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Background: It is postulated that early determination of the need for admission can improve flow through EDs. There are several scoring systems which have been developed for predicting patient admission at triage, although they have not been directly compared. In addition, it is not known if these scoring systems perform better than clinical judgement. Therefore, the aim of this study was to validate existing tools in predicting hospital admission during triage and then compare them with the clinical judgement of triage nurses.
Methods: To conduct this prospective, single-centre observational study, we enrolled consecutive adult patients who presented between 30 September 2019 and 25 October 2019 at the ED of a large teaching hospital in Milan, Italy. For each patient, triage nurses recorded all of the variables needed to perform Ambulatory (AMB), Glasgow Admission Prediction (GAP) and Sydney Triage to Admission Risk Tool (START) scoring. The probability of admission was estimated by the triage nurses using clinical judgement and expressed as a percentage from 0 to 100 with intervals of 5. Nurse estimates were dichotomised for analysis, with ≥50% likelihood being a prediction of admission. Receiver operating characteristic curves were generated for accuracy of the predictions. Area under the curve (AUC) with 95% CI for each of the scores and for the nursing judgements was also calculated.
Results: A total of 1710 patients (844 men; median age, 54 years (IQR: 34-75)) and 35 nurses (15 men; median age, 37 years (IQR: 33-48)) were included in this study. Among these patients, 310 (18%) were admitted to hospital from the ED. AUC values for AMB, GAP and START scores were 0.77 (95% CI: 0.74 to 0.79), 0.72 (95% CI: 0.69 to 0.75) and 0.61 (95% CI: 0.58 to 0.64), respectively. The AUC for nurse clinical judgement was 0.86 (95% CI: 0.84 to 0.89).
Conclusion: AMB, GAP and START scores provided moderate accuracy in predicting patient admission. However, all of the scores were significantly worse than the clinical judgement of the triage nurses.
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http://dx.doi.org/10.1136/emermed-2020-210814 | DOI Listing |
SAGE Open Nurs
September 2025
Department of Public Health Nursing, School of Nursing and Midwifery, University of Ghana, Legon, Ghana.
Introduction: The world is in an era where healthcare professionals require training in soft skills to improve their caring ability. Regrettably, a concise compilation of nursing soft skills remains empirically unclassified.
Objectives: This study described a perceived list of soft skills necessary in nursing, as itemized by nurses and midwives in Ghana.
Int J Gen Med
September 2025
Betty and Guy Beatty Center for Integrated Research, Inova Health System, Falls Church, VA, USA.
Purpose: The diagnosis of post-acute SARS-CoV-2 infection (PASC) is broad, referring to new or persistent health problems >four weeks after being infected with SARSCoV-2. The aim of this study was to determine whether cytokines, chemokines or catecholamine levels could specify the clinical condition.
Patients And Methods: Seventy-nine participants participated in person to study PASC.
JAMIA Open
October 2025
Division of Pulmonary and Critical Care, Brigham and Women's Hospital, Boston, MA, United States.
Objectives: Unstructured data, such as procedure notes, contain valuable medical information that is frequently underutilized due to the labor-intensive nature of data extraction. This study aims to develop a generative artificial intelligence (GenAI) pipeline using an open-source Large Language Model (LLM) with built-in guardrails and a retry mechanism to extract data from unstructured right heart catheterization (RHC) notes while minimizing errors, including hallucinations.
Materials And Methods: A total of 220 RHC notes were randomly selected for pipeline development and 200 for validation from the Pulmonary Vascular Disease Registry.
Front Artif Intell
August 2025
Department of Biomedical Sciences, School of Health Sciences, State University of Rio Grande do Norte, Mossoró, Brazil.
Introduction: ChatGPT, a generative artificial intelligence, has potential applications in numerous fields, including medical education. This potential can be assessed through its performance on medical exams. Medical residency exams, critical for entering medical specialties, serve as a valuable benchmark.
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May 2025
Potentia Analytics Inc, IL, USA.
The shift toward patient-centric healthcare requires understanding comprehensive patient journeys. Current healthcare data systems often fail to provide holistic representations, hindering coordinated care. Patient Journey Knowledge Graphs (PJKGs) solve this by integrating diverse patient information into unified, structured formats.
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