J Med Syst
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Delirium is a serious medical complication associated with poor outcomes. Given the complexity of the syndrome, prevention and early detection are critical in mitigating its effects. We used Confusion Assessment Method (CAM) screening and Electronic Health Record (EHR) data for 64,038 inpatient visits to train and test a model predicting delirium arising in hospital. ⋯ The Random Forest predictive model yielded an area under the receiver operating characteristic curve (ROC AUC) of 0.909 (95% CI 0.898 to 0.921). Important variables in the model included previously identified predisposing and precipitating risk factors. This machine learning approach displayed a high degree of accuracy and has the potential to provide a clinically useful predictive model for earlier intervention in those patients at greatest risk of developing delirium.
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To support the next generation of healthcare innovators - whether they be engineers, designers, clinicians, or business experts by training - education in the emerging field of medical innovation should be made easily and widely accessible to undergraduate students, graduate students, and young professionals, early in their careers. Currently, medical innovation curricula are taught through semester-long courses or year-long fellowships at a handful of universities, reaching only a limited demographic of participants. This study describes the structure and preliminary outcomes of a 1-2 week "extended hackathon" course that seeks to make medical innovation education and training more accessible and easily adoptable for academic medical centers. ⋯ In this study, the extended hackathon is presented as a novel educational model to teach undergraduate and graduate students a foundational skillset for medical innovation. Participants reported gaining significant knowledge across all ten categories assessed. To more robustly assess the educational value of extended hackathons, a standardized assessment for medical innovation knowledge needs to be developed, and a larger sample size of participants surveyed.
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The science of solving clinical problems by analyzing images generated in clinical practice is known as medical image analysis. The aim is to extract information in an affective and efficient manner for improved clinical diagnosis. The recent advances in the field of biomedical engineering have made medical image analysis one of the top research and development area. ⋯ This includes application areas such as segmentation, abnormality detection, disease classification, computer aided diagnosis and retrieval. In this study, a comprehensive review of the current state-of-the-art in medical image analysis using deep convolutional networks is presented. The challenges and potential of these techniques are also highlighted.
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Electronic health records (EHRs) have emerged among health information technology as "meaningful use" to improve the quality and efficiency of healthcare, and health disparities in population health. In other instances, they have also shown lack of interoperability, functionality and many medical errors. With proper implementation and training, are electronic health records a viable source in managing population health? The primary objective of this systematic review is to assess the relationship of electronic health records' use on population health through the identification and analysis of facilitators and barriers to its adoption for this purpose. ⋯ This review identifies more facilitators than barriers to using the EHR to support public health, which implies a certain level of usability and acceptance to use the EHR in this manner. The public-health industry should combine their efforts with the interoperability projects to make the EHR both fully adopted and fully interoperable. This will greatly increase the availability, accuracy, and comprehensiveness of data across the country, which will enhance benchmarking and disease surveillance/prevention capabilities.
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The Glossary of Times Used for Scheduling and Monitoring of Diagnostic and Therapeutic Procedures also known as the Procedural Times Glossary (PTG) was originally developed with the support of the Association of Anesthesia Clinical Directors (AACD). The goal was to establish standardized terms to measure and assess the performance of operating room and procedural areas. By incorporating standardized concepts of efficiency and utilization, the PTG codified operating room metrics and facilitated benchmarking and quality improvement initiatives. ⋯ We describe each of the categories and corresponding metrics. The PTG provides the fundamental building blocks for managing operating and non-operating room suites. We hope that reintroducing these important time markers will help facilitate the reporting of standardized metrics.