is limited understanding of the drivers of increasing infant accident and emergency (A&E) attendances and emergency hospital admissions across England. We examine variations in use of emergency hospital services among infants by local areas in England and investigate the extent to which infant and socio-economic factors explain these variations.MethodsBirth cohort study using linked administrative Hospital Episode Statistics data in England. Singleton live births between 1-April-2012 and 31-March-2019 were followed up for 1 year; from 1-April-2013 (from the discharge date of their birth admission) until their first birthday, death or 31-March-2019. Mixed effects negative binomial models were used to calculate incidence rate ratios for A&E attendances and emergency admissions and mixed effects logistic regression models estimated odds ratio of conversion (the proportion of infants subsequently admitted after attending A&E). Models were adjusted for individual-level factors and included a random effect for local authority (LA).ResultsThe cohort comprised 3,665,414 births in 150 English LAs. Rates of A&E attendances and emergency admissions were highest amongst: infants born < 32 weeks gestation; with presence of congenital anomaly; and to mothers < 20-years-old. Area-level deprivation was positively associated with A&E attendance rates, but not associated with conversion probability. A&E attendance rates were highest in the North East (916 per 1000 child-years, 95%CI: 911 to 921) and London (876 per 1000, 95%CI: 874 to 879), yet London had the lowest emergency admission rates (232 per 1000, 95%CI: 231 to 234) and conversion probability (25% vs 39% in South West). Adjusting for individual-level factors did not significantly affect variability in A&E attendance and emergency admission rates by local authority.ConclusionsDrivers of A&E attendances and emergency admissions include individual-level factors such being born premature, with congenital anomaly and from socio-economically disadvantaged young parent families. Support for such vulnerable infants and families should be provided alongside preventative health care in primary and community care settings. The impact of these services requires further investigation. Substantial geographical variations in rates were not explained by individual-level factors. This suggests more detailed understanding of local and underlying service-level factors would provide targets for further research on mechanisms and policy priority.

Project description:BackgroundEmergency departments (EDs) see a rising number of patients, but only a small fraction of ED patients need immediate intensive care. The characteristics of these patients are mostly unknown and there is reason to believe that there are large inter-hospital differences in thresholds for intensive care admissions from the ED. The purpose of this study was to give a nationwide overview of ED admissions directly to intensive care units.MethodsWe used the Swedish Intensive care Registry to identify all patients admitted to intensive care from the ED (January 1, 2013 until June 7, 2018). The primary outcome was discharge diagnosis after intensive care (primary ICU diagnosis code). ICU mortality and" ICU admission due to only observation" were analyzed as secondary outcomes.Results110,072 ICU admissions were included, representing 94,546 unique patients. Intoxication, trauma and neurological conditions were the most common causes for intensive care, but large variations according to age, sex and hospital type were seen. Intoxication was the most prevalent diagnosis in young adults (46.8% of admissions in 18-29 years old), whereas infectious diseases predominated in the elderly (17.0% in 65-79 years old). Overall, ICU mortality was 7.2%, but varied substantially with age, sex, type of hospital and medical condition. Cardiac conditions had the highest mortality rates, reaching 32.9%. The mortality was higher in academic centers compared to rural hospitals (9.3% vs 5.0%). It was more common to be admitted to ICU for only observation in rural hospitals than in academic centers (20.1% vs 7.8%). Being admitted to ICU only for observation was most common in patients with intoxication (30.6%).ConclusionsOverall, intoxication was the most common cause for ICU admission from the ED. However, causes of ED ICU admissions differ substantially according to age, sex and hospital type. Being admitted to the ICU only for observation was most common in intoxicated patients.Trial registrationNot applicable (no interventions).

