Overcrowded Emergency Departments Research Articles

Impact of the 24/7 nurse practitioner model on emergency department stay at a level 1 trauma center: A retrospective study.

The aim of the study was to assess the impact of 24/7 trauma nurse practitioner service model on the emergency department patient flow. Seamless transition of trauma patients through the emergency department to inpatient hospital care is crucial for coordination of care, clinical safety and positive health outcomes. A level 1 trauma centre located in Southern West Virginia, USA expanded their trauma nurse practitioner service covering the emergency department 24/7. Retrospective cohort study conducted in accordance with the Strengthening the Reporting of Observational studies in Epidemiology guidelines. Patients admitted to the trauma centre between March 2019 and February 2020 were divided into two groups: trauma patients managed by trauma nurse practitioners versus the hospitalist service. The hospital service group was chosen as the comparator group because any admission prior to night coverage by the trauma nurse practitioners were managed by the hospitalist service. The emergency department length of stay was significantly lower in trauma nurse practitioners' patients by an average of 300min (772.25±831.91 vs. 471.44±336.65, p=<.001). Similarly, time to place emergency department discharge order was shorter by 49min (277.76±159.69 vs. 228.27±116.04, p=.001) for this group. Moreover, trauma nurse practitioners on an average placed one less consultation (1.06±0.23 vs. 1.46±0.74, p<.001). The patient care provided by trauma nurse practitioners aided in the reduction of strain felt by their emergency department. They were able to help facilitate patient flow thus lessening the pressure of boarding in an overcrowded emergency department. The study institution hopes to sustain the current service model and continue to review outcomes and processes managed by trauma nurse practitioners to ensure consistency and quality. Similar trauma centres should evaluate the structure of their trauma service that includes the role of trauma nurse practitioner service and work towards allowing them to manage patient care from the emergency department 24/7.

Development and Validation of the Acute PNeumonia Early Assessment Score for Safely Discharging Low-Risk SARS-CoV-2-Infected Patients from the Emergency Department.

A continuous demand for assistance and an overcrowded emergency department (ED) require early and safe discharge of low-risk severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-infected patients. We developed (n = 128) and validated (n = 330) the acute PNeumonia early assessment (aPNea) score in a tertiary hospital and preliminarily tested the score on an external secondary hospital (n = 97). The score’s performance was compared to that of the National Early Warning Score 2 (NEWS2). The composite outcome of either death or oral intubation within 30 days from admission occurred in 101 and 28 patients in the two hospitals, respectively. The area under the receiver operating characteristic (AUROC) curve of the aPNea model was 0.86 (95% confidence interval (CI), 0.78–0.93) and 0.79 (95% CI, 0.73–0.89) for the development and validation cohorts, respectively. The aPNea score discriminated low-risk patients better than NEWS2 at a 10% outcome probability, corresponding to five cut-off points and one cut-off point, respectively. aPNea’s cut-off reduced the number of unnecessary hospitalizations without missing outcomes by 27% (95% CI, 9–41) in the validation cohort. NEWS2 was not significant. In the external cohort, aPNea’s cut-off had 93% sensitivity (95% CI, 83–102) and a 94% negative predictive value (95% CI, 87–102). In conclusion, the aPNea score appears to be appropriate for discharging low-risk SARS-CoV-2-infected patients from the ED.

Assessment of Emergency Department Satisfaction Level in Saudi Arabia General Hospital.

Background and objectives:An overcrowded emergency department (ED) cannot meet the patients’ growing demand. This situation harms employees’ performance and, alternatively, causes anxiety and dissatisfaction among patients since the quality of healthcare outcomes fall below their expectations. This study aimed at improving and validating a scale for assessing patient satisfaction in the ED.Methods:In this study, 134 participants from Wadi Al-Dawasir General Hospital were enrolled using a convenient sampling technique. A cross-sectional survey was conducted using 5-point Likert scales.Results:All tested hypotheses showed statistical significance (P < 0.05). Our results show that male employees were more satisfied compared with their female counterparts. Furthermore, Saudi employees were more satisfied with the health services than non-Saudi ones.Conclusion:The findings of this study brought to the fore that patients and their families were satisfied with the healthcare services and their quality. This means better service delivery played a crucial role in enhancing satisfaction levels. Nevertheless, this study also highlights that overcrowding is a significant problem for healthcare organizations. Wadi al-Dawasir General Hospital's ED should continually improve its quality to meet the growing needs of its clients.

