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Objective: Integrating family-reported safety data into hospitals' operational safety reporting systems could enrich them, but requires understanding how reports would be classified. We sought to evaluate how family safety reports would be classified in an operational system and compare classifications with a newer research taxonomy.
Design/methods: We prospectively collected safety reports from English and Spanish-speaking families of children hospitalized in a pediatric quaternary hospital's complex care service. Three physicians scored reports using research (modified Bates and colleagues and NCC-MERP) and operational taxonomies. In total, 10% of reports were reviewed independently to determine interrater reliability [kappa (κ)].
Results: In total, 132 families provided 289 reports. Research κ (% agreement) was 0.40 (52.0%) for safety classification and 0.58 (68.0%) for NCC-MERP category. Operational κ was 0.46 (62.5%) for severity. κ for preventability, a shared category across operational and research taxonomies, was 0.53 (76.9%). Using operational taxonomy, reports were commonly classified as medications and fluids (29.8%, n=86), severity level 1 (no harm; 34.6%, n=100), with 34.9% (n=101) deemed unclassifiable. Using research taxonomy, reports were most commonly medicine/IV fluids (36.3%, n=105), nonharmful errors (38.4%, n=111), non-safety-related quality (30.8%, n=89), and NCC-MERP C (29.8%, n=86). 63% (n=182) were possibly preventable/preventable.
Conclusions: Operational and research taxonomies classify family-reported safety events similarly, though many are nonclassifiable in the operational taxonomy. Research taxonomy characterized family-reported concerns, including quality and environmental hazards, highlighting important aspects that operational systems do not capture. Hospitals and researchers should include family-reported data, and operational systems could add research categories to better capture safety and quality information from families.
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http://dx.doi.org/10.1097/PTS.0000000000001368 | DOI Listing |
Am J Emerg Med
September 2025
University of Toronto, Rotman School of Management, Canada.
Study Objective: Accurately predicting which Emergency Department (ED) patients are at high risk of leaving without being seen (LWBS) could enable targeted interventions aimed at reducing LWBS rates. Machine Learning (ML) models that dynamically update these risk predictions as patients experience more time waiting were developed and validated, in order to improve the prediction accuracy and correctly identify more patients who LWBS.
Methods: The study was deemed quality improvement by the institutional review board, and collected all patient visits to the ED of a large academic medical campus over 24 months.
BJS Open
September 2025
Department of General, Visceral and Thoracic Surgery, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
Clin Transl Gastroenterol
September 2025
Department of Internal Medicine, School of Medicine, University of Medicine and Pharmacy at Ho Cho Minh City, Vietnam.
Background: Severe acute pancreatitis (SAP) is a life-threatening condition requiring early risk stratification. While the Bedside Index for Severity in Acute Pancreatitis (BISAP) is widely used, its reliance on complex parameters limits its applicability in resource-constrained settings. This study introduces a decision tree model based on Classification and Regression Tree (CART) analysis, utilizing Neutrophil-to-Lymphocyte Ratio (NLR) and C-reactive Protein (CRP), as a simpler alternative for early SAP prediction.
View Article and Find Full Text PDFInterv Neuroradiol
September 2025
Department of Neurological Surgery, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Lake Success, New York, USA.
BackgroundEndovascular coil embolization is a common treatment for intracranial aneurysms, but aneurysm recanalization remains a significant problem that may necessitate retreatment. This study aimed to identify patient, aneurysm, and procedural factors associated with recanalization in aneurysms treated exclusively with coil embolization.MethodsThis single center retrospective study assessed intracranial aneurysms treated with coiling-only between 2017 and 2022.
View Article and Find Full Text PDFIEEE Trans Med Imaging
September 2025
Mammography is a primary method for early screening, and developing deep learning-based computer-aided systems is of great significance. However, current deep learning models typically treat each image as an independent entity for diagnosis, rather than integrating images from multiple views to diagnose the patient. These methods do not fully consider and address the complex interactions between different views, resulting in poor diagnostic performance and interpretability.
View Article and Find Full Text PDF