Background: Patients allergic to antibiotics are at higher risk of receiving treatment with a broader spectrum, more harmful, and expensive agents. The aims of this study were (1) to assess the quality of documentation of antibiotics allergies in the electronic medical records (EMR) in a Pediatric tertiary care setting, and (2) to determine the validity of physicians' decision to hold antibiotics prescriptions.
Methods: This is a retrospective cohort study at King Abdullah Specialized Children Hospital, Riyadh, Saudi Arabia. A review of the EMR and all Adverse Drug Reaction (ADR) reports of pediatric patients 1-14 years old, with a documented allergy to antibiotics from June 2016 until June 2019. The quality of documentation of antibiotics allergy was assessed based on the presence of four parameters: 1) allergy alert notification, 2) allergy severity classification, 3) setting notes, and 4) symptoms' description. In addition, all physicians' reports of allergy to antibiotics were cross-classified according to their corresponding ADR reports, and the validity of physicians' documentation of allergy was assessed.
Results: Of a total of 105 Pediatric patients' EMR, documentation of antibiotics allergy was available in 98 (93.3%), with the presence of symptoms description (83%), allergy notes (87%), severity (67%), and signs of alert (50.8%). Overall documentation quality was good for only 23.5% of patients, while it was poor for 35.7%. Physicians' documentation of antibiotics allergy was 0.82 sensitive [with 0.18 risk of allergy] and 0.60 specific [with 0.40 unnecessary restrictions of prescriptions]. Of all children with possible/actual allergies, only 38.9% were referred to the immunology clinic.
Conclusion: The quality of documentation of antibiotic allergy in children and the validity of physicians' decisions are less than satisfactory. Therefore, improving communications between all healthcare providers regarding patients' allergy status and follow-up for further assessment of the reaction is recommended to improve patient care.
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http://dx.doi.org/10.2147/IJGM.S341629 | DOI Listing |
J Am Med Inform Assoc
March 2025
Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, ON M5C 3G7, Canada.
Objectives: Electronic health records (EHRs) data are increasingly used for research and analysis, but there is little empirical evidence to inform how automated and manual assessments can be combined to efficiently assess data quality in large EHR repositories.
Materials And Methods: The GEMINI database collected data from 462 226 patient admissions across 32 hospitals from 2021 to 2023. We report data quality issues identified through semi-automated and manual data quality assessments completed during the data collection phase.
J Educ Eval Health Prof
March 2025
Department of Medical Education, Chonnam National University Medical School, Gwangju, Korea.
Purpose: The revised Clinical Skills Test (CST) of the Korean Medical Licensing Exam aims to provide a better assessment of physicians' clinical competence and ability to interact with patients. This study examined the impact of the revised CST on medical education curricula and resources nationwide, while also identifying areas for improvement within the revised CST.
Methods: This study surveyed faculty responsible for clinical clerkships at 40 medical schools throughout Korea to evaluate the status and changes in clinical skills education, assessment, and resources related to the CST.
Healthcare (Basel)
February 2025
Department of Medicine, Division of General Internal Medicine, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA.
Background/objectives: High levels of burnout among healthcare professionals and trainees represent a global problem with identified profound impacts. The collection of national data for better characterization of this problem can guide more needs-sensitive targeted interventions. We aimed to identify the prevalence of burnout, the associated factors, and their impacts among trainees of Saudi postgraduate healthcare professions training programs.
View Article and Find Full Text PDFInt J Mol Sci
March 2025
San Diego Supercomputer Center, University of California San Diego, La Jolla, CA 92093, USA.
Diagnostic practices for schizophrenia are unreliable due to the lack of a stable biomarker. However, machine learning holds promise in aiding in the diagnosis of schizophrenia and other neurological disorders. Dysregulated miRNAs were extracted from public sources.
View Article and Find Full Text PDFJ Cancer Surviv
March 2025
Department of Communication and Journalism at Texas A&M University, College Station, TX, USA.
Purpose: We evaluated healthcare providers' current knowledge, practices, and perspectives on a novel clinical decision tool (beta-version) to facilitate individualized exercise prescriptions and discussions in clinical settings.
Methods: We recruited healthcare providers who had treated or provided care to breast cancer survivors aged ≥ 35-years in the past 12 months. The participants were presented with a tool to provide individualized exercise recommendations considering women's individual, clinical, and contextual characteristics.
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