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Minimising diagnostic error in paediatric critical care: a checklist approach.
Sudan J Paediatr
January 2024
Department of Medical Education, College of Medicine, King Saud bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia.
Caring for critically ill children presents unique challenges due to their rapid deterioration and the need for immediate, complex interventions. The assessment, diagnosis and treatment of deteriorating paediatric patients require a comprehensive and holistic, systematic approach. However, the dynamic nature of critical illness and the need for stabilisation can often lead to missed opportunities for assessment and intervention.
View Article and Find Full Text PDFSoftware solution for integration of frozen section quality assurance into daily practice.
Am J Clin Pathol
January 2025
Department of Pathology, Yale University School of Medicine, New Haven, CT, United States.
Objective: Diagnoses rendered using the frozen section (FS) technique during surgical procedures are used to guide intraoperative decisions. Therefore, diagnostic FS errors have the potential to affect patient safety and quality of care. Diagnostic FS errors arise due to both technical and interpretative factors and present a challenge to surgical pathology laboratories to recognize, document, and manage in a timely fashion.
View Article and Find Full Text PDFWearMoCap: multimodal pose tracking for ubiquitous robot control using a smartwatch.
Front Robot AI
January 2025
Interactive Robotics Laboratory, School of Computing and Augmented Intelligence (SCAI), Arizona State University (ASU), Tempe, AZ, United States.
We present WearMoCap, an open-source library to track the human pose from smartwatch sensor data and leveraging pose predictions for ubiquitous robot control. WearMoCap operates in three modes: 1) a Watch Only mode, which uses a smartwatch only, 2) a novel Upper Arm mode, which utilizes the smartphone strapped onto the upper arm and 3) a Pocket mode, which determines body orientation from a smartphone in any pocket. We evaluate all modes on large-scale datasets consisting of recordings from up to 8 human subjects using a range of consumer-grade devices.
View Article and Find Full Text PDFArtificial Intelligence Scribe and Large Language Model Technology in Healthcare Documentation: Advantages, Limitations, and Recommendations.
Plast Reconstr Surg Glob Open
January 2025
Department of Computer Science, Johns Hopkins University, Baltimore, MD.
Artificial intelligence (AI) scribe applications in the healthcare community are in the early adoption phase and offer unprecedented efficiency for medical documentation. They typically use an application programming interface with a large language model (LLM), for example, generative pretrained transformer 4. They use automatic speech recognition on the physician-patient interaction, generating a full medical note for the encounter, together with a draft follow-up e-mail for the patient and, often, recommendations, all within seconds or minutes.
View Article and Find Full Text PDFDeveloping an automatic treatment record review system for quality assurance of patient treatment delivery in radiation therapy.
Radiat Oncol
January 2025
Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China.
Background And Purpose: Treatment record contains most of information related to treatment plan delivery in radiation therapy. Reviewing treatment record is an important quality assurance (QA) task for safety and quality of patient treatments. This task is usually performed by senior medical physicists.
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