View clinical trials related to Emergency Medicine.
Filter by:An emergency department (ED) is a healthcare service that provides the first clinical assessment and treatment to patients with various acute conditions. These departments, however, are often overwhelmed by the large volume of patients. As a consequence, ED crowding has become a global concern and has been correlated to reduced timeliness and effectiveness of care and increased patient mortality. Concerning input, 20% to 30% of patients are brought to the ED by ambulance; the remaining are self-presenting for the vast majority. Notably, non-urgent conditions characterize a high proportion of all ED visits worldwide, and almost all of these visits involve self-presenting patients. Increasing the awareness of these patients about the mandate of EDs and the real-time situation of the neighboring emergency departments has the potential to reduce the self-presentation of patients with minor, non-urgent conditions. Such patient empowerment can be achieved through a dashboard. Concerning throughput, working in the ED requires emergency physicians and nurses to treat many patients at once while maintaining situational awareness of the surroundings. This is especially true for the head of the department, but it also holds for all physicians. It can be crucial, for example, for physicians to know if there is a bottleneck in the flow of the entire patient care process, such as a particularly high average waiting time for radiology reporting or cardiologic consultation. The availability of this information allows countermeasures to be put in place to regain efficiency. All this can be achieved through dedicated dashboards automatically fed from various information system. In addition, appropriate dashboards also enable health policymakers to monitor specific epidemiological phenomena, such as the emergence of certain infectious diseases, in a timely manner.
The peer-to-peer comparison means center-to-center comparison, which requires adjusting for possible differences among centers to be fair and convincing. The first step to reach this goal is to develop a predictive model that accurately estimates each patient's probability of being admitted, starting from clinical conditions and boundary variables. Such a model would make it possible to calculate, for each ED, the expected hospitalization rate; that is, the hospitalization rate that would have been observed if the ED had behaved like the average of the EDs that provided the data to build the model itself. Comparing the observed hospitalization rate in the single ED with the expected rate derived from the model provides a rigorous method of comparing the department with the average performance, taking into account the characteristics of the patients treated and the conditions under which the ED operated. In other words, the predictive model represents the benchmark against which each ED is evaluated.
The goal of this retrospective cohort study is to develop and validate a language model that can interpret the contents of emergency department electronic medical records and extract relevant information for research purposes in all adult patients who arrived at the participating emergency departments in a three-year period. The main question it aims to answer is: is the language model able to interpret the contents of emergency department electronic medical records and extract the requested information from them so that it can be used to make accurate analyses and predictions? The study is retrospective and data will be extracted automatically from the medical health records.
The goal of this clinical trial is to test giving all medical/non-medical information in the pediatric emergency room(ER). Main questions it aims to answer are: - Does providing medical/non-medical information to parents of patients visiting the emergency room raise the satisfaction with the ER visit? - Does providing medical/non-medical information to parents of patients visiting the emergency room lower the workload of medical staff? 60 participants will be randomly assigned to treatment group and control group. Both groups will communicate freely with the researchers through mobile chat service. Treatment group will get information of medical/non-medical information in emergency room and control group will get information if they need. Before leaving the emergency room, both group will fill out a questionnaire related to satisfaction with the emergency room visits. 5 out of 30 participants of each group will be interviewed about their satisfaction with service. 10 nurses in charge of patients participating in the study record the number of questions directly received and 5 out of 10 nurse will be interviewed about their nursing experience for participants using mobile chatbot service. Researchers will compare treatment group and control group to see if providing medical/non-medical information raise the satisfaction with emergency room visits.