Improving lung disease detection by joint learning with COVID-19 radiography database

Diagnostic chest radiography is one of the most common imaging tests performed in medical practice. A radiology workflow goal is to detect, diagnose, and manage diseases using chest radiography in an automated, timely, and accurate manner. Radiography data have proved very effective for assessing COVID-19 patients, particularly for treating overcrowded emergency departments and hospitals. The use of Deep Learning (DL) methods in Artificial Intelligence (AI) has become dominant in detecting diseases via chest X-rays. This study utilized the COVID-19 Radiographic Database and the National Institutes of Health (NIH) Chest-Xray to study pre-training fine-tuning of the DL model on chest radiographic images. We investigate the robust network architecture in detail: DenseNet-121, in this dataset dual technique to improve insight into the different methods and their application to chest X-ray classification. Consequently, this dual dataset technique is able to provide better detection results for each cluster of lung diseases. AUC results obtained using DenseNet-121 reached an average of 82.16 percent, with the highest AUC reaching 99.99% in the cluster containing Viral Pneumonia lung disease.

Patient perception regarding privacy and confidentiality: A study from the emergency department of a tertiary care hospital in Karachi, Pakistan.

Background and Objective:Maintaining privacy and ensuring confidentiality with patients is paramount to developing an effective patient-provider relationship. This is often challenging in over-crowded Emergency Departments (EDs). This survey was designed to explore patients’ perceptions on maintenance of privacy and confidentiality and their subsequent interactions with providers in a busy tertiary care hospital in Karachi.Methods:Trained nursing staff conducted structured interviews with 571 patients who presented to The Indus Hospital (TIH) ED from January to December 2020. All patients were 14 years of age or older, could speak and understand Urdu, and provide informed consent. Patients were asked about their perceptions of privacy and confidentiality in the ED and whether this affected their interactions with providers.Results:Respondents were primarily men (64%) under the age of 45 (62%) presenting for the first time (49%). The majority of patients felt that privacy and confidentiality were maintained, however 10% of patients reported that they had rejected examination due to privacy concerns and 15% of patients reported that they had changed or omitted information provided to a provider due to confidentiality concerns. There was correlation between privacy and confidentiality concerns and patient-provider interactions (p<0.0001).Conclusions:Despite the often over-crowded and busy environment of the ED, patients generally felt that privacy and confidentiality were maintained. Given the correlation between perception and behavior and the importance of an effective patient-provider relationship, particularly in the acute setting when morbidity and mortality is high, initiatives that focus on maintaining privacy and confidentiality should be pursued.

Predicting outcomes after trauma: Prognostic model development based on admission features through machine learning.

In an overcrowded emergency department (ED), trauma surgeons and emergency physicians need an accurate prognostic predictor for critical decision-making involving patients with severe trauma. We aimed to develope a machine learning-based early prognostic model based on admission features and initial ED management.We only recruited patients with severe trauma (defined as an injury severity score >15) as the study cohort and excluded children (defined as patients <16 years old) from a 4-years database (Chi-Mei Medical Center, from January 2015, to December 2018) recording the clinical features of all admitted trauma patients. We considered only patient features that could be determined within the first 2 hours after arrival to the ED. These variables included Glasgow Coma Scale (GCS) score; heart rate; respiratory rate; mean arterial pressure (MAP); prehospital cardiac arrest; abbreviated injury scales (AIS) of head and neck, thorax, and abdomen; and ED interventions (tracheal intubation/tracheostomy, blood product transfusion, thoracostomy, and cardiopulmonary resuscitation). The endpoint for prognostic analyses was mortality within 7 days of admission.We divided the study cohort into the early death group (149 patients who died within 7 days of admission) and non-early death group (2083 patients who survived at >7 days of admission). The extreme Gradient Boosting (XGBoost) machine learning model provided mortality prediction with higher accuracy (94.0%), higher sensitivity (98.0%), moderate specificity (54.8%), higher positive predict value (PPV) (95.4%), and moderate negative predictive value (NPV) (74.2%).We developed a machine learning-based prognostic model that showed high accuracy, high sensitivity, and high PPV for predicting the mortality of patients with severe trauma.

