International journal of medical informatics
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To explore attitudes about artificial intelligence (AI) among staff who utilized AI-based clinical decision support (CDS). ⋯ AI-based CDS tools that are perceived negatively by staff may reduce staff excitement about AI technology, and hands-on experience with AI may lead to more realistic expectations about the technology's capabilities. In our setting, although AI-based CDS prompted an interdisciplinary discussion about the needs of patients at high risk for poor glycemic control, the interventions recommended by the CDS were often perceived to be poorly tailored, inappropriate, or not useful. Developers should carefully consider tasks that are best performed by AI and those best performed by the patient's care team.
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Research has shown that frailty, a geriatric syndrome associated with an increased risk of negative outcomes for older people, is highly prevalent among residents of residential aged care facilities (also called long term care facilities or nursing homes). However, progress on effective identification of frailty within residential care remains at an early stage, necessitating the development of new methods for accurate and efficient screening. ⋯ There is some potential for AI techniques to contribute towards better frailty identification within residential care. However, potential benefits will need to be weighed against administrative burden, data quality concerns and presence of potential bias.
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Usability associates with patient safety and quality of care. This article reports results from nation-wide usability-focused survey studies for physicians and nurses in Finland. Earlier research has shown dissatisfaction and serious deficiencies, which hamper the efficient use of health information systems (HIS); however, evaluation studies covering the viewpoints of both user groups are practically lacking. Our study aimed at comparing end-users' experiences on the usability of electronic health record (EHR) systems by employment sector and EHR brand. ⋯ Nurses' and physicians' experiences on EHR usability appear to vary more by EHR brand and employment sector rather than either professional group being generally more satisfied. Development of EHR systems should consider the perspectives of these two main user groups and their working contexts.
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Pneumonia is a common complication after stroke, causing an increased length of hospital stay and death. Therefore, the timely and accurate prediction of post-stroke pneumonia would be highly valuable in clinical practice. Previous pneumonia risk score models were often built on simple statistical methods such as logistic regression. This study aims to investigate post-stroke pneumonia prediction models using more advanced machine learning algorithms, specifically deep learning approaches. ⋯ The deep learning-based predictive model is feasible for stroke patient management and achieves the optimal performance compared to many classic machine learning methods.
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Adverse events in healthcare are often collated in incident reports which contain unstructured free text. Learning from these events may improve patient safety. Natural language processing (NLP) uses computational techniques to interrogate free text, reducing the human workload associated with its analysis. There is growing interest in applying NLP to patient safety, but the evidence in the field has not been summarised and evaluated to date. ⋯ NLP can generate meaningful information from unstructured data in the specific domain of the classification of incident reports and adverse events. Understanding what or why incidents are occurring is important in adverse event analysis. If NLP enables these insights to be drawn from larger datasets it may improve the learning from adverse events in healthcare.