BMC Emergency Medicine最新文献

Background: Globally, healthcare institutions have seen a marked rise in workplace violence (WPV), especially since the Covid-19 pandemic began, affecting primarily acute care and emergency departments (EDs). At the University Health Network (UHN) in Toronto, Canada, WPV incidents in EDs jumped 169% from 0.43 to 1.15 events per 1000 visits (p < 0.0001). In response, UHN launched a comprehensive, systems-based quality improvement (QI) project to ameliorate WPV. This study details the development of the project's design and key takeaways, with a focus on presenting trauma-informed strategies for addressing WPV in healthcare through the lens of health systems innovation.

Methods: Our multi-intervention QI initiative was guided by the Systems Engineering Initiative for Patient Safety (SEIPS) 3.0 framework. We utilized the SEIPS 101 tools to aid in crafting each QI intervention.

Results: Using the SEIPS 3.0 framework and SEIPS 101 tools, we gained a comprehensive understanding of organizational processes, patient experiences, and the needs of HCPs and patient-facing staff at UHN. This information allowed us to identify areas for improvement and develop a large-scale QI initiative comprising 12 distinct subprojects to address WPV at UHN.

Conclusions: Our QI team successfully developed a comprehensive QI project tailored to our organization's needs. To support healthcare institutions in addressing WPV, we created a 12-step framework designed to assist in developing a systemic QI approach tailored to their unique requirements. This framework offers actionable strategies for addressing WPV in healthcare settings, derived from the successes and challenges encountered during our QI project. By applying a systems-based approach that incorporates trauma-informed strategies and fosters a culture of mutual respect, institutions can develop strategies to minimize WPV and promote a safer work environment for patients, families, staff, and HCPs.

Background: Saudi ambulance clinicians face unique challenges in providing prehospital care to older trauma patients. Limited geriatric-specific training and complex needs of this population hinder effective management, leading to adverse outcomes. This study explores the perceptions of Saudi ambulance clinicians regarding geriatric trauma care and identify facilitators and barriers to improved care.

Methods: A qualitative study was conducted using a purposive sample of Saudi paramedics and ambulance technicians from Riyadh and Makkah using online semi-structured interviews and analysed using the framework method.

Results: The qualitative study recruited twenty participants and identified that they reported age-related challenges including physiological changes, polypharmacy, and communication difficulties. They all wanted training and guidelines to improve their knowledge. They reported struggling with communication difficulties, inaccurate adverse outcomes predictions, difficult intravenous cannulations, and cultural restrictions affecting care provision for female patients. We identified organisational barriers (e.g. lack of shared patient records and lack of guidelines) and cultural barriers (e.g. barriers to assessing women, attitudes towards older people, and attitudes towards paramedics) that influenced implementation of knowledge.

Conclusion: Ambulance clinicians in Saudi Arabia want guidelines and training in managing older trauma patients but these need to take into account the organisational and cultural barriers that we identified to facilitate implementing knowledge and changing practice to providing improved care.

Background: The incidence of contrast-induced acute kidney injury (CI-AKI) in the general population ranges from 0.6 to 2.3%, whereas for specific high-risk patients, the incidence can reach more than 30-40%. Ultrasound measurements of the development of CI-AKI after contrast-enhanced imaging for diagnosis in the emergency department (ED) have yet to be adequately studied. Accordingly, we aimed to evaluate the usefulness of Doppler ultrasound measurements for predicting CI-AKI in patients with normal renal function.

Methods: This prospective, observational, single-center study was conducted in the ED of a tertiary teaching and research hospital between 1 January and 1 July 2024. All patients who presented to the tertiary training and research hospital ED, who were admitted to the hospital with a decision to undergo contrast-enhanced tomography for diagnosis, and who did not meet any exclusion criteria were included in the study. Patients included in the study were evaluated by ultrasonographic measurements (interlobar renal artery peak systolic velocity (PSV), interlobar renal artery end-diastolic velocity (EDV), inferior vena cava (IVC) collapsibility index, and renal resistive index (RRI)).

Results: The postcontrast RRI cutoff values were calculated to predict CI-AKI. The area under the curve (AUC) for the postcontrast RRI was 0.914, and the cutoff value for the postcontrast RRI was 0.70 (≥), exhibiting 72.7% sensitivity and 95.6% specificity.

Conclusion: Postcontrast RRI ultrasound measurements performed after diagnostic contrast imaging in the ED show high specificity in predicting CI-AKI development. Postcontrast ultrasound measurements may predict CI-AKI development, allowing further measures to be taken. Further studies are needed to confirm these findings.

Trial registration: Clinical trial number: not applicable.

Background: The ongoing opioid epidemic in the United States has reinforced the need to provide multimodal and non-opioid pain management interventions. The PAMI-ED ALT program employed a multifaceted approach in the Emergency Department (ED) developing electronic health record (EHR) pain management order panels and discharge panels, as well as educating patients, clinicians, and ED staff on opioid alternatives, including non-pharmacologic interventions. The primary objective of this analysis was to compare changes in opioid and non-opioid analgesic administrations and prescribing in ED patients with select pain conditions (renal colic, headache, low back, and non-low back musculoskeletal pain) before and after implementation of PAMI ED-ALT. Secondary outcomes included characterizing changes in 30-day ED all-cause recidivism and hospital all-cause admissions within these pain populations.

