View clinical trials related to Triage.
Filter by:"Triage" is the first process of emergency treatment. It is mainly based on the severity of the disease and the appropriate allocation of limited people, time, place, and medical resources. It is very important and related to the quality of medical care. In order to accurately judge the severity of patients, reduce the individual errors of artificial memory and increase the consistency of the judgment of triage levels, the new emergency five-level triage classification standard (Taiwan Triage and Acuity Scale) was implemented in 2010. , TTAS), the purpose is to allocate medical resources in the most appropriate manner at the appropriate time. It has been revised many times so far, so correct injury classification can maximize the safety of people's lives. This article reviews the relevant literature and finds that to have an efficient injury classification so that each patient receives appropriate medical allocation, an organized, effective and systematic injury inspection process structure is needed to enable the injury nurse to perform correct inspections. Injury skills and cognition should be actively promoted, and this policy must require traumatization nurses to continuously update their traumatization cognition and skills, and must also continue to conduct regular assessments and reviews to continuously improve the quality of traumatization grading by traumatization nurses. It can be seen that triage nurses must have skills, knowledge, attitudes and systems to improve the accuracy of triage levels and ensure the treatment of patients who need priority treatment . Therefore, this study can be used to understand the learning effectiveness of trauma care before and after diversified teaching, which can be used as a basis for teaching new trauma care nurses.
This interventional study aims to investigate whether Virtual Reality (VR) compared to traditional education and training, can enhance the correct triage abilities of nursing students in simulated mass casualty incidents (MCI). The primary research question seeks to answer if VR compared to Standard education supports students' ability to triage correctly in a simulated MCI situation. Additionally, this study aims to address the following secondary questions: Does VR compared to standard education have an effect on the time to triage in a simulated MCI situation? And, does VR compared to Standard education have an effect on theoretical knowledge retention concerning triage in MCI situations? In the interventional group, participants will utilize VR for MCI training and learning, while the control group will undergo standard education, including lectures and paper exercises. The researchers will compare the two groups of nursing students to assess whether VR yields better outcomes in MCI triage education.
This clinical audit focused on improving how patients are prioritized and managed in the emergency department. The audit took place in a hospital in Rawalpindi and involved two cycles of data collection. The results showed that after an educational intervention, there was an improvement in accurately triaging patients and a decrease in cases that were categorized as urgent when they were not. The audit also revealed a decrease in non-urgent cases visiting the emergency department. The findings highlight the importance of proper training, following triage guidelines, and referring non-urgent cases to other departments. The suggestions include improving the triage process and making the emergency department more efficient for better patient care.
In the emergency department, the urgency for treating patients is determined according to the Manchester Triage System. The parameters collected in this process are deterministically translated into a treatment priority. The Manchester Triage System (MTS), which has been in use for at least 20 years, is a widely used, validated and standardized procedure for initial assessment in the emergency department - this initial assessment (triage) is done to prioritize medical assistance at a central point. Especially in emergency situations, critically endangered patients often require the deployment of a large part of the available staff at the same time - the medically correct triage of patients according to objective criteria in order to enable an adequate allocation of the available resources at the right time is the main objective. In the optimal case, each patient is treated by medical professionals within the time frame that is adequate for his/her health condition. Using artificial intelligence methods, it may be possible to increase the accuracy of treatment priority assignment. In the best case, incorrect prioritization of patients can be prevented and medical care can be ensured for those patients who actually need it most urgently. However, initial assessment, even if standardized and validated, still runs under limited resource conditions - time, space, material and personnel. Last but not least, the very idea of conducting an initial assessment limits its validity, and the results of the allocation fluctuate according to current research, although the determinants of this are currently unknown.
The objective of this study is to design a computer-based Decision Support System (DSS) using the ESI (Emergency Severity Index) and ATS (Australasian Triage Scale) algorithms, integrate it into the Hospital Information Management System (HIMS), evaluate the effect of the triage management in the emergency department and ensure the continuity of the system.
