Introduction: Emergency medicine (EM) programs train residents to perform clinical procedures with known iatrogenic risks. Currently, there is no established framework for graduating medical students to demonstrate procedural competency prior to matriculating into residency. Mastery-based learning has demonstrated improved patient-safety outcomes. Incorporation of this framework allows learners to demonstrate procedural competency to a predetermined standard in the simulation laboratory prior to performing invasive procedures on patients in the clinical setting. This study describes the creation and implementation of a competency-based procedural curriculum for first-year EM residents using simulation to prepare learners for supervised participation in procedures during patient care.
Methods: Checklists were developed internally for five high-risk procedures (central venous line placement, endotracheal intubation, lumbar puncture, paracentesis, chest tube placement). Performance standards were developed using Mastery-Angoff methods. Minimum passing scores were determined for each procedure. Over a two-year period, 38 residents underwent baseline assessment, deliberate practice, and post-testing against the passing standard score to demonstrate procedural competency in the simulation laboratory during intern orientation.
Results: We found that 37% of residents required more than one attempt to achieve the minimum passing score on some procedures, however, all residents ultimately met the competency standard on all five high-risk procedures in simulation. One critical incident of central venous catheter guideline retention was identified in the simulation laboratory during the second year of implementation.
Conclusion: All incoming first-year EM residents demonstrated procedural competence on five different procedures using a mastery-based educational framework. A competency-based EM curriculum allowed for demonstration of procedural competence prior to resident participation in supervised clinical patient care.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9897246 | PMC |
http://dx.doi.org/10.5811/westjem.2022.11.58057 | DOI Listing |
Invest Radiol
October 2024
From the Institute for Diagnostic and Interventional Radiology, University Hospital Zurich, University Zurich, Zurich, Switzerland (B.K., F.E., J.K., T.F., L.J.); Advanced Radiology Center, Department of Diagnostic Imaging and Oncological Radiotherapy, Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Rome, Italy (C.S., A.R.L.); and Section of Radiology, Department of Radiological and Hematological Sciences, Università Cattolica del Sacro Cuore, Rome, Italy (A.R.L.).
Objectives: The aim of this study was to evaluate the feasibility and efficacy of visual scoring, low-attenuation volume (LAV), and deep learning methods for estimating emphysema extent in x-ray dose photon-counting detector computed tomography (PCD-CT), aiming to explore future dose reduction potentials.
Methods: One hundred one prospectively enrolled patients underwent noncontrast low- and chest x-ray dose CT scans in the same study using PCD-CT. Overall image quality, sharpness, and noise, as well as visual emphysema pattern (no, trace, mild, moderate, confluent, and advanced destructive emphysema; as defined by the Fleischner Society), were independently assessed by 2 experienced radiologists for low- and x-ray dose images, followed by an expert consensus read.
Med Phys
December 2024
School of Physics and Optoelectronic Engineering, Foshan University, Foshan, China.
Background: In clinical practices, doctors usually need to synthesize several single-modality medical images for diagnosis, which is a time-consuming and costly process. With this background, multimodal medical image fusion (MMIF) techniques have emerged to synthesize medical images of different modalities, providing a comprehensive and objective interpretation of the lesion.
Purpose: Although existing MMIF approaches have shown promising results, they often overlook the importance of multiscale feature diversity and attention interaction, which are essential for superior visual outcomes.
Med Phys
December 2024
Department of Physics, Lakehead University, Thunder Bay, Ontario, Canada.
Background: This study investigates a multi-angle acquisition method aimed at improving image quality in organ-targeted PET detectors with planar detector heads. Organ-targeted PET technologies have emerged to address limitations of conventional whole-body PET/CT systems, such as restricted axial field-of-view (AFOV), limited spatial resolution, and high radiation exposure associated with PET procedures. The AFOV in organ-targeted PET can be adjusted to the organ of interest, minimizing unwanted signals from other parts of the body, thus improving signal collection efficiency and reducing the dose of administered radiotracer.
View Article and Find Full Text PDFJ Med Imaging Radiat Oncol
December 2024
Department of Radiology, Grampians Health, Ballarat Central, Victoria, Australia.
Background: CT-guided percutaneous transthoracic needle biopsy is the primary method for diagnosing lung lesions. Widely accepted validated risk prediction models are yet to be developed. A recently published study conducted at Grampians Health Services (GHS) developed two risk prediction models for predicting pneumothorax and intercostal catheter (ICC) insertion.
View Article and Find Full Text PDFJ Neurosurg
December 2024
1Department of Neurological Surgery, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama.
Objective: The extent of resection (EOR) is an important prognostic factor for both low- and high-grade gliomas. Intraoperative MRI (iMRI) has been used to increase the EOR in glioma surgery. While a recent study reported differences between iMRI and early postoperative MRI (epMRI), their specific relationship to postoperative clinical symptoms remains unclear.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!