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...
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...
Our purpose was to evaluate the approach of two different chatbots (ChatGPT and Gemini) to a list of questions about emergency department as a border area between hospital and territory. This study was performed in a single day, on 3 March 2024. Two ...
Academic emergency medicine : official journal of the Society for Academic Emergency Medicine
39676165
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 ...
International journal of medical informatics
39671851
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...
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...
Australian critical care : official journal of the Confederation of Australian Critical Care Nurses
39643547
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.
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...
BMC medical informatics and decision making
39633370
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...
OBJECTIVE: Natural language processing (NLP) can enhance research studies for febrile infants by more comprehensive cohort identification. We aimed to refine and validate an NLP algorithm to identify and extract quantified temperature measurements fr...