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

BackgroundOver 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.ObjectiveThis 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.MethodsBed 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.ResultsThe 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).ConclusionsIt 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.

Assessment of Daily Burden and Factors for Overcrowded Emergency Department at Tertiary Care Hospital of Karachi

Background: When in emergency room there is no enough area left to serve or to admit the subsequent sick patients who may require urgent attention and observation the setting is called as the overcrowded emergency room. Due to overcrowded emergency department the quality of services provided by the staff and doctors is compromised ultimately patients with severe diseases are ignored and this may be one of the causes for causalities.&#x0D; Objective: To assess the daily burden and factors responsible for overcrowding at emergency department of tertiary care hospital of Karachi.&#x0D; Methodology: It was a cross sectional study conducted at tertiary care hospital of Karachi from October 2020 to January 2021. Data of patients coming to adult emergency department of either gender were collected. Patients age &lt;14 were excluded as these were referred to pediatric emergency department. Data collection was done according to Canadian emergency department triage and acuity scale (CTAS).&#x0D; Results: Total number (N) of patients who visited emergency department in study duration was 13434. The mean number of patients who visited ED was 141±13during our study duration. There was no any significant difference in presenting complaint. Delay in investigations was found to be a reason of prolong stay and overcrowding in ED in our setting.&#x0D; Conclusion: Overcrowding of patients in our ED of our setting was a common problem. The number of staff, doctors and beds were not matching the number of patient flow in the department. The main reason of prolong stay in ED was delay in investigations.

Outcomes of arrest patients resuscitated in an emergency department: a prospective, observational study

In developing countries, a lack of knowledge about basic life support and overcrowded emergency departments (EDs) may cause problems related to the quality of cardiopul-monary resuscitation and postresuscitation care. We aimed to investigate which factors affect the return of spontaneous circulation (ROSC) and survival rates among out-of-hospital and in-hospital arrest patients in an upper-middle income country. The study was prospectively conducted from January 2018 to April 2019. All patients resuscitated in the ED, except trauma patients, were included. The out-of-hospital cardiac arrest (OHCA) and in-hospital cardiac arrest (IHCA) groups were followed up for 30 days. The primary outcome was the 30-day-survival rate, while the secondary outcome was the ROSC rate. A total of 177 patients were included in the study (80 OHCA and 97 IHCA patients). Among the OHCA patients, ROSC was achieved at a rate of 58.8%, and a 30-day survival rate of 12.5% was observed. None of the OHCA patients underwent bystander CPR. One of the main factors affecting survival in this group was the time interval until the patient reached the ED. ROSC was achieved in 54.4% of IHCA patients, while 17.5% of them were alive at 30 days. Patients who survived 30 days were significantly younger than those who died within 30 days (56 (46–74) vs. 73 (64.2–83.7) years, respectively). In the IHCA group, patients with creatinine and potassium levels closer to normal survived for 30 days. Effective and rapid fluid-electrolyte treatments of patients with high lactate and potassium levels may improve the mortality rates of these patients. We think that a focus on improving the quality of the prehospital CPR practice in OHCA patients and increasing the rates of bystander CPR by educating the public can positively contribute to outcomes.

