This article shows how Barcoded Medication Administration technology institutionally organizes and rules the daily actions of nurses. Although it is widely assumed that Barcoded Medication Administration technology improves quality and safety by reducing the risk of human error, little research has been done on how this technology alters the work of nurses. Drawing on empirical and conceptual strategies of analysis, this qualitative study used certain tools of institutional ethnography to provide a view of how nurses negotiate Barcoded Medication Administration technology. The approach also uses elements from practice theory in order to discern how technology operates as a player on the field instead of being viewed as a 'mere' tool. A literature review preceded participant observation, whereby 17 nurses were followed and data on an orthopaedic ward were collected over a period of 9 months in 2011 and 2012. Barcoded Medication Administration technology relies on nurses' knowledge to mediate between the embedded logics of its design and the unpredictable needs of patients. Nurses negotiate their own professional logic of care in the form of moment-to-moment deliberations which subvert the ruling frame of the barcoded system and its objectified model of patient safety. The logic of Barcoded Medication Administration technology differs from the logic of nursing care, as this technology presumes medication distribution to be linear, even though nurses follow another line of actor-bound safety practices that we characterize as 'deliberations'.
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http://dx.doi.org/10.1177/1363459318800155 | DOI Listing |
Int J Lab Hematol
January 2025
Computer Vision Institute, College of Computer Science and Software, Shenzhen University, China.
Background: Acute lymphoblastic leukemia (ALL) is a leading cause of death among pediatric malignancies. Early diagnosis of ALL is crucial for minimizing misdiagnosis, improving survival rates, and ensuring the implementation of precise treatment plans for patients.
Methods: In this study, we propose a multi-modal deep neural network-based framework for early and efficient screening of ALL.
Risk Manag Healthc Policy
January 2025
Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, New Taipei City, 235603, Taiwan.
Purpose: As HF progresses into advanced HF, patients experience a poor quality of life, distressing symptoms, intensive care use, social distress, and eventual hospital death. We aimed to investigate the relationship between morality and potential prognostic factors among in-patient and emergency patients with HF.
Patients And Methods: A case series study: Data are collected from in-hospital and emergency care patients from 2014 to 2021, including their international classification of disease at admission, and laboratory data such as blood count, liver and renal functions, lipid profile, and other biochemistry from the hospital's electrical medical records.
Zhongguo Zhong Yao Za Zhi
December 2024
Experimental Research Center, China Academy of Chinese Medical Sciences Beijing 100700, China.
The chloroplast genome is an important tool for studying plant classification, evolution, and the heterologous production of secondary metabolites and protein drugs. With advancements in sequencing technology and reductions in sequencing costs, chloroplast genome data have rapidly accumulated. However, existing chloroplast genome databases suffer from issues such as incomplete data, inadequate management, and inconsistent, inaccurate information, posing significant challenges for the development and utilization of the chloroplast genome.
View Article and Find Full Text PDFZhongguo Zhong Yao Za Zhi
December 2024
State Key Laboratory of Chinese Medicine Modernization, Tianjin University of Traditional Chinese Medicine Tianjin 301617, China.
Artemisiae Scoporiae Herba is derived from Artemisia scoparia or A. capillaris. The accurate identification of the herbs, particularly when dealing with bulk samples, is critical for ensuring the quality and efficacy of the medicinal product.
View Article and Find Full Text PDFRespiratory diseases pose a significant global health burden, with challenges in early and accurate diagnosis due to overlapping clinical symptoms, which often leads to misdiagnosis or delayed treatment. To address this issue, we developed , an artificial intelligence (AI)-based diagnostic system that utilizes natural language processing (NLP) to extract key clinical features from electronic health records (EHRs) for the accurate classification of respiratory diseases. This study employed a large cohort of 31,267 EHRs from multiple centers for model training and internal testing.
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