AIMC Topic: Emergency Service, Hospital
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Evaluating LLM-based generative AI tools in emergency triage: A comparative study of ChatGPT Plus, Copilot Pro, and triage nurses.
The American journal of emergency medicine
Dec 19, 2024
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 accur...
Enhanced forecasting of emergency department patient arrivals using feature engineering approach and machine learning.
BMC medical informatics and decision making
Dec 18, 2024
BACKGROUND: Emergency department (ED) overcrowding is an important problem in many countries. Accurate predictions of ED patient arrivals can help management to better allocate staff and medical resources. In this study, we investigate the use of cal...
Using machine learning and natural language processing in triage for prediction of clinical disposition in the emergency department.
BMC emergency medicine
Dec 18, 2024
BACKGROUND: Accurate triage is required for efficient allocation of resources and to decrease patients' length of stay. Triage decisions are often subjective and vary by provider, leading to patients being over-triaged or under-triaged. This study de...
SMART: Development and Application of a Multimodal Multi-organ Trauma Screening Model for Abdominal Injuries in Emergency Settings.
Academic radiology
Dec 16, 2024
RATIONALE AND OBJECTIVES: Effective trauma care in emergency departments necessitates rapid diagnosis by interdisciplinary teams using various medical data. This study constructed a multimodal diagnostic model for abdominal trauma using deep learning...
Leveraging artificial intelligence to reduce diagnostic errors in emergency medicine: Challenges, opportunities, and future directions.
Academic emergency medicine : official journal of the Society for Academic Emergency Medicine
Dec 15, 2024
Diagnostic errors in health care pose significant risks to patient safety and are disturbingly common. In the emergency department (ED), the chaotic and high-pressure environment increases the likelihood of these errors, as emergency clinicians must ...
Off-console automated artificial intelligence enhanced workflow enables improved emergency department CT capacity.
Emergency radiology
Dec 11, 2024
PURPOSE: Increasing CT capacity to keep pace with rising ED demand is critical. The conventional process has inherent drawbacks. We evaluated an off-console automated AI enhanced workflow which moves all final series creation off-console. We hypothes...
Preliminary findings regarding the association between patient demographics and ED experience scores across a regional health system: A cross sectional study using natural language processing of patient comments.
International journal of medical informatics
Dec 10, 2024
OBJECTIVE: Existing literature shows associations between patient demographics and reported experiences of care, but this relationship is poorly understood. Our objective was to use natural language processing of patient comments to gain insight into...
Early prediction of intensive care unit admission in emergency department patients using machine learning.
Australian critical care : official journal of the Confederation of Australian Critical Care Nurses
Dec 5, 2024
BACKGROUND: The timely identification and transfer of critically ill patients from the emergency department (ED) to the intensive care unit (ICU) is important for patient care and ED workflow practices.
Integrating structured and unstructured data for predicting emergency severity: an association and predictive study using transformer-based natural language processing models.
BMC medical informatics and decision making
Dec 5, 2024
BACKGROUND: Efficient triage in emergency departments (EDs) is critical for timely and appropriate care. Traditional triage systems primarily rely on structured data, but the increasing availability of unstructured data, such as clinical notes, prese...
Performance of machine learning versus the national early warning score for predicting patient deterioration risk: a single-site study of emergency admissions.
BMJ health & care informatics
Dec 4, 2024
OBJECTIVES: Increasing operational pressures on emergency departments (ED) make it imperative to quickly and accurately identify patients requiring urgent clinical intervention. The widespread adoption of electronic health records (EHR) makes rich fe...
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