Abstract 13455: Predicting Emergency Department Disposition at Triage for Suspected Patients With Acute Coronary Syndrome Using Machine Learning Algorithms

Introduction: Overcrowded emergency departments (ED) and undifferentiated patients make the provision of care and resources challenging. We examined whether machine learning algorithms could identify ED patients’ disposition (hospitalization and critical care admission) using readily available objective triage data among patients with symptoms suggestive of acute coronary syndrome (ACS). Methods: This was a retrospective observational cohort study of adult patients who were triaged at the ED for a suspected coronary event. A total of 162 input variables (k) were extracted from the electronic health record: demographics (k=3), mode of transportation (k=1), past medical/surgical history (k=57), first ED vital signs (k=7), home medications (k=31), symptomology (k=40), and the computer generated automatic interpretation of 12-lead electrocardiogram (k=23). The primary outcomes were hospitalization and critical care admission (i.e., admission to intensive or step-down care unit). We used 10-fold stratified cross validation to evaluate the performance of five machine learning algorithms to predict the study outcomes: logistic regression, naïve Bayes, random forest, gradient boosting and artificial neural network classifiers. We determined the best model by comparing the area under the receiver operating characteristic curve (AUC) of all models. Results: Included were 1201 patients (age 64±14, 39% female; 10% Black) with a total of 956 hospitalizations, and 169 critical care admissions. The best performing machine learning classifier for the outcome of hospitalization was gradient boosting machine with an AUC of 0.85 (95% CI, 0.82–0.89), 89% sensitivity, and F-score of 0.83; random forest classifier performed the best for the outcome of critical care admission with an AUC of 0.73 (95% CI, 0.70–0.77), 76% sensitivity, and F-score of 0.56. Conclusion: Predictive machine learning algorithms demonstrate excellent to good discriminative power to predict hospitalization and critical care admission, respectively. Administrators and clinicians could benefit from machine learning approaches to predict hospitalization and critical care admission, to optimize and allocate scarce ED and hospital resources and provide optimal care.

Prisoners seeking healthcare in emergency department.

To evaluate inmate referrals to emergency department of a tertiary healthcare facility in terms of demographical and clinical characteristics as well as their impact on the department. The retrospective cross-sectional study was conducted at Ankara Numune Training and Research Hospital, Ankara, Turkey and comprised data of incarcerated patients who were brought to the emergency department from January 01, 2010, to December 31, 2012. Demographical characteristics, consultations, duration of hospitalisation, recurrent admissions, disposal and mortality rates were noted. The referrals were grouped as surgical conditions, medical disorders, Eye, Ear, Nose, Throat problems, injury and psychiatric disorders. The groups were then subdivided according to diagnosis. SPSS 22 was used for data analysis. Of the 856 patients, 804(93.4%) were men and 52(6.1%) were women. The overall mean age was 37.54±14.81 years (range: 15-83 years). The number of patients was the highest in the medical group 363(42.4%) and the lowest in the Eye, Ear, Nose, Throat group 56(6.5%). Mean age of the surgical group was significantly lower than the medical group (p<0.001) but significantly higher than that of the trauma group (p=0.001). Functional emergency response units, strict emergency triage of inmates and their rapid care and management in jails can help avoid referring these patients to already overcrowded emergency departments.

Evaluation of Patient Flow and Waiting Time for the First Physician Visit in an Overcrowded Emergency Department

Background: The velocity of providing services in health centers is crucial to reduce mortality and adverse outcomes. Objectives: The present study aimed at determining the waiting time from entering the emergency department (ED) up to physician visiting based on congestion in the triple shift at Shahid Mohammadi Hospital in Bandar Abbas, Iran. Methods: The current cross-sectional, analytical study was conducted in 2019 on 1285 subjects selected from three shifts. The data collection tools included demographic data and standard triage forms, as well as a timetable with a stopwatch. The time between patient arrival and physician visit was recorded. SPSS software version 21 was employed to analyze the data. Results: The highest percentage of patients, 65.1% (n = 837), was non-traumatic, 38.98% (n = 501) referred during the evening shift, and 47.54% (n = 611) were related to the triage level 3. The maximum average waiting time from the beginning to the end of the triage was 4.46, and up to the physician, the visit was 12.8 minutes. Waiting time from entering ED up to physician first visit in terms of gender, refer to ED, and cause of referral statistically divulged a significant difference (P &lt; 0.05). Estimation of the maximum congestion in the department was from 16:00 to 20:00, which showed a significant difference with other day times (P &lt; 0.05). Conclusions: The average waiting time for patients was higher than the global standard. The interventions based on the maximum congestion in ED can be effective in reducing patient waiting time.