Project description:ImportanceIncreased patient acuity, decreased intensive care unit (ICU) bed availability, and a shortage of intensivist physicians have led to strained ICU capacity. The resulting increase in emergency department (ED) boarding time for patients requiring ICU-level care has been associated with worse outcomes.ObjectiveTo determine the association of a novel ED-based ICU, the Emergency Critical Care Center (EC3), with 30-day mortality and inpatient ICU admission.Design, setting, and participantsThis retrospective cohort study used electronic health records of all ED visits between September 1, 2012, and July 31, 2017, with a documented clinician encounter at a large academic medical center in the United States with approximately 75 000 adult ED visits per year. The pre-EC3 cohort included ED patients from September 2, 2012, to February 15, 2015, when the EC3 opened, and the post-EC3 cohort included ED patients from February 16, 2015, to July 31, 2017. Data analyses were conducted from March 2, 2018, to May 28, 2019.ExposuresImplementation of EC3, an ED-based ICU designed to provide rapid initiation of ICU-level care in the ED setting and seamless transition to inpatient ICUs.Main outcomes and measuresThe main outcomes were 30-day mortality among ED patients and rate of ED to ICU admission.ResultsA total of 349 310 visits from a consecutive sample of ED patients (mean [SD] age, 48.5 [19.7] years; 189 709 [54.3%] women) were examined; the pre-EC3 cohort included 168 877 visits and the post-EC3 cohort included 180 433 visits. Implementation of EC3 was associated with a statistically significant reduction in risk-adjusted 30-day mortality among all ED patients (pre-EC3, 2.13%; post-EC3, 1.83%; adjusted odds ratio, 0.85; 95% CI, 0.80-0.90; number needed to treat, 333 patient encounters; 95% CI, 256-476). The risk-adjusted rate of ED admission to ICU decreased with implementation of EC3 (pre-EC3, 3.2%; post-EC3, 2.7%; adjusted odds ratio, 0.80; 95% CI, 0.76-0.83; number needed to treat, 179 patient encounters; 95% CI, 149-217).Conclusions and relevanceImplementation of a novel ED-based ICU was associated with improved 30-day survival and reduced inpatient ICU admission. Additional research is warranted to further explore the value of this novel care delivery model in various health care systems.

Project description:ObjectivesFollowing the outbreak of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus and the subsequent global spread of the 2019 novel coronavirus disease (COVID-19), health systems and the populations who use them have faced unprecedented challenges. We aimed to measure the impact of COVID-19 on the uptake of hospital-based care at a national level.DesignThe study period (weeks ending 5 January to 28 June 2020) encompassed the pandemic announcement by the World Health Organization and the initiation of the UK lockdown. We undertook an interrupted time-series analysis to evaluate the impact of these events on hospital services at a national level and across demographics, clinical specialties and National Health Service Health Boards.SettingScotland, UK.ParticipantsPatients receiving hospital care from National Health Service Scotland.Main outcome measuresAccident and emergency (A&E) attendances, and emergency and planned hospital admissions measured using the relative change of weekly counts in 2020 to the averaged counts for equivalent weeks in 2018 and 2019.ResultsBefore the pandemic announcement, the uptake of hospital care was largely consistent with historical levels. This was followed by sharp drops in all outcomes until UK lockdown, where activity began to steadily increase. This time-period saw an average reduction of -40.7% (95% confidence interval [CI]: -47.7 to -33.7) in A&E attendances, -25.8% (95% CI: -31.1 to -20.4) in emergency hospital admissions and -60.9% (95% CI: -66.1 to -55.7) in planned hospital admissions, in comparison to the 2018-2019 averages. All subgroup trends were broadly consistent within outcomes, but with notable variations across age groups, specialties and geography.ConclusionsCOVID-19 has had a profoundly disruptive impact on hospital-based care across National Health Service Scotland. This has likely led to an adverse effect on non-COVID-19-related illnesses, increasing the possibility of potentially avoidable morbidity and mortality. Further research is required to elucidate these impacts.

