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• W2964176472 abstract "Medical simulation has become an integral part of the training of medical students, residents, and faculty. While our traditional sense of medical simulation involves scenarios, manikins, resuscitation, and procedures, we must now expand this vision. With the development of the electronic medical record and the resultant large data sets (“Big Data”) that encompass each patient's record, there is an opportunity to engage in the modeling and prediction of individual patient outcomes and trajectories of care. Here, medical simulation delves into the arena of clinical decision support systems (CDSSs) which leverage advanced algorithms, analytics, and machine learning approaches. The advent and continued adoption of health information technologies such as CDSSs into real-world health-care operations can lead to improved patient care, safety, and cost savings. The following core competencies are addressed in this article: System-based practice, Practice-based learning, and Patient care." @default.
• W2964176472 created "2019-07-30" @default.
• W2964176472 creator A5003994767 @default.
• W2964176472 creator A5047608011 @default.
• W2964176472 date "2017-01-01" @default.
• W2964176472 modified "2023-10-07" @default.
• W2964176472 title "Clinical decision support systems: From medical simulation to clinical practice" @default.
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• W2964176472 doi "https://doi.org/10.4103/ijam.ijam_34_17" @default.
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