Harnessing the Power of Hospitalists in Operational Disaster Planning: COVID-19.

Hospitalists are well poised to serve in key leadership roles and in frontline care in particular when facing a pandemic such as the SARS-CoV-2 (COVID-19) infection. Much of the disaster planning in hospitals around the country addresses overcrowded emergency departments and decompressing these locations; however, in the case of COVID-19, intensive care units, emergency departments, and medical wards ran the risk of being overwhelmed by a large influx of patients needing high-level medical care. In a matter of days, our Division of Hospital Medicine, in partnership with our hospital, health system, and academic institution, was able to modify and deploy existing disaster plans to quickly care for an influx of medically complex patients. We describe a scaled approach to managing hospitalist clinical operations during the COVID-19 pandemic.

Reducing ultrasound in diagnosing deep vein thrombosis by using clinical scores and D-dimer testing

Background: To reduce unnecessary venous ultrasound examination in cases suspected to have deep venous thrombosis (DVT) in emergency department by using D dimer and wells score. venous duplex is widely used to diagnose DVT increasing burden on ultrasound in overcrowded emergency department. Authors can decrease this burden by using clinical probability scores and D dimer.Methods: This is prospective study done on 50 consecutive patients suspected to have DVT represented to emergency department of Menoufia University Hospital during the period from June 2018 to June 2019. Full history, physical examination, assessment of clinical probability score, d dimer level and results of venous duplex collection.Results: According to wells score, the majority of cases diagnosed as DVT were of high probability group 13(68.4%), 5 patients with moderate probability and only one patient with low probability was diagnosed as DVT. The mean of D dimer level in cases diagnosed as DVT is (4173.6±2173.1) and in cases without DVT is (927.4±1064.6). Using wells score and D dimer together, sensitivity is 100%, Specificity is 94%. PPV is 90%, and NPV is 100% in predicting DVT. All cases with negative d dimer and low risk probability do not have DVT.Conclusions: Based on this result, using wells score and d dimer level in early work up of patients suspected to have DVT will decrease overusing and cost of venous duplex.

Assessment of Clinical and Investigations Probabilities for Pulmonary Embolism

Background: pulmonary embolism (PE) is one of the most frequent diseases that could be missed in overcrowded emergency departments. Early and accurate diagnosis could decrease the mortality rate and this standard algorithm should be defined. Aim: The aim of this study was to evaluate the role of estimating clinical probability of pulmonary embolism and to reveal which a combination of presented clinical data that is more predictable to PE. Patients and Methods: The study included 50 patients with suspected pulmonary embolism, admitted to El Hussein and Sayed Galal University hospitals. Results: according clinical probability of patients 90% of patients from 50 cases were approved positive by CT pulmonary angio. In the current study we found that sensitivity accuracy of clinical probability in diagnosing pulmonary embolism in comparison to CT pulmonary angio was 90%. Conclusion: clinical probability of pulmonary embolism was efficient like other diagnostic methods, so biochemical parameters, clinical findings, and scoring systems, when used altogether, can contribute to the diagnosis of PE.

Assessment of Clinical and Investigations Probabilities for Pulmonary Embolism

Background: pulmonary embolism (PE) is one of the most frequent diseases that could be missed in overcrowded emergency departments. Early and accurate diagnosis could decrease the mortality rate and this standard algorithm should be defined. Aim: The aim of this study was to evaluate the role of estimating clinical probability of pulmonary embolism and to reveal which a combination of presented clinical data that is more predictable to PE. Patients and Methods: The study included 50 patients with suspected pulmonary embolism, admitted to El Hussein and Sayed Galal University hospitals. Results: according clinical probability of patients 90% of patients from 50 cases were approved positive by CT pulmonary angio. In the current study we found that sensitivity accuracy of clinical probability in diagnosing pulmonary embolism in comparison to CT pulmonary angio was 90%. Conclusion: clinical probability of pulmonary embolism was efficient like other diagnostic methods, so biochemical parameters, clinical findings, and scoring systems, when used altogether, can contribute to the diagnosis of PE.

