Background: Human errors are the underlying cause of many occupational accidents. In recent years, human errors have increased in the healthcare sector.
Aim: This study aimed to identify human errors committed by emergency department (ED) nurses working at Shahid Beheshti Hospital in Kashan using the SHERPA method.
Method: This study is a descriptive cross-sectional study performed in the emergency department of Shahid Beheshti Hospital. Human errors were first identified and analyzed using the Hierarchical Task Analysis (HTA) technique and then studied using the SHERPA method.
Results: In total, 426 errors were identified including 263 action errors, 108 checking errors, 35 selection errors, 12 retrieval errors, and eight communication errors. Also, based on the levels presented in the risk matrix in terms of severity of consequences, the highest percentage (36.34%) belonged to the borderline category.
Conclusion: The majority of identified errors were action errors, which can be reduced by providing appropriate instructions and training nurses, compiling reports and building error recording systems, improving management controls, and promoting a safety culture.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1016/j.ienj.2022.101159 | DOI Listing |
Viruses
November 2024
Laboratório de Imunofarmacologia, Instituto Oswaldo Cruz (IOC), Fundação Oswaldo Cruz (Fiocruz), Rio de Janeiro 21040-361, RJ, Brazil.
Coronavirus disease 2019 (COVID-19) still causes death in elderly and immunocompromised individuals, for whom the sustainability of the vaccine response may be limited. Antiviral treatments, such as remdesivir or molnupiravir, have demonstrated limited clinical efficacy. Nirmatrelvir, an acute respiratory syndrome coronavirus 2 (SARS-CoV-2) major protease inhibitor, is clinically effective but has been associated with viral rebound and antiviral resistance.
View Article and Find Full Text PDFSensors (Basel)
December 2024
Department of Mechanical Engineering, Brigham Young University, Provo, UT 84602, USA.
Flexible high-deflection strain gauges have been demonstrated to be cost-effective and accessible sensors for capturing human biomechanical deformations. However, the interpretation of these sensors is notably more complex compared to conventional strain gauges, particularly during dynamic motion. In addition to the non-linear viscoelastic behavior of the strain gauge material itself, the dynamic response of the sensors is even more difficult to capture due to spikes in the resistance during strain path changes.
View Article and Find Full Text PDFSensors (Basel)
December 2024
School of Electronic Information Engineering, Taiyuan University of Science and Technology, Taiyuan 030024, China.
Human pose estimation is an important research direction in the field of computer vision, which aims to accurately identify the position and posture of keypoints of the human body through images or videos. However, multi-person pose estimation yields false detection or missed detection in dense crowds, and it is still difficult to detect small targets. In this paper, we propose a Mamba-based human pose estimation.
View Article and Find Full Text PDFSensors (Basel)
December 2024
Department of Electrical Engineering, Center for Innovative Research on Aging Society (CIRAS), Advanced Institute of Manufacturing with High-Tech Innovations (AIM-HI), National Chung Cheng University, Chia-Yi 621, Taiwan.
In computer vision, accurately estimating a 3D human skeleton from a single RGB image remains a challenging task. Inspired by the advantages of multi-view approaches, we propose a method of predicting enhanced 2D skeletons (specifically, predicting the joints' relative depths) from multiple virtual viewpoints based on a single real-view image. By fusing these virtual-viewpoint skeletons, we can then estimate the final 3D human skeleton more accurately.
View Article and Find Full Text PDFSensors (Basel)
December 2024
Postgraduate Program in Biotechnology, Parnaíba Delta Federal University, Parnaíba 64202-020, Brazil.
Human Pose Estimation (HPE) is a computer vision application that utilizes deep learning techniques to precisely locate Key Joint Points (KJPs), enabling the accurate description of a person's pose. HPE models can be extended to facilitate Range of Motion (ROM) assessment by leveraging patient photographs. This study aims to evaluate and compare the performance of HPE models for assessing upper limbs ROM.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!