View clinical trials related to Emergency Medical Services.
Filter by:The goal of this pilot, non-controlled, non-randomised, single centre, prospective intervention feasibility study is to assess the feasibility of a home DC-ECV in the treatment of recurrent symptomatic AF performed by APP in 25 patients. The main question[s] it aims to answer are: Primary objective: In this prospective intervention feasibility study, in 25 patients the primary endpoint is completion of cardioversion to sinus rhythm. (% of study patients with a recurrence of AF in whom a home cardioversion is performed, i.e. to whom at least one DC shock was administered while the patient was under sedation). Feasibility endpoints are ; (a) evaluation of enrolment of participants, (b) evaluation and refinement of data and outcome collection procedures, (c) evaluation of logistics, (d) evaluation of the appropriateness of the intervention and research procedures to manage and implement the intervention, and (e) preliminary evaluation of participant responses to the intervention. Secondary objectives: Safety endpoint: Complications immediately during and one hour after cardioversion (e.g. arrhythmias, changes in the electrocardiogram, hypotension related to sedation and/or vasodilation or skin irritation). A composite of major adverse cardiovascular and cerebrovascular events (MACCE) occurring within 24 hours MACCE occurring during 6 weeks follow-up; any hospitalisation and all-cause mortality during 6 weeks follow-up; number (%) of patients in sinus rhythm at 1 hour in the post-shock observation period; idem at the end of 6 weeks follow-up; inventory of all interventions in the study related to cost-of-care.
This study will assess the efficacy of receiving emergency care at home versus in the brick-and-mortar emergency department.
The goal of this clinical trial is to test if treatment with prehospital Non-invasive ventilation (NIV) for patients with acute respiratory failure (ARF), due to acute exacerbation of chronic obstructive pulmonary disease (AECOPD) based on in-hospital criteria, should be used in the prehospital setting. This is performed with the introduction of prehospital arterial blood gas analyzation. The primary objective is: • To determine if early prehospital applied NIV together with standard medical treatment will affect arterial pH at hospital arrival in patients with ARF due to AECOPD. Participants in the intervention will receive Non-invasive ventilation together with standard medical treatment. The intervention will be compared to standard medical treatment alone, that may include inhaled bronchodilators, intravenous corticosteroids, and titrated oxygen supplementation.
This project is a randomized controlled clinical research design, The hypothesis P-I-C-O of the study is: For adult patients in the Taipei City and New Taipei City communities who have suffered sudden non-traumatic death and have been resuscitated by advanced paramedics, the intervention group that receives combined drug treatment (epinephrine, vasopressin, methylprednisolone) has a better rate of sustained recovery of spontaneous circulation (ROSC) (primary outcome) and long-term survival status (secondary outcomes) compared to the control group that receives single drug treatment (epinephrine).
Fentanyl and esketamine are both standard of care for treatment of acute severe traumatic pain in the prehospital setting in the Netherlands. However, it is not known whether they are equally effective and safe. It is also not known whether intranasal (IN) administration of fentanyl or esketamine is equally effective and safe as intravenous (IV) administration. The FORE-PAIN trial is a double-blind multi-arm randomized non-inferiority trial comparing Fentanyl IN, esketamine IV and esketamine IN (intervention arms) to fentanyl IV (comparator arm) for prehospital management of traumatic pain. The investigators hypothesize that all intervention arms provide analgesia that is non-inferior to the comparator arm, and that all study arms are equally safe.