Arcadia Medical Center: Emergency Department Crowding (A)

Arcadia Medical Center's newly appointed medical director of operations for the department of emergency medicine has noticed a gradual increase in the length of time it takes for patients to be seen and their care completed. He frequently examines patients who are waiting in their stretchers in hallways in an effort to expedite patient care. It is becoming increasingly difficult to maneuver through the overcrowded emergency department to perform routine patient care activities. Having considered how to improve the crowding issue, the medical director of operations has created a team to analyze the situation and make recommendations to implement change. Excerpt UVA-OM-1468 Rev. Oct. 21, 2019 Arcadia Medical Center: Emergency Department Crowding (A) Early for the meeting, Dr. Marc Schuler sat in a conference room at Arcadia Medical Center (AMC) and stared at a thick file containing his recent notes about the current state of the emergency department (ED). Schuler was AMC's newly appointed medical director of operations for the department of emergency medicine. During the 10 years he had been working at AMC, Schuler had noticed a gradual increase in the length of time it took to see patients and complete their care. He frequently examined patients waiting in their stretchers in hallways in an effort to expedite patient care. The overcrowded ED at AMC looked more like an early–20th century infirmary than a state-of-the-art ED. It was becoming increasingly difficult to maneuver through the ED to perform routine patient-care activities. This was not how he had envisioned practicing medicine in 2008. After having considered how to improve the crowding issue, Schuler had decided his first task was to create a team. Given the complex nature of implementing change in the department, he would need the help of the nursing staff and the support of the hospital administration and the academic chair of the emergency medicine department. Cindy Locker, the ED nurse manager, had observed her nursing staff becoming increasingly stressed and overwhelmed by the patient volume, the lack of communication in the ED, and the amount of time patients waited to receive care. When Schuler had asked Locker to lead the team, she had agreed. Schuler and Locker had already selected five people for the ED crowding team. Schuler still needed to pick another physician, and Locker had to choose another nurse. They had also contacted the quality improvement (QI) office to ask for a staff member to assist in the technical aspects of the data analysis. . . .

Emergency nurses’ Evidence-Based Practice attitudes, self-efficacy, knowledge, skills and behaviors before an educational intervention – Baseline of a Randomized Controlled Trial

BackgroundEmergency nurses are expected to follow recent research findings and ensure that their practice is based on evidence. Yet, during busy work-shifts in overcrowded emergency departments, evidence-based practice (EBP) is not always implemented. AimTo describe emergency nurses’ EBP attitudes, self-efficacy, knowledge, skills and behaviours at the baseline of a RCT. MethodsA baseline study as a part of a randomised, single-blind controlled multicentre intervention trial (RCT). The study population consisted of 300 emergency nurses employed in two university hospitals’ emergency departments in Finland. The total sample size (n = 80) was estimated using a simulation approach. The data were collected from voluntary emergency nurses by using four structured questionnaires. FindingsEmergency nurses’ EBP attitudes, self-efficacy, knowledge and skills were above the average level, and their EBP behaviour was below the average level when measured with the other of the two sum variables measuring behaviour. A significant correlation was found between age, years since graduation, the length of working experience in health care and EBP attitudes, behaviour, knowledge and self-efficacy. Younger emergency nurses who had recently graduated and had shorter work experience showed more positive attitudes towards EBP than older counterparts. No correlation was found between gender, educational background or prior exposure to EBP and EBP attitudes, self-efficacy, knowledge, skills or behaviours. ConclusionsThis study showed that to promote emergency nurses’ use of EBP in future, tailored educational interventions on EBP are needed. This would ensure implementation of EBP in daily clinical practice for quality and effective patient care.

