Objective: To evaluate the impact of implemented work environment changes on nursing and support staff roles.
Background: In 1999, the authors identified key drivers of unnecessary work associated with the day-to-day delivery of patient care in their institution and implemented changes based on their results.
Methods: Both quantitative and qualitative methods were used. Work sampling and focus groups were used to evaluate work flow. Activity categories were identified and clearly defined by advanced practice nurses. All compiled data were subsequently synthesized and cross-checked with the information acquired through independent, multidisciplinary validation studies.
Results: There were significant changes (P <.0001) noted in overall distribution of observed activities for nurses and all support staff.
Conclusions: The significant changes noted in overall distribution of observed activities reflect the important adjustments made in both job descriptions and the environment to eliminate key drivers of unnecessary work in the delivery of patient care.
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http://dx.doi.org/10.1097/00005110-200405000-00008 | DOI Listing |
Med Phys
January 2025
OncoRay - National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden - Rossendorf, Dresden, Germany.
Background: Patient-specific quality assurance (PSQA) is a crucial yet resource-intensive task in proton therapy, requiring special equipment, expertise and additional beam time. Machine delivery log files contain information about energy, position and monitor units (MU) of all delivered spots, allowing a reconstruction of the applied dose. This raises the prospect of phantomless, log file-based QA (LFQA) as an automated replacement of current phantom-based solutions, provided that such an approach guarantees a comparable level of safety.
View Article and Find Full Text PDFJ Imaging Inform Med
January 2025
Leiden University Medical Center (LUMC), Leiden, the Netherlands.
Rising computed tomography (CT) workloads require more efficient image interpretation methods. Digitally reconstructed radiographs (DRRs), generated from CT data, may enhance workflow efficiency by enabling faster radiological assessments. Various techniques exist for generating DRRs.
View Article and Find Full Text PDFJ Imaging Inform Med
January 2025
Department of Radiology, University of Pennsylvania Perelman School of Medicine, 3400 Spruce St., Philadelphia, PA, 19104, USA.
Integration of artificial intelligence (AI) into radiology practice can create opportunities to improve diagnostic accuracy, workflow efficiency, and patient outcomes. Integration demands the ability to seamlessly incorporate AI-derived measurements into radiology reports. Common data elements (CDEs) define standardized, interoperable units of information.
View Article and Find Full Text PDFMed Phys
January 2025
Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, China.
Background: Online adaptive radiotherapy (OART) and rapid quality assurance (QA) are essential for effective heavy ion therapy (HIT). However, there is a shortage of deep learning (DL) models and workflows for predicting Monte Carlo (MC) doses in such treatments.
Purpose: This study seeks to address this gap by developing a DL model for independent MC dose (MCDose) prediction, aiming to facilitate OART and rapid QA implementation for HIT.
Discov Oncol
January 2025
Department of Otolaryngology-Head and Neck Surgery, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China.
The zinc finger protein 32 (ZNF32) has been associated with high expression in various cancers, underscoring its significant function in both cancer biology and immune response. To further elucidate the biological role of ZNF32 and identify potential immunotherapy targets in cancer, we conducted an in-depth analysis of ZNF32. We comprehensively investigated the expression of ZNF32 across tumors using diverse databases, including TCGA, CCLE, TIMER2.
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