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Background: Information stored within electronic health records is often recorded as unstructured text. Special computerized natural language processing (NLP) tools are needed to process this text; however, complex governance arrangements make such data in the National Health Service hard to access, and therefore, it is difficult to use for research in improving NLP methods. The creation of a donated databank of clinical free text could provide an important opportunity for researchers to develop NLP methods and tools and may circumvent delays in accessing the data needed to train the models. However, to date, there has been little or no engagement with stakeholders on the acceptability and design considerations of establishing a free-text databank for this purpose.
Objective: This study aimed to ascertain stakeholder views around the creation of a consented, donated databank of clinical free text to help create, train, and evaluate NLP for clinical research and to inform the potential next steps for adopting a partner-led approach to establish a national, funded databank of free text for use by the research community.
Methods: Web-based in-depth focus group interviews were conducted with 4 stakeholder groups (patients and members of the public, clinicians, information governance leads and research ethics members, and NLP researchers).
Results: All stakeholder groups were strongly in favor of the databank and saw great value in creating an environment where NLP tools can be tested and trained to improve their accuracy. Participants highlighted a range of complex issues for consideration as the databank is developed, including communicating the intended purpose, the approach to access and safeguarding the data, who should have access, and how to fund the databank. Participants recommended that a small-scale, gradual approach be adopted to start to gather donations and encouraged further engagement with stakeholders to develop a road map and set of standards for the databank.
Conclusions: These findings provide a clear mandate to begin developing the databank and a framework for stakeholder expectations, which we would aim to meet with the databank delivery.
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http://dx.doi.org/10.2196/45534 | DOI Listing |
Int J Lang Commun Disord
September 2025
Cumbria, Northumberland, Tyne and Wear NHS Foundation Trust, Newcastle upon Tyne, UK.
Background: Research Capacity and Culture (RCC) is important for research engagement. Little is known of what speech and language therapy staff perceives to be the barriers or enablers to this at individual, team and organisational levels.
Aims: To identify the barriers and enablers to RCC among speech and language therapy staff, using behaviour change theory as a framework, and to explore their self-reported level of research engagement.
Eur Heart J Open
September 2025
Calderdale and Huddersfield NHS Foundation Trust, Acre St, Lindley, Huddersfield HD3 3EA, UK.
Aims: Cardiogenic shock remains a significant cause of mortality despite multiple advancements in medical interventions. Veno-arterial extracorporeal membrane oxygenation (VA-ECMO) provides crucial circulatory support but also increases left ventricular (LV) after-load, potentially worsening outcomes. Effective LV unloading strategies can enhance patient survival during VA-ECMO treatment.
View Article and Find Full Text PDFClin Pharmacol
September 2025
Department of Pharmacology and Clinical Pharmacy, Faculty of Pharmacy, Universitas Padjadjaran, Sumedang, Indonesia.
Red yeast rice (RYR) is an Asian indigenous medicine that ferments grains using the Monascus fungi, specifically . Monacolins, pigments, phenols, sterols, and benzopyrans, such as the mycotoxin citrinin, were proven to be present in RYR, contributing to its numerous effects. This study aims to provide a thorough overview of the in vitro and in vivo pharmacological activities of red yeast rice, its studies in humans, and a summary of recent case reports.
View Article and Find Full Text PDFFront Big Data
August 2025
MaiNLP, Center for Information and Language Processing, LMU Munich, Munich, Germany.
Predicting career trajectories is a complex yet impactful task, offering significant benefits for personalized career counseling, recruitment optimization, and workforce planning. However, effective career path prediction (CPP) modeling faces challenges including highly variable career trajectories, free-text resume data, and limited publicly available benchmark datasets. In this study, we present a comprehensive comparative evaluation of CPP models-linear projection, multilayer perceptron (MLP), LSTM, and large language models (LLMs)-across multiple input settings and two recently introduced public datasets.
View Article and Find Full Text PDFJ Obstet Gynecol Neonatal Nurs
September 2025
Objective: To examine the association between patient disability status and use of stigmatizing language in clinical notes from the hospital admission for birth.
Design: Cross-sectional study of electronic health record data.
Setting: Two urban hospitals in the northeastern United States.