Methods: Demographics, opioid and opioid alternative utilization, hospital admission, 30-day ED returns and change in pain intensity score were collected from January 2019-March 2020 (pre-program implementation) and January 2021-March 2023 (post-program implementation) for both the ED aggregate and program target pain populations.

Results: Pain management order panel utilization increased throughout the post-implementation period. When comparing pre to post program data, there was a reduction in opioid administrations and prescriptions for most of the target pain conditions, as well as within the ED aggregate population. Opioid alternative administrations and prescriptions increased for all pain conditions except renal colic. Hospital admissions decreased significantly amongst those with low back pain and headache/migraine and 30-day ED returns significantly declined in those with musculoskeletal pain.

Conclusion: Our findings demonstrate an opioid-alternatives program implemented within a safety-net hospital system serving a predominantly socially disadvantaged patient population can lead to changes in ED pain management and potentially reduce 30-day ED recidivism and hospitalizations.

Introduction: Overcrowding in emergency departments (ED) is a major public health issue, leading to increased workload and exhaustion for the teams, resulting poor outcomes. It seems interesting to be able to predict the admissions of patients in the ED.

Aim: The main objective of this study was to build and test a prediction tool for ED admissions using artificial intelligence.

Methods: We performed a retrospective multicenter study in two French ED from January 1st, 2010 to December 31st, 2019.We tested several machine learning algorithms and compared the results.

Results: The arrival and departure times from the ED of 2 hospitals were collected from all consultations during the study period, then grouped into 87 600 one-hour slots. Through the development of two models (one for each location), we found that the XGBoost method with hyperparameter adaptations was the best, suggesting that the studied data could be predicted (mean absolute error) at 2.63 for Hospital 1 and 2.64 for Hospital 2).

Conclusions: This study ran the construction and validation of a powerful tool for predicting ED admissions in 2 French ED. This type of tool should be integrated into the overall organization of an ED, to optimize the resources of healthcare professionals.

Background: In Sweden with about 10 million inhabitants, there are about one million primary ambulance missions every year. Among them, around 10% are assessed by Emergency Medical Service (EMS) clinicians with the primary symptom of dyspnoea. The risk of death among these patients has been reported to be remarkably high, at 11,1% and 13,2%. The aim was to develop a Machine Learning (ML) model to provide support in assessing patients in pre-hospital settings and to compare them with established triage tools.

Methods: This was a retrospective observational study including 6,354 patients who called the Swedish emergency telephone number (112) between January and December 2017. Patients presenting with the main symptom of dyspnoea were included which were recruited from two EMS organisations in Göteborg and Södra Älvsborg. Serious Adverse Event (SAE) was used as outcome, defined as any of the following:1) death within 30 days after call for an ambulance, 2) a final diagnosis defined as time-sensitive, 3) admitted to intensive care unit, or 4) readmission within 72 h and admitted to hospital receiving a final time-sensitive diagnosis. Logistic regression, LASSO logistic regression and gradient boosting were compared to the Rapid Emergency Triage and Treatment System for Adults (RETTS-A) and National Early Warning Score2 (NEWS2) with respect to discrimination and calibration of predictions. Eighty percent (80%) of the data was used for model development and 20% for model validation.

Results: All ML models showed better performance than RETTS-A and NEWS2 with respect to all evaluated performance metrics. The gradient boosting algorithm had the overall best performance, with excellent calibration of the predictions, and consistently showed higher sensitivity to detect SAE than the other methods. The ROC AUC on test data increased from 0.73 (95% CI 0.70-0.76) with RETTS-A to 0.81 (95% CI 0.78-0.84) using gradient boosting.

Conclusions: Among 6,354 ambulance missions caused by patients suffering from dyspnoea, an ML method using gradient boosting demonstrated excellent performance for predicting SAE, with substantial improvement over the more established methods RETTS-A and NEWS2.

Background: Air medical transport services play a significant role in emergency situations by providing timely transfers of critically ill patients to medical facilities. This study aimed to investigate the mission characteristics of helicopter emergency medical services (HEMS) and the associated time intervals in a geographically remote region of eastern Iran. We also compared the prehospital times of HEMS and ground transportation to determine whether dispatching a helicopter is time-efficient.

Methods: This retrospective cross-sectional study was conducted at the prehospital emergency medical center in Gonabad, a remote area in eastern Iran. Data were collected using standardized electronic forms developed by the Ministry of Health and Medical Education (MOHME) in Iran. We analyzed the mission profiles and prehospital time intervals for all Gonabad HEMS missions conducted between 2021 and 2024. The mean activation time was compared to the national benchmark of three minutes, and the prehospital time intervals of air ambulances were compared to those of ground ambulances.

Results: From 2021 to 2024, there were 252 HEMS missions, transporting 265 patients. Of all 252 missions, 95 (37.7%) were primary missions, and 157 (62.3%) were secondary missions. The most frequent reasons for air ambulance dispatch were trauma, acute coronary syndrome, and strokes. The mean ± SD for HEMS activation time was 9.14 ± 3.63 min, significantly exceeding the national benchmark of three minutes. HEMS prehospital time was 49.73 ± 9.67 min. The comparison of prehospital time intervals indicated that air emergency services are more time-efficient than ground ambulances.

Conclusion: This study found that the mean activation time of air ambulances exceeded the national benchmark of three minutes. When comparing prehospital times for air ambulance and ground ambulance services, HEMS was faster than both ground scenarios. The current benchmark for helicopter activation time in Iran may need clarification and revision.