Emergency Departments (EDs) across Ontario are being inundated with unprecedented high patient volumes and a staffing shortage that directly impacts patient care and flow. An area of concern among EDs is the offload zone where patients are brought in by ambulance. EMS offload time is the time it takes paramedics to transfer a patient to the appropriate area within an emergency department and give hospital staff a summary of what concerns the patient is seeking care for. There are multiple factors that may delay this time, including limited staff in the offload area to complete the transfer process due to competing patient care responsibilities. The adaptive staffing model study will look to add a primary care paramedic (PCP) or a registered nurse (RN) in the offload zone during times of high ambulance volume (August to January) to help with patient care within the offload zone. This single-centered community hospital study will evaluate the benefits of having a PCP or RN, compared to the current model, on ambulance offload times, patient safety outcomes, patient treatment times, and staff well-being using three different models of staffing.
This study assessed the feasibility and effectiveness of using Mixed Reality (MR) through the use of HoloLens2TM technology to enhance emergency clinical care delivery in a simulated environment. This was achieved by inviting 22 resident grade doctors to complete two scenarios. Each scenario was supported either by standard care methods or Mixed reality. The participants were randomised to at the start of the scenarios to determine which support they would receive first. The main outcome was to see if there was difference in error rates. This was assessed using the ICECAP multidimensional error capture tool. Secondary outcomes included teamwork, scenario completion, stress/cognitive load, and Mixed reality device user acceptability.
The purpose of the research is to see if patients that come to the Emergency Department with chest pain can be more accurately and more quickly diagnosed by magnetocardiography (MCG) to see if their chest pain is caused by coronary ischemia (reduced blood flow to the heart) in patients with normal or have non-specific changes on the ECG vs other causes by other reasons.
Airway management in out-of-hospital cardiac arrest is still debated. Several options exist: bag-valve-mask ventilation, supraglottic devices and endotracheal intubation. Intermediate and advanced airway management strategies could be useful devices to increase chest compression fraction. A previous study shows that early insertion of an i-gel device significantly increases chest compression fraction and enhances respiratory parameters. However, the compressions were found to be shallower in the experimental group using the i-gel device. Although, the shallower compressions found in the supraglottic airway device group did not appear to be linked to their provision in an over-the-head position, it is reasonable to assume that the addition of a feedback device to the use of an i-gel® device could fix this issue. The feedback devices seem to be able to provide a benefit, and allow deeper compressions / more often in the depth target. There is a mismatch between perceived and actual cardiopulmonary resuscitation performance supporting the need for such a feedback device's study.
This observational study aims to use electronic health records to build an International Big Data Centre in Emergency Medicine, within the Institute of Sciences in Emergency Medicine (ISEM) at the Guangdong Provincial People's Hospital. The main questions it seeks to answer are not limited to the following: - Identify the relationship between Emergency Department Length of Stay (EDLOS), Mortality, and Adverse Events (AE) - Identify the risk factors associated with high mortality and AE rate among patients who experience prolonged EDLOS - Other research questions related to emergency medicine, such as building prediction and cluster models for acute diseases
Our aim is to evaluate the feasibility of telemedicine in context of medical service in an ambulance station and in a further step in context of civil defense situations. The conditions in those situations are different to the usual usage of telemedicine in context of emergency medical services (EMS) like the "Telenotarzt" in Aachen. The medical personnel who are performing the medical treatment in ambulance stations or civil defense situations are most volunteers and are ordinarily not as experienced and educated as professional medical personnel in EMS. In case of civil defense situations, the personnel also must deal with shortage in material which we are not going to simulate in our study. The study will be realized at each one ambulance station at four large-scale events. Every time there will be a telemedicine workspace with an EMS-physician educated in telemedicine who can be contacted by briefed and equipped medical personnel according to the "TeleSAN"-standard. The emergency personnel start the patient's treatment according to their known standards and decide on their own whether they want to contact the tele-EMS-physician or not. Before starting the tele-consultation, every patient must declare his consent to the tele-consultation, otherwise tele-consultation cannot be realized, and the patient needs to be treated according to common standards. Due to spatial proximity of the telemedicine workspace and the ambulance station, the tele-EMS-physician can also work as an EMS-physician in the ambulance station if necessary. As a hypothesis we declare that telemedicine is feasible in context of an ambulance station. To evaluate the feasibility, we use patient's medical protocols, observations and surveys.