The aims of this study is to integrate real-time data flow infrastructure between hospital information system and AI models and to conduct a cluster randomized crossover trial to evaluate the efficacy of the AI models in improving patient flow and relieving ED crowding.
Almost everyone will have low back pain (LBP) at some point in their lives. LBP is a complex multifactorial condition for which diagnosis and clinical management remains a challenge. Factors such as wait times, delays in diagnosis or proper referral can result in Canadian patients having difficulty getting the care that they need. The overall objective of this project is to explore how chiropractors, who specialize in the diagnosis and clinical management of spinal conditions, can transform healthcare trajectories and improve the health of patients with LBP by integrating medical specialist team.To do so, patients with low back pain seeking medical care within the public health system will be first seen by chiropractors. Chiropractors will play a key role in identifying the type of low back pain and subsequently offering guidance to medical specialists with regard to the best treatment and management options that are currently recommended. Participating patients will be followed over a year while extensive health-related data will be collected and compared to non-triage patients with LBP.
In the year of 2010, the Taiwan Ministry of Health and Welfare had implemented the 5-level Taiwan Triage and Acuity Scale(TTAS) in Taiwan emergency department. TTAS is a triage system that was modified from CTAS. It has a comprehensive chief complaint system and accomplished with an electronic decision support system (eTTAS) to help triage nurse to assign acuity level on emergency patients in emergency departments of Taiwan. TTAS has adult and children modifiers, but no modifier for the elderly. In emergency departments, the geriatric population have been increasing in many countries for the last few years. They often present with atypical symptoms or signs, and multiple comorbidities that complicate diagnosis and treatment and are at risk of under-triage. Although the TTAS has overall validity and reliability, its performance in geriatric patients has not been well studied. In order to reduce the chance to under-triage the geriatric patients in emergency departments and to improve the discriminatory and predictive power for triaged patients who require admission, the objective of the study is to examine the difference of urgency, admission rate and medical resource consumption of geriatric patients with different age group compared to non-geriatric patients, to develop predictive model for hospitalization and medical resource consumption among geriatric patients and to develop special geriatric modifiers.
Digital health technologies (DHT) are increasingly developed to support healthcare systems around the world. However, they are frequently lacking evidence-based medicine and medical validation. There is considerable need in the western countries to allocate healthcare resources accurately and give the population detailed and reliable health information enabling to take greater responsibility for their health. Intelligent patient flow management system (IPFM, product name Klinik Frontline) is developed to meet these needs. In practice, IPFM is used for decision support in the triaging and diagnostic processes as well as automatizing the management of inflow of the patients. The core of the IPFM is a clinical artificial intelligence (AI), which utilizes a comprehensive medical database of clinical correlations generated by medical doctors. The study population of this research consists of patients from the Paediatric Emergency Clinic of Turku University Hospital (TUH). Data will be gathered during 6 months of piloting, after which the results will be analysed. Anticipated number of patients to the study is minimum of 500 patients, with objective to be 1 000. When attending to the hospital, patients or their guardians will report their demographics, background information and symptoms using structured IPFM online form. Results obtained from IPFM are blinded from the healthcare professional and IPFM does not affect professional's clinical decision making. The data obtained from IPFM online form and clinical data from the emergency department and TUH will be analysed after the data collection. The main aim of the research is to validate the use of IPFM by evaluating the association of IPFM output with 1) urgency and severity of the conditions; and 2) actual diagnoses diagnosed by medical doctors. The main hypotheses of the research are that 1) IPFM is safe and sensitive in evaluating the urgency of the conditions of arriving patients at the emergency department and that 2) IPFM has sufficient correlation of differential diagnosis with actual diagnosis made by medical doctor.
The QUICK study main aim is to assess the predictive value at Day 1, of a model built on lung ultrasound (LUS) and clinical data, both recorded at hospital admission of COVID-19 patients.