Background: Choosing Wisely® is a national initiative to deimplement or reduce low-value care. However, there is limited evidence on the effectiveness of strategies to influence ordering patterns.
Objective: We aimed to describe the effectiveness of an intervention to reduce daily chest X-ray (CXR) ordering in two intensive care units (ICUs) and evaluate deimplementation strategies.
Design: We aimed to describe the effectiveness of an intervention to reduce daily chest X-ray (CXR) ordering in two intensive care units (ICUs) and evaluate deimplementation strategies.
Setting: The study was performed in the medical intensive care unit (MICU) and cardiovascular intensive care unit (CVICU) of an academic medical center in the United States from October 2015 to June 2016.
Participants: The initiative included the staff of the MICU and CVICU (physicians, surgeons, nurse practitioners, fellows, residents, medical students, and X-ray technologists).
Intervention Components: We utilized provider education, peer champions, and weekly data feedback of CXR ordering rates.
Measurements: We analyzed the CXR ordering rates and factors facilitating or inhibiting deimplementation.
Results: Segmented linear time-series analysis suggested a small but statistically significant decrease in CXR ordering rates in the CVICU (P < .001) but not in the MICU. Facilitators of deimplementation, which were more prominent in the CVICU, included engagement of peer champions, stable staffing, and regular data feedback. Barriers included the need to establish goal CXR ordering rates, insufficient intervention visibility, and waning investment among medical residents in the MICU due to frequent rotation and competing priorities.
Conclusions: Intervention modestly reduced CXRs ordered in one of two ICUs evaluated. Understanding why adoption differed between the two units may inform future interventions to deimplement low-value diagnostic tests.
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http://dx.doi.org/10.12788/jhm.3129 | DOI Listing |
J Med Radiat Sci
January 2025
Discipline of Medical Imaging Sciences, Faculty of Medicine and Health, University of Sydney, Camperdown, New South Wales, Australia.
Introduction: Quality assurance (QA) in medical imaging ensures consistently high-quality images at acceptable radiation doses. However, the applicability of the chest X-ray (CXR) QA tool in images with pathology, particularly infectious diseases like COVID-19, has not been explored. This study examines the utility of the European Guidelines for image quality in QA of CXRs with varying severity and types of infectious disease.
View Article and Find Full Text PDFComput Struct Biotechnol J
December 2024
Computer Science Dept., University of Turin, Italy.
In this paper, we present the significant results from the Covid Radiographic imaging System based on AI (Co.R.S.
View Article and Find Full Text PDFMed Biol Eng Comput
November 2024
Alberta Machine Intelligence Institute, University of Alberta, Edmonton, Canada.
Recent advancements in deep learning techniques have significantly improved multi-label chest X-ray (CXR) image classification for clinical diagnosis. However, most previous studies neither effectively learn label correlations nor take full advantage of them to improve multi-label classification performance. In addition, different labels of CXR images are usually severely imbalanced, resulting in the model exhibiting a bias towards the majority class.
View Article and Find Full Text PDFArch Pediatr
November 2024
Department of Pediatric, University Hospital of Caen, Caen, France.
Emerg Med Int
September 2024
Department of General Practice and Emergency Medicine Kathmandu University School of Medical Sciences, Dhulikhel, Kavrepalanchowk, Bagmati Province, Nepal.
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