Patient experience in a surgical assessment unit following a closed-loop audit using a Kaizen Lean system.

The Acute Surgical Assessment Unit (ASAU) in University Hospital Limerick (UHL) has provided a solution to overcrowding and long waiting times since centralization of emergency services to UHL. Recognising the importance of its evolving role, the ASAU in UHL has recently undergone a major revamp to ease the pressure in the overcrowded emergency department (ED). This included a new purpose state-of-the-art-built unit and equipment, improved staffing levels and use of a Kaizen Lean system to identify key area of delays and inefficiency with subsequent implementation of suggestions derived after Kaizen Lean. Patient satisfaction reporting was analysed in the older building and subsequently in the new site post Kaizen Lean to complete a closed-loop audit. The aim of the study was to examine patient satisfaction response rates with the ASAU prior to and following Kaizen Lean implementation and transfer to a new purpose-built unit. The survey comprised of two phases. In phase 1, 100 questionnaires were distributed to patients in ASAU (older building, pre-Kaizen Lean). In phase 2, the survey was repeated (100 patients) after Kaizen Lean implementation and transfer to the new state-of-the-art unit. Questionnaire design was based on WHO strategy on responsiveness measurement guidelines and was tailored to a population of surgical patients. Results were analysed using IBM SPSS, version 21 and Excel 2013. Two hundred questionnaires were analysed. Implementation of suggestions derived post Kaizen Lean translated into improved resource usage with reduced waiting time to be seen by nurses and doctors. Patients had better awareness about their treatment plan and planned next steps in the management. Satisfaction across different domains, perceptions of privacy and overall treatment were also improved. A Kaizen Lean approach improved the transition of an ASAU to a new purpose-built unit and further improved overall patient satisfaction with services of the ASAU.

Freestanding Emergency Department Entry and Market-level Spending on Emergency Care.

BackgroundFreestanding emergency departments (FrEDs) could reduce wait times in overcrowded emergency departments (EDs), but they might also increase usage and overall spending for emergency care. We investigate the relationship between the number of FrEDs entering a local market and overall spending on emergency care.MethodsWe accessed data from Arizona, Florida, North Carolina, and Texas in Blue Cross Blue Shield Axis; a limited data set of deidentified insurance data claims that we linked to Public Use Microdata Area (PUMA) data from the American Community Survey; and lists of licensed FrEDs from state agencies. Regression analysis was used to estimate the association between changes in the number of FrEDs in 495 PUMAs and total spending on emergency care, out‐of‐pocket spending, utilization, and price per visit from January 2013 to December 2017. Final estimates came from a PUMA‐level fixed‐effects model, with controls for state, quarter, and PUMA‐level demographics.ResultsEntry of an additional FrED in a PUMA was associated with a 3.6 percentage point (pp; CI = 2.4 to 4.9) increase in emergency provider reimbursement per insured beneficiary in Texas, Florida, and North Carolina. There was no change in spending (2.5 pp; CI = −8.2 to 3.1) associated with a FrED's entry in Arizona. Entry of an additional FrED was associated with a 0.18 (CI = 0.12 to 0.23) increase in the number of emergency care visits per 100 enrollees in Texas, Florida, and Arizona. In contrast, entry of another FrED was not associated with a change in utilization (−0.03; CI = −0.09 to 0.02) in North Carolina. Estimated out‐of‐pocket payments for emergency care increased 3.6 pp (CI = 2.5 to 4.8) with the entry of a FrED in Texas, Florida, and Arizona, but declined by 15.3 pp (CI = −26.8 to −3.7) in North Carolina.ConclusionsRather than functioning as substitutes for hospital‐based EDs, FrEDs have increased local market spending on emergency care in three of four states’ markets where they have entered. State policy makers and researchers should carefully track spending and utilization of emergency care as FrEDs disseminate to better understand their potential health benefits and cost implications for patients.