The number of patients seeking help in emergency departments is steadily increasing. In part, this is due to patients who have acute symptoms, but do not require emergency care, as well as multimorbid patients needing complex medical care. Emergency departments serve as an interface between primary care and in-patient as well as out-patient care. The ongoing public discussion about the need to readjust emergency care structures in Germany does not adequately address this aspect. The knowledge of characteristics and needs of patients seeking help in emergency departments is insufficient. In order to develop interventions matching these needs it is necessary to gain deeper insight into these characteristics and needs. EMANet is a health services research project funded by the Federal Ministry of Education and Research. Its aim is to collect representative data on the course of medical care of emergency patients with ambulatory care sensitive conditions in all eight emergency departments in Mitte, the inner city district of Berlin. The EMANet project focuses on three patient groups: a) patients with cardiac symptoms and possible psychiatric comorbidities, b) ambulatory patients with acute or chronic diseases of the respiratory tract, and c) geriatric patients with hip fractures. The collected data shall be used to gain a better understanding of health care utilization patterns, patient-perceived satisfaction and risk factors for potentially avoidable medical conditions or worsening of chronic disease. The mixed methods design of EMANet includes quantitative data of 1,650 patients at two time points and corresponding secondary (i. e. routine) data from hospital information systems. In addition, qualitative interviews with patients and health care professionals shall reveal unmet needs for medical care. The results will give us more in-depth insight into the perceived current capacity overload and help implement structural changes in the health care system.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1016/j.zefq.2018.07.009 | DOI Listing |
J Gen Intern Med
January 2025
Icahn School of Medicine at Mount Sinai, Institute for Health Equity Research, New York, USA.
Background: Over 60 million patients in the USA have limited English proficiency (LEP) and experience barriers in care. Still, there exists no standardized method of monitoring the utilization of language interpreting services (LIS).
Objective: To introduce a methodological approach to systematically monitor utilization of LIS for LEP patients.
Sci Rep
January 2025
Infectious Diseases Clinic, Azienda Sanitaria Universitaria Friuli Centrale, 33100, Udine, Italy.
Enterococcus faecalis is responsible for numerous serious infections, and treatment options often include ampicillin combined with an aminoglycoside or dual beta-lactam therapy with ampicillin and a third-generation cephalosporin. The mechanism of dual beta-lactam therapy relies on the saturation of penicillin-binding proteins (PBPs). Ceftobiprole exhibits high affinity binding to nearly all E.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Urology, Vanderbilt University Medical Center, Nashville, USA.
Recent advancements of large language models (LLMs) like generative pre-trained transformer 4 (GPT-4) have generated significant interest among the scientific community. Yet, the potential of these models to be utilized in clinical settings remains largely unexplored. In this study, we investigated the abilities of multiple LLMs and traditional machine learning models to analyze emergency department (ED) reports and determine if the corresponding visits were due to symptomatic kidney stones.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Emergency Medicine, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea.
This study developed a predictive model using deep learning (DL) and natural language processing (NLP) to identify emergency cases in pediatric emergency departments. It analyzed 87,759 pediatric cases from a South Korean tertiary hospital (2012-2021) using electronic medical records. Various NLP models, including four machine learning (ML) models with Term Frequency-Inverse Document Frequency (TF-IDF) and two DL models based on the KM-BERT framework, were trained to differentiate emergency cases using clinician transcripts.
View Article and Find Full Text PDFShock
January 2025
Pharmacology, University of Vermont, Burlington, VT.
Objective: Loss of function of the phospholipid scramblase (PLS) TMEM16F results in Scott Syndrome, a hereditary bleeding disorder generally attributed to intrinsic platelet dysfunction. The role of TMEM16F in endothelial cells, however, is not well understood. We sought to test the hypothesis that endothelial TMEM16F contributes to hemostasis by measuring bleeding time and venous clotting in endothelial-specific knockout (ECKO) mice.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!