Relationships from benefits-costs analyses in radiography are presented. The impact of several factors (equipment, frequency of examinations, ...) on the planning of a department and special financial aspects, e.g., leasing of equipment, are discussed. The cost relationships of conventional versus digital radiography are demonstrated for the example of a 1000-bed hospital.
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Surg Endosc
January 2025
Department of Visceral, Thoracic and Vascular Surgery, Faculty of Medicine, University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Fetscherstraße 74, 01307, Dresden, Germany.
Introduction: Simulator training is an efficient method for the development of basic laparoscopic skills. We aimed to investigate if low-cost simulators are comparable to more expensive box trainers regarding surgeons usability, likability, and performance.
Methods: This multi-center, randomized crossover study included 16 medical students, seven abdominal surgeons, and seven urological surgeons.
J Diabetes Sci Technol
January 2025
Spotlight Consultations Ltd, Portsmouth, UK.
Background And Aims: Burnout affects >50% of physicians and nurses. Spotlight-AQ is a personalized digital health platform designed to improve routine diabetes visits. We assessed cost-effectiveness, visit length, and association with health care professional (HCP) burnout.
View Article and Find Full Text PDFDigit Health
January 2025
Department of Neurology and Stroke Centre, Hospital La Paz Institute for Health Research-IdiPAZ (La Paz University Hospital-Autonomous University of Madrid), Madrid, Spain.
Introduction: New technologies could play a role in post-stroke aphasia (PSA). Our aims were to develop a digital tool; to evaluate its acceptance and usability by patients and caregivers; and to demonstrate its effectiveness in improving language skills in patients with PSA, applying it from the acute phase.
Methods: The study consisted of two phases: development of a digital tool; and an interventional before-and-after study.
BMC Cancer
January 2025
Department of Urology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, South Korea.
Background: To develop and test the performance of a fully automated system for classifying renal tumor subtypes via deep machine learning for automated segmentation and classification.
Materials And Methods: The model was developed using computed tomography (CT) images of pathologically proven renal tumors collected from a prospective cohort at a medical center between March 2016 and December 2020. A total of 561 renal tumors were included: 233 clear cell renal cell carcinomas (RCCs), 82 papillary RCCs, 74 chromophobe RCCs, and 172 angiomyolipomas.
Background: Given the global burden of substance use disorders (SUD), innovations in methods to achieve sustained recovery are critical. Digital health products (e.g.
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