Is the fast-track process efficient and safe for older adults admitted to the emergency department?

Strategies to Measure and Improve Emergency Department Performance: A Review

Emergency Departments (EDs) globally face escalating challenges such as overcrowding, resource limitations, and increased patient demand. This study aims to identify and analyze strategies to enhance the structural performance of EDs, with a focus on reducing overcrowding, optimizing resource allocation, and improving patient outcomes. Through a comprehensive review of the literature and observational studies, the research highlights the effectiveness of various approaches, including triage optimization, dynamic staffing, technological integration, and strategic resource management. Key findings indicate that tailored strategies, such as implementing advanced triage protocols and leveraging telemedicine, can significantly reduce wait times and enhance patient throughput. Furthermore, evidence suggests that dynamic staffing models and the integration of cutting-edge diagnostic tools contribute to operational efficiency and improved quality of care. These strategies, when combined, offer a multifaceted solution to the complex challenges faced by EDs, promising better patient care and satisfaction. The study underscores the need for a comprehensive approach, incorporating both organizational and technological innovations, to address the evolving needs of emergency healthcare.

Impact of Geriatric Admissions on Workload in the Emergency Department

Background: Due to the increase in life expectancy, both the general population and the population of patients of emergency departments (ED) are getting older. An understanding of differences, workload and resource requirements may be helpful in improving patient care. The main goal of this study was to evaluate the reasons for geriatric admissions in the ED, identify typical medical problems and assess the number of resources in order to provide more effective management. Methods: We examined 35,720 elderly patients' ED visits over the course of 3 years. The data collected included age, sex, timing and length of stay (LOS), use of various resources, endpoint (admission, discharge or death) and ICD-10 diagnoses. Results: The median age was 73 years [66-81], with more females (54.86%). There were 57.66% elderly (G1), 36.44% senile (G2) and 5.89% long-liver (G3) patients. There were more females in the older groups. The total admission rate was 37.89% (34.19% for G1, 42.21% for G2 and 47.33% for G3). The average length of the patient's stay was 150 min [81-245] (G3 180 min [108-277], G2 (162 min [92-261]) and G1 139 min [71-230]). Heart failure, atrial fibrillation and hip fracture were the most common diagnoses. Nonspecific diagnoses were common in all groups. Conclusion: The vast majority of geriatric patients required considerable resources. With increasing ages, the number of women, LOS and number of admissions increased.

Prediction of hospitalization using artificial intelligence for urgent patients in the emergency department

Timely assessment to accurately prioritize patients is crucial for emergency department (ED) management. Urgent (i.e., level-3, on a 5-level emergency severity index system) patients have become a challenge since under-triage and over-triage often occur. This study was aimed to develop a computational model by artificial intelligence (AI) methodologies to accurately predict urgent patient outcomes using data that are readily available in most ED triage systems. We retrospectively collected data from the ED of a tertiary teaching hospital between January 1, 2015 and December 31, 2019. Eleven variables were used for data analysis and prediction model building, including 1 response, 2 demographic, and 8 clinical variables. A model to predict hospital admission was developed using neural networks and machine learning methodologies. A total of 282,971 samples of urgent (level-3) visits were included in the analysis. Our model achieved a validation area under the curve (AUC) of 0.8004 (95% CI 0.7963-0.8045). The optimal cutoff value identified by Youden's index for determining hospital admission was 0.5517. Using this cutoff value, the sensitivity was 0.6721 (95% CI 0.6624-0.6818), and the specificity was 0.7814 (95% CI 0.7777-0.7851), with a positive predictive value of 0.3660 (95% CI 0.3586-0.3733) and a negative predictive value of 0.9270 (95% CI 0.9244-0.9295). Subgroup analysis revealed that this model performed better in the nontraumatic adult subgroup and achieved a validation AUC of 0.8166 (95% CI 0.8199-0.8212). Our AI model accurately assessed the need for hospitalization for urgent patients, which constituted nearly 70% of ED visits. This model demonstrates the potential for streamlining ED operations using a very limited number of variables that are readily available in most ED triage systems. Subgroup analysis is an important topic for future investigation.

Overuse of Health Care in the Emergency Services in Chile

The Public Health Service in Chile consists of different levels of complexity and coverage depending on the severity and degree of specialization of the pathology to be treated. From primary to tertiary care, tertiary care is highly complex and has low coverage. This work focuses on an analysis of the public health system with emphasis on the healthcare network and tertiary care, whose objectives are designed to respond to the needs of each patient. A review of the literature and a field study of the problem of studying the perception of internal and external users is presented. This study intends to be a contribution in the detection of opportunities for the relevant actors and the processes involved through the performance of Triage. The main causes and limitations of the excessive use of emergency services in Chile are analyzed and concrete proposals are generated aiming to benefit clinical care in emergency services. Finally, improvements related to management are proposed and the main aspects are determined to improve decision-making in hospitals, which could be a contribution to public health policies.