Objective: The present study aims to determine the importance of certain factors in predicting the need of hospital admission for a patient in the ED.
Methods: This is a retrospective observational cohort study between January 2010 and March 2012. The characteristics, including blood test results, of 100,123 patients who presented to the ED of a tertiary referral urban hospital, were incorporated into models using logistic regression in an attempt to predict the likelihood of patients' disposition on leaving the ED. These models were compared with triage nurses' prediction of patient disposition.
Results: Patient age, their initial presenting symptoms or diagnosis, Australasian Triage Scale category, mode of arrival, existence of any outside referral, triage time of day and day of the week were significant predictors of the patient's disposition (P < 0.001). The ordering of blood tests for any patient and the extent of abnormality of those tests increased the likelihood of admission. The accuracy of triage nurses' admission prediction was similar to that offered by a model that used the patients' presentation characteristics. The addition of blood tests to that model resulted in only 3% greater accuracy in prediction of patient disposition.
Conclusions: Certain characteristics of patients as they present to hospital predict their admission. The accuracy of the triage nurses' prediction for disposition of patients is the same as that afforded by a model constructed from these characteristics. Blood test results improve disposition accuracy only slightly so admission decisions should not always wait for these results.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1111/1742-6723.12252 | DOI Listing |
Am J Emerg Med
December 2024
Department of Emergency Medicine, Sisli Hamidiye Etfal Training and Research Hospital, Istanbul, Turkey.
Background: The number of emergency department (ED) visits has been on steady increase globally. Artificial Intelligence (AI) technologies, including Large Language Model (LLMs)-based generative AI models, have shown promise in improving triage accuracy. This study evaluates the performance of ChatGPT and Copilot in triage at a high-volume urban hospital, hypothesizing that these tools can match trained physicians' accuracy and reduce human bias amidst ED crowding challenges.
View Article and Find Full Text PDFThis systematic review evaluates the impact of trauma care and emergency preparedness training programs on prehospital primary survey effectiveness. A comprehensive search strategy was employed across multiple databases, including PubMed, Cochrane Library, Embase, and the Cumulated Index to Nursing and Allied Health Literature (CINAHL), focusing on studies involving healthcare professionals such as paramedics, nurses, and emergency medical technicians (EMTs). The review included randomized controlled trials (RCTs), clinical trials, and cohort studies that assessed various training modalities like virtual reality (VR) simulations, case-based learning (CBL), and hands-on workshops.
View Article and Find Full Text PDFCureus
November 2024
Endocrinology, Diabetes and Metabolism, University of Minnesota School of Medicine, Minneapolis, USA.
Background: Depression screening is an important first step to identifying patients who might benefit from depression treatment. Merit-based incentive payment system (MIPS) quality measures can yield financial benefits or losses for healthcare systems, including depression screening.
Objectives: This study aims to (1) develop a team-based care workflow to improve MIPS depression screening in a specialty clinic and (2) modify the workflow to include a virtual nursing and behavioral health resource after the COVID-19 pandemic hit.
Rev Bras Enferm
December 2024
Universidade Federal de Santa Catarina. Florianópolis, Santa Catarina, Brazil.
Objectives: to analyze interobserver agreement in the Reception and Risk Stratification in Obstetrics protocol implementation.
Methods: a cross-sectional study carried out during Reception and Risk Stratification in Obstetrics implementation, conducted in a tertiary hospital in southern Brazil with 891 participants in January 2020. Descriptive and interobserver agreement analysis was carried out using the Kappa coefficient in the risk stratification assigned by the triage nurse and reviewed by the researcher.
The Asst Valcamonica faces significant challenges in providing continuity of care services due to a shortage of medical personnel, exacerbated by its location in a mountainous area and the seasonal variability of tourism. To address these needs, a temporary nursing-run clinic was developed, aimed at providing healthcare to tourists, particularly during August 2023. The Family and Community Nurse (IFeC) plays a crucial role in primary care, working in synergy with remote medical consultations to ensure an integrated care model; provide first-level healthcare during peak tourist periods.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!