Journal of evaluation in clinical practice
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Limited health literacy (HL) leads to poor health outcomes, psychological stress, and misutilization of medical resources. Although interventions aimed at improving HL may be effective, identifying patients at risk of limited HL in the clinical workflow is challenging. With machine learning (ML) algorithms based on readily available data, healthcare professionals would be enabled to incorporate HL screening without the need for administering in-person HL screening tools. ⋯ Elastic-Net Penalized Logistic Regression had the best performance when compared with other ML algorithms with a c-statistic of 0.766, calibration slope/intercept of 1.044/-0.037, and a Brier score of 0.179. Over one-third of patients presenting to an outpatient spine center were found to have limited HL. While this algorithm is far from being used in clinical practice, ML algorithms offer a potential opportunity for identifying patients at risk for limited HL without administering in-person HL assessments. This could possibly enable screening and early intervention to mitigate the potential negative consequences of limited HL without taxing the existing clinical workflow.
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To determine the effect of the health literacy levels of caregivers of individuals with T2DM on caregiving activities and supportive behaviours. ⋯ In the research, it was determined that care activities and supportive behaviours were higher in those with higher levels of HL, higher levels of education, those who were not employed, those with higher income levels, and those who received education about diabetes. Nurses should develop training programmes to increase the HL levels of caregivers.
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Artificial intelligence (AI) has significant transformative potential across various sectors, particularly in health care. This study aims to develop a protocol for the content analysis of a method designed to assess AI applications in drug-related information, specifically focusing on contraindications, adverse reactions, and drug interactions. By addressing existing challenges, this preliminary research seeks to enhance the safe and reliable integration of AI into healthcare practices. ⋯ This preliminary study demonstrates the potential for using an AI-powered tool to standardize drug-related information retrieval, particularly for contraindications and adverse reactions. While AI responses were generally appropriate, improvements are needed in identifying contraindicated drug interactions. Further research with larger datasets and broader evaluations is required to enhance AI's reliability in healthcare settings.
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To assess the Knowledge, Attitude, and Practice (KAP) of medical students at Hunan Medicine College towards insomnia and TCM treatment. ⋯ The study underscores the need for targeted educational interventions to enhance understanding and promote effective practices among medical students, potentially improving their well-being and addressing insomnia more comprehensively.
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Individuals living in rural or medically underserved areas (MUA) with chronic obstructive pulmonary disease (COPD) face significant barriers to specialised pulmonary care, including pulmonologists, diagnostic spirometry, and pulmonary rehabilitation. Remote spirometry for diagnostic screening and disease monitoring may mitigate access barriers and contribute to improved COPD management in this population. This study protocol describes the proposed implementation of a Mobile Health (mHealth) intervention using Bluetooth-enabled portable spirometry combined with a mobile disease management platform. ⋯ An mHealth intervention using Bluetooth-enabled remote portable spirometry is a potential solution to expanding healthcare access and improving outcomes in under-resourced populations at risk for increased morbidity and mortality. This study will evaluate the acceptability and feasibility of mHealth and remote monitoring, including symptom reporting among at-risk under-resourced adults with COPD.