Objective: Developments in radiation oncology in recent years have highlighted the increasing deployment of personnel resources for tasks not directly related to patients. These tasks include patient-related activities such as treatment planning, reviewing files, and administrative duties (e.g., invoicing for services, documentation). The aim of the present study, part of the QUIRO project of the German Society of Radiation Oncology (DEGRO), was to describe, on the basis of valid data, the deployment of personnel resources in radiation oncology centers for "overhead" tasks.
Methods: Questionnaires were used to analyze the percentages of time needed for various tasks. The target group comprised physicians, medical physics experts (MPE), and medical technical radiology assistants (MTRA). A total of 760 personnel from 65 radio-oncology centers in the German inpatient and outpatient sector participated (32 % physicians, 23 % MPE, and 45 % MTRA).
Results: High percentages of overhead tasks during working time were measured for each of the three personnel groups considered (physicians, MPE, and MTRA). Patient-related efficiency, i.e., the percentage of working time associated directly or indirectly with the patient, was highest among MTRA and lowest among MPE. Particular features could be seen in the activity profiles of personnel in university clinics. Duties in the areas of research and teaching resulted in a greater percentage of overhead tasks for physicians and MPE. Irrespective of function (physician, MPE, or MTRA), a managerial role resulted in lower patient-related efficiency, as well as a narrower time budget for direct patient care compared with non-managerial employees.
Conclusion: Using the data gathered, it was possible to systematically investigate the time required for overhead tasks in radio-oncological centers. Overall, relatively high time requirements for a variety of overhead tasks were measured. These time requirements, generated for example by administrative duties or research and teaching, are currently not taken into adequate consideration in terms of remuneration or personnel capacity planning.
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http://dx.doi.org/10.1007/s00066-014-0758-2 | DOI Listing |
BMC Med Inform Decis Mak
December 2024
Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, Kasteelpark Arenberg 10, Leuven, 3001, Belgium.
Background: Modern machine learning and deep learning methods have been widely incorporated in decision making processes in healthcare in the form of decision support mechanisms. In healthcare, data are abundant but typically not centrally available and, therefore, require some form of aggregation to facilitate training procedures. Aggregating sensitive data poses a significant privacy risk, which is why, both in Europe and the United States, legal frameworks regulate the treatment of such data.
View Article and Find Full Text PDFPeerJ Comput Sci
August 2024
Department of Computer Programming, Acibadem University, Istanbul, Turkey.
Text classification tasks, particularly those involving a large number of features, pose significant challenges in effective feature selection. This research introduces a novel methodology, MBO-NB, which integrates Migrating Birds Optimization (MBO) approach with naïve Bayes as an internal classifier to address these challenges. The motivation behind this study stems from the recognized limitations of existing techniques in efficiently handling extensive feature sets.
View Article and Find Full Text PDFNeural Netw
November 2024
Tencent Group, China. Electronic address:
Multi-scenario and multi-task learning are crucial in industrial recommendation systems to deliver high-quality recommendations across diverse scenarios with minimal computational overhead. However, conventional models often fail to effectively leverage cross-scenario information, limiting their representational capabilities. Additionally, multi-step conversion tasks in real-world applications face challenges from sequential dependencies and increased data sparsity, particularly in later stages.
View Article and Find Full Text PDFSci Rep
December 2024
Department of computer science and applications, Maharshi Dayanand University, Rohtak, India.
Background: The superior labrum and biceps complex is commonly implicated in shoulder pain and there remains discordance regarding the surgical management of superior labrum anterior to posterior (SLAP) tears. The purpose of this study was to establish an expert consensus regarding the management of superior labrum and biceps complex pathology.
Methods: The NEER Circle is an organization of shoulder experts recognized for their service to the American Shoulder and Elbow Surgeons (ASES) society.
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