AIMC Topic: Emergency Service, Hospital
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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...
A novel deep learning algorithm for real-time prediction of clinical deterioration in the emergency department for a multimodal clinical decision support system.
Scientific reports
Dec 3, 2024
The array of complex and evolving patient data has limited clinical decision making in the emergency department (ED). This study introduces an advanced deep learning algorithm designed to enhance real-time prediction accuracy for integration into a n...
Machine Learning-based Prediction of Blood Stream Infection in Pediatric Febrile Neutropenia.
Journal of pediatric hematology/oncology
Dec 2, 2024
OBJECTIVES: This study aimed to develop machine learning (ML) prediction models for identifying bloodstream infection (BSI) and septic shock (SS) in pediatric patients with cancer who presenting febrile neutropenia (FN) at emergency department (ED) v...
Evaluating the Utility of ChatGPT in Diagnosing and Managing Maxillofacial Trauma.
The Journal of craniofacial surgery
Nov 28, 2024
Maxillofacial trauma is a significant concern in emergency departments (EDs) due to its high prevalence and the complexity of its management. However, many ED physicians lack specialized training and confidence in handling these cases, leading to a h...
An AI deep learning algorithm for detecting pulmonary nodules on ultra-low-dose CT in an emergency setting: a reader study.
European radiology experimental
Nov 20, 2024
BACKGROUND: To retrospectively assess the added value of an artificial intelligence (AI) algorithm for detecting pulmonary nodules on ultra-low-dose computed tomography (ULDCT) performed at the emergency department (ED).
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