The implementation of clinical information systems and electronic medical records does not have a good track record. It is estimated that more than 50% of implementations fail. A review of electronic health information system (EHIS) models incorporating clinical information systems and electronic medical records was undertaken to determine the models developed and applied in health. Twenty one health and five non-health models were identified. The non-health models were included as a number of health models were derived form these. The findings and evaluation of the models has identified varying contents and results. The models identified were assessed to determine how these related to each other, whether models were tested and how, if benefits were identified and if costsavings were projected or realised. This review of EHIS implementation models has identified a need for clear definition of terms used, careful categorisation and for models to be comprehensive, extensive and rigorous if successful outcomes are to occur.
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Trials
December 2024
Division of Infectious Diseases, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
Background: Vancomycin, an antibiotic with activity against methicillin-resistant Staphylococcus aureus (MRSA), is frequently included in empiric treatment for community-acquired pneumonia (CAP) despite the fact that MRSA is rarely implicated in CAP. Conducting polymerase chain reaction (PCR) testing on nasal swabs to identify the presence of MRSA colonization has been proposed as an antimicrobial stewardship intervention to reduce the use of vancomycin. Observational studies have shown reductions in vancomycin use after implementation of MRSA colonization testing, and this approach has been adopted by CAP guidelines.
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December 2024
School of Computing, Queen's University, 557 Goodwin Hall, 21-25 Union St, Kingston, K7L 2N8, Ontario, Canada.
Background: Epistasis, the phenomenon where the effect of one gene (or variant) is masked or modified by one or more other genes, significantly contributes to the phenotypic variance of complex traits. Traditionally, epistasis has been modeled using the Cartesian epistatic model, a multiplicative approach based on standard statistical regression. However, a recent study investigating epistasis in obesity-related traits has identified potential limitations of the Cartesian epistatic model, revealing that it likely only detects a fraction of the genetic interactions occurring in natural systems.
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December 2024
Department of Orthopedics, Guru Gobind Singh Medical College and Hospital, Faridkot, Punjab, 151203, India.
Generative Artificial Intelligence (AI), characterized by its ability to generate diverse forms of content including text, images, video and audio, has revolutionized many fields, including medical education. Generative AI leverages machine learning to create diverse content, enabling personalized learning, enhancing resource accessibility, and facilitating interactive case studies. This narrative review explores the integration of generative artificial intelligence (AI) into orthopedic education and training, highlighting its potential, current challenges, and future trajectory.
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December 2024
Klinikum Stuttgart, Stuttgart Cancer Center - Tumorzentrum Eva Mayr-Stihl DE, Kriegsbergstraße 60, Stuttgart, D-70174, Germany.
Background: Medical narratives are fundamental to the correct identification of a patient's health condition. This is not only because it describes the patient's situation. It also contains relevant information about the patient's context and health state evolution.
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December 2024
Upstream Lab, MAP Centre for Urban Health Solutions, Li Ka Shing Knowledge Institute, Unity Health Toronto, 30 Bond Street, Toronto, ON, M5B 1W8, Canada.
Background: Machine learning (ML) is increasingly used in population and public health to support epidemiological studies, surveillance, and evaluation. Our objective was to conduct a scoping review to identify studies that use ML in population health, with a focus on its use in non-communicable diseases (NCDs). We also examine potential algorithmic biases in model design, training, and implementation, as well as efforts to mitigate these biases.
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