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Enhancing Medication Safety: Reducing Administration Errors in Oncology Setting.
Asian Pac J Cancer Prev
January 2025
Center Incharge, Sultan Qaboos Comprehensive Cancer Care and Research Centre (SQCCCRC), University Medical City, Muscat, Oman.
Purpose: This project aimed to minimize medication errors and improve safe medication administration in an oncology setting in Muscat, Oman.
Methods: The study, spanning from the second quarter of 2022 to the first quarter of 2023, employed a one-group pretest-posttest quasi-experimental design, assessing key performance indicators (medication error and medication administration errors rates per 1000 patient days) on quarterly basis before and after implementing targeted interventions. Interventions focused on medication management processes and Healthcare Informatics System (HIS), Environment and equipment, and Education The project utilized the FOCUS PDCA (find, organize, clarify, understand, select, plan, do, check and act) methodology.
Genomic profiling at a single center cracks the code in inborn errors of immunity.
Intern Emerg Med
January 2025
Unit of Internal Medicine and Clinical Oncology "G. Baccelli", Department of Precision and Regenerative Medicine and Ionian Area (DiMePRe-J), University of Bari Aldo Moro Medical School, Bari, Italy.
Inborn errors of immunity (IEI) entail a diverse group of disorders resulting from hereditary or de novo mutations in single genes, leading to immune dysregulation. This study explores the clinical utility of next-generation sequencing (NGS) techniques in diagnosing monogenic immune defects. Eight patients attending the immunodeficiency clinic and with unclassified antibody deficiency were included in the analysis.
View Article and Find Full Text PDFEvaluating robustly standardized explainable anomaly detection of implausible variables in cancer data.
J Am Med Inform Assoc
January 2025
Information Systems and Business Administration, Johannes Gutenberg University, Mainz 55128, Germany.
Objectives: Explanations help to understand why anomaly detection algorithms identify data as anomalous. This study evaluates whether robustly standardized explanation scores correctly identify the implausible variables that make cancer data anomalous.
Materials And Methods: The dataset analyzed consists of 18 587 truncated real-world cancer registry records containing 8 categorical variables describing patients diagnosed with bladder and lung tumors.
Estimating hypothetical estimands with causal inference and missing data estimators in a diabetes trial case study.
Biometrics
January 2025
Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London WC1E 7HT, United Kingdom.
The ICH E9 addendum on estimands in clinical trials provides a framework for precisely defining the treatment effect that is to be estimated, but says little about estimation methods. Here, we report analyses of a clinical trial in type 2 diabetes, targeting the effects of randomized treatment, handling rescue treatment and discontinuation of randomized treatment using the so-called hypothetical strategy. We show how this can be estimated using mixed models for repeated measures, multiple imputation, inverse probability of treatment weighting, G-formula, and G-estimation.
View Article and Find Full Text PDFLarge-Scale Validation of the Feasibility of GPT-4 as a Proofreading Tool for Head CT Reports.
Radiology
January 2025
From the Departments of Biomedical Systems Informatics (S.K., Jaewoong Kim, C.H., D.Y.) and Neurology (Joonho Kim, J.Y.), Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul 03722, Republic of Korea; Department of Radiology, Central Draft Physical Examination Office of Military Manpower Administration, Daegu, Republic of Korea (D.K.); Department of Radiology, Research Institute of Radiological Science and Center for Clinical Imaging Data Science (H.J.S. Y.K., S.J.), and Center for Digital Health (H.J.S., D.Y.), Yongin Severance Hospital, Yonsei University College of Medicine, Yongin, Republic of Korea; Department of Radiology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea (S.H.L.); Departments of Radiology (M.H.) and Neurology (S.J.L.), Ajou University Hospital, Ajou University School of Medicine, Suwon, Republic of Korea; and Institute for Innovation in Digital Healthcare, Severance Hospital, Seoul, Republic of Korea (D.Y.).
Background The increasing workload of radiologists can lead to burnout and errors in radiology reports. Large language models, such as OpenAI's GPT-4, hold promise as error revision tools for radiology. Purpose To test the feasibility of GPT-4 use by determining its error detection, reasoning, and revision performance on head CT reports with varying error types and to validate its clinical utility by comparison with human readers.
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