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.