J Med Syst
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Show the learning results obtained by a simulation tool used by students of an online course on anesthesia techniques and regional analgesia guided by ultrasound. A satisfaction survey generated with a form of Google Forms was carried out in September 2018 with 14 questions related to the quality, ease and capacity of the learning obtained after the use of the nerve blocks Simulator, which was firstly published on the first edition of the course for 34 students. ⋯ The students are, in their immense majority, habitual users of the ICTs and 73% of them consider that their experience with the simulator has been satisfactory and that their learning has been favored by this fact. The authors have verified that the ultrasound simulator contributes to the learning of skills for the practice of nerve blocks and, furthermore, it helps to ensure that theoretical knowledge is carried out in a more productive and efficient way.
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Operating room (OR) utilization is a significant determinant of hospital profitability. One aspect of this is surgical scheduling, which depends on accurate predictions of case duration. This has been done historically by either the surgeon based on personal experience, or by an electronic health record (EHR) based on averaged historical means for case duration. ⋯ Over all sub-specialties, Leap Rail showed a 7 min improvement in absolute difference between pCD and actual case duration when compared to conventional EHR (p < 0.0001). In aggregate, the Leap Rail method resulted in a 70% reduction in overall scheduling inaccuracy. Machine-learning algorithms are a promising method of increasing pCD accuracy and represent one means of improving OR planning and efficiency.
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Robot-assisted surgery (RAS) requires a large capital investment by healthcare organizations. The cost of a robotic unit is fixed, so institutions must maximize use of each unit by utilizing all available operating room block time. One way to increase utilization is to accurately predict case durations. ⋯ Using boosted regression tree, we can increase the number of accurately booked cases from 148 to 219 (34.9% to 51.7%, p < 0.001). This study shows that using various machine learning approaches can improve the accuracy of RAS case length predictions, which will increase utilization of this limited resource. Further work is needed to operationalize these findings.
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Internet of Things (IoT) has emerged as a new paradigm today, connecting a variety of physical and virtual elements integrated with electronic components, sensors, actuators and software to collect and exchange data. IoT is gaining increasing attention as a priority research topic in the Health sector in general and in specific areas such as Mental Health. The main objective of this paper is to show a review of the existing research works in the literature, referring to the main IoT services and applications in Mental Health diseases. ⋯ Many of the publications (more than 60%) found show the applications developed for monitoring patients with mental disorders through sensors and networked devices. The inclusion of the new IoT technology in Health brings many benefits in terms of monitoring, welfare interventions and providing alert and information services. In pathologies such as Mental Health is a vital factor to improve the patient life quality and effectiveness of the medical service.
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Randomized Controlled Trial
The Effect of an Electronic Dynamic Cognitive Aid Versus a Static Cognitive Aid on the Management of a Simulated Crisis: A Randomized Controlled Trial.
The aim of this study was to assess the effect of a dynamic electronic cognitive aid with embedded clinical decision support (dCA) versus a static cognitive aid (sCA) tool. Anesthesia residents in clinical anesthesia years 2 and 3 were recruited to participate. Each subject was randomized to one of two groups and performed an identical simulated clinical scenario. ⋯ In conclusion, we evaluated the use of a sCA versus a dCA with embedded decision support in a simulated environment. The dCA group was found to perform more checklist items correctly. Clinical Trial Registration: Clinicaltrials.gov study #: NCT02440607.