Introduction: Early and rapid diagnosis of etiology is often an important part of saving the lives of patients in emergency department. Chest CT is an important examination method for emergency diagnosis because of its fast examination speed and accurate localization. Traditional medical imaging diagnosis relies on radiologists to report in a qualitative and subjective manner. Through the interdisciplinary combination of clinical, imaging and artificial intelligence, the integration of multi-omics data, the construction of large-scale language models, and the construction of the auxiliary diagnosis support system of "one check for multiple diseases" provide new ideas and means for the rapid and accurate screening of emergency critical diseases. Method： Study design Investigators retrospectively collected cardiovascular, respiratory, digestive, and neurological CT images, demographic data, medical history and laboratory date of emergency department patients during the period from 1 January 2018 and 30 December 2024. Regularly carry out standardized follow-up work, and complete the collection and database establishment of clinical-imaging multi-omics data of patients attending emergency department.The inclusion criteria are:1. adult emergency patients with cardiovascular, respiratory, digestive, and nervous system diseases; 2. These patients had CT images. Patients with incomplete clinical or radiographic data were excluded from the analysis. Regularly carry out standardized follow-up work, and complete the collection and database establishment of clinical-imaging multi-omics data of patients attending emergency department. Based on the collected medical text data, an artificial intelligence large-scale language model algorithm framework is built. After the structure annotation of chest CT images is performed by doctors above the intermediate level of imaging, the Transformer deep neural network is trained for CT image segmentation, and a series of tasks such as structural structure segmentation, damage detection, disease classification and automatic report generation are developed based on Vision Transformer self-attention architecture mechanism. A multi-disease diagnosis and treatment decision-making system based on chest CT images, clinical text and examination multimodal data was constructed and validated. Disscusion Emergency medicine deals mainly with unpredictable critical and sudden illnesses. Patients who come to the emergency department for medical treatment often have acute onset, hidden condition, rapid progress, many complications, high mortality and disability rate. Assisted diagnosis systems developed by combining clinical text, images and artificial intelligence can greatly improve the ability of emergency department doctors to accurately diagnose diseases. This study fills the blank of CT artificial intelligence aided diagnosis system for emergency patients, and provides a rapid diagnosis scheme for multi-system and multi-disease. Finally, the results will be transformed into clinical application software and used and promoted in clinical work to improve the diagnosis and treatment level.
The goal of this quality improvement study is to measure the impact of incorporation of a manual rapid fluid infuser (RFI) for intravenous crystalloid infusion in patients with suspected sepsis in the prehospital interval. The main question[s] it aims to answer are: - Does the intervention affect the timeliness of fluid administration? - Does the intervention affect CMS sepsis bundle care measure compliance? - Does the intervention affect processes and outcomes of care? - Are there any adverse effects? Researchers will compare this intervention to use of more conventional gravity or pressure-infusion bag crystalloid infusion.
Paramedics and EMT will be recruited among four Emergency Medical Services (EMS) in Switzerland to manage a 10-minutes simulation-based adult out-of-hospital cardiac arrest scenario in teams of two. Depending on randomization, each team will manage the scenario according either to their current approach (30 compressions with 2 bag-mask ventilations), or to the experimental approach (continuous compressions since the start of CPR except for rhythm analysis and shock delivering, with early insertion of an i-gel® device to deliver asynchronous ventilations). The main hypothesis is that early insertion of i-gel could improve CCF during out-of-hospital cardiac arrest, with a reasonable time to first effective ventilation.
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 Emergency Department of Kuopio University Hospital (KUH). Data will be gathered during 2 weeks of piloting, after which the results will be analysed. Anticipated number of patients to the study is minimum of 246 patients, with objective to be several hundreds. When attending to the hospital, patients will report their demographics, background information and symptoms using structured IPFM online form. Patients entering the unit in an ambulance or with need of immediate care of healthcare professionals due to severe and acute conditions are referred similar to normal process to ensure the patient safety. Results obtained from IPFM are blinded from the healthcare professional and IPFM does not affect professional's clinical decision making in any way. The data obtained from IPFM online form and clinical data from the emergency department and KUH 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 (using Emergency Severity Index [ESI], an international triaging protocol for emergency units, and an assessment by triage nurse); 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.
Rational: Out of hospital cardiac arrest is a devastating event with a high mortality. Survival rates have increased over the last years, with the availability of AED's and public BLS. Previous studies have shown that deranged physiology after return of spontaneous circulation (ROSC) is associated with a worse neurological outcome. Good quality post-arrest care is therefore of utmost importance. Objective: To determine how often prehospital crews (with their given skills set) encounter problems meeting optimal post-ROSC targets in patients suffering from OHCA, and to investigate if this can be predicted based on patient-, provider- or treatment factors. Study design: Prospective cohort study of all patients attended by the EMS services with an OHCA who regain ROSC and are transported to a single university hospital, in order to identify those patients with a ROSC after a non-traumatic OHCA who had deranged physiology and/or complications from OHCA EMS personnel was unable to prevent/deal with in the prehospital environment. Study population: Patients, >18 years, transported by the EMS services to the ED of the University Hospital Groningen (UMCG) with a ROSC after OHCA in a 1 year period Main study parameters/endpoints: Primary endpoint of our study is the percentage of OHCA patients with a prehospital ROSC who arrive in hospital with either a deranged physiology or with complications from OHCA EMS personnel was unable to deal with.