Background: The public health strategy of increasing access to comprehensive home or community-based healthcare services and emergency home visits is intent on reducing the overcrowding of emergency departments. However, scientific evidence regarding the association between home-based healthcare services and emergency department uses is surprisingly insufficient and controversial so far. The present retrospective study identified the risk factors for emergency department visits among patients receiving publicly-funded homecare services.
Methods: The personal demographic and medical information, caregiver characteristics, and behaviours related to homecare services and emergency department visits from the medical records and structured questionnaires of 108 patients who were recipients of integrated homecare services in a regional hospital in southern Taiwan between January 1, 2020, and December 31, 2020, were collected. After screening the potential predictor variables using the preliminary univariate analyses, the multivariate logistic regression with best subset selection approach was conducted to identify best combination of determinants to predict unplanned emergency department utilizations.
Results: Best subset selection regression analysis showed Charlson Comorbidity Index (odds ratio (OR)=1.33, 95% CI=1.05 to 1.70), male caregiver (OR=0.18, 95% CI=0.05 to 0.66), duration of introducing homecare services (OR=0.97, 95% CI=0.95 to 1.00), working experience of dedicated nurses (OR=0.89, 95% CI=0.79 to 0.99) and number of emergency department utilizations within previous past year before enrollment (OR=1.54, 95% CI=1.14 to 2.10) as significant determinants for unplanned emergency department visits.
Conclusions: The present evidence may help government agencies propose supportive policies to improve access to integrated homecare resources and promote appropriate care recommendations to reduce unplanned or nonurgent emergency department visits among patients receiving homecare services.
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http://dx.doi.org/10.34172/ijhpm.2023.7377 | DOI Listing |
Am J Emerg Med
January 2025
Department of Emergency Medicine, Yale University School of Medicine, New Haven, CT, USA; Center for Outcomes Research and Evaluation, Yale University, New Haven, CT, USA.
Background: This study aimed to examine how physician performance metrics are affected by the speed of other attendings (co-attendings) concurrently staffing the ED.
Methods: A retrospective study was conducted using patient data from two EDs between January-2018 and February-2020. Machine learning was used to predict patient length of stay (LOS) conditional on being assigned a physician of average speed, using patient- and departmental-level variables.
Am J Emerg Med
January 2025
Faculty of Medicine, Universidad de Valladolid, Valladolid, Spain; Emergency Department, Hospital Clínico Universitario, Gerencia Regional de Salud de Castilla y León, Valladolid, Spain.
Background: The study of the inclusion of new variables in already existing early warning scores is a growing field. The aim of this work was to determine how capnometry measurements, in the form of end-tidal CO2 (ETCO2) and the perfusion index (PI), could improve the National Early Warning Score (NEWS2).
Methods: A secondary, prospective, multicenter, cohort study was undertaken in adult patients with unselected acute diseases who needed continuous monitoring in the emergency department (ED), involving two tertiary hospitals in Spain from October 1, 2022, to June 30, 2023.
JMIR AI
January 2025
Department of Radiology, Children's National Hospital, Washington, DC, United States.
Clin Infect Dis
January 2025
Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Infectious Diseases, Respiratory Medicine and Critical Care, Berlin, Germany.
Background: Existing risk evaluation tools underperform in predicting intensive care unit (ICU) admission for patients with the Coronavirus Disease 2019 (COVID-19). This study aimed to develop and evaluate an accurate and calculator-free clinical tool for predicting ICU admission at emergency room (ER) presentation.
Methods: Data from patients with COVID-19 in a nationwide German cohort (March 2020-January 2023) were analyzed.
PLoS One
January 2025
Animal and Human Health Department, International Livestock Research Institute, Nairobi, Kenya.
Non-conformance with antibiotic withdrawal period guidelines represents a food safety concern, with potential for antibiotic toxicities and allergic reactions as well as selecting for antibiotic resistance. In the Kenyan domestic pig market, conformance with antibiotic withdrawal periods is not a requirement of government legislation and evidence suggests that antibiotic residues may frequently be above recommended limits. In this study, we sought to explore enablers of and barriers to conformance with antibiotic withdrawal periods for pig farms supplying a local independent abattoir in peri-urban Nairobi.
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