Project description:OBJECTIVE:To derive and validate clinical prediction models to identify children at low risk of clinically significant intoxications for whom intensive care admission is unnecessary. DESIGN:Retrospective review of data in the National Poison Data Systems from 2011 to 2014 and Georgia Poison Center cases from July to December 2016. SETTING:United States PICUs and poison centers participating in the American Association of Poison Control Centers from 2011 to 2016. PATIENTS:Children 18 years and younger admitted to a United States PICU following an acute intoxication. INTERVENTIONS:None. MEASUREMENTS AND MAIN RESULTS:The primary study outcome was the occurrence of clinically significant intoxications defined a priori as organ system-based clinical effects that require intensive care monitoring and interventions. We analyzed 70,364 cases. Derivation (n = 42,240; 60%) and validation cohorts (n = 28,124; 40%) were randomly selected from the eligible population and had similar distributions of clinical effects and PICU interventions. PICU interventions were performed in 1,835 children (14.1%) younger than 6 years, in 374 children (15.4%) 6-12 years, and in 4,446 children (16.5%) 13 years and older. We developed highly predictive models with an area under the receiver operating characteristic curve of 0.834 (< 6 yr), 0.771 (6-12 yr), and 0.786 (?13 yr), respectively. For predicted probabilities of less than or equal to 0.10 in the validation cohorts, the negative predictive values were 95.4% (< 6 yr), 94.9% (6-12 yr), and 95.1% (? 13 yr). An additional 700 patients from the Georgia Poison Center were used to validate the model and would have reduced PICU admission by 31.4% (n = 110). CONCLUSIONS:These validated models identified children at very low risk of clinically significant intoxications for whom pediatric intensive care admission can be avoided. Application of this model using Georgia Poison Center data could have resulted in a 30% reduction in PICU admissions following intoxication.

Project description:BackgroundMobile health (mHealth) is showing increasing potential to address health outcomes in underresourced settings as smartphone coverage increases. The NeoTree is an mHealth app codeveloped in Malawi to improve the quality of newborn care at the point of admission to neonatal units. When collecting vital demographic and clinical data, this interactive platform provides clinical decision support and training for the end users (health care professionals [HCPs]), according to evidence-based national and international guidelines.ObjectiveThis study aims to examine 1 month's data collected using NeoTree in an outcome audit of babies admitted to a district-level neonatal nursery in Malawi and to demonstrate proof of concept of digital outcome audit data in this setting.MethodsUsing a phased approach over 1 month (November 21-December 19, 2016), frontline HCPs were trained and supported to use NeoTree to admit newborns. Discharge data were collected by the research team using a discharge form within NeoTree, called NeoDischarge. We conducted a descriptive analysis of the exported pseudoanonymized data and presented it to the newborn care department as a digital outcome audit.ResultsOf 191 total admissions, 134 (70.2%) admissions were completed using NeoTree, and 129 (67.5%) were exported and analyzed. Of 121 patients for whom outcome data were available, 102 (84.3%) were discharged alive. The overall case fatality rate was 93 per 1000 admitted babies. Prematurity with respiratory distress syndrome, birth asphyxia, and neonatal sepsis contributed to 25% (3/12), 58% (7/12), and 8% (1/12) of deaths, respectively. Data were more than 90% complete for all fields. Deaths may have been underreported because of phased implementation and some families of babies with imminent deaths self-discharging home. Detailed characterization of the data enabled departmental discussion of modifiable factors for quality improvement, for example, improved thermoregulation of infants.ConclusionsThis digital outcome audit demonstrates that data can be captured digitally at the bedside by HCPs in underresourced newborn facilities, and these data can contribute to a meaningful review of the quality of care, outcomes, and potential modifiable factors. Coverage may be improved during future implementation by streamlining the admission process to be solely via digital format. Our results present a new methodology for newborn audits in low-resource settings and are a proof of concept for a novel newborn data system in these settings.

Dataset Information

Mobile Stroke Unit in the UK Healthcare System: Avoidance of Unnecessary Accident and Emergency Admissions.

Publications

Mobile Stroke Unit in the UK Healthcare System: Avoidance of Unnecessary Accident and Emergency Admissions.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets