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https://www.selleckchem.com/products/shp099-dihydrochloride.html We aimed to develop and validate an instrument to detect hospital medication prescribing errors using repurposed clinical decision support system data. Despite significant efforts to eliminate medication prescribing errors, these events remain common in hospitals. Data from clinical decision support systems have not been used to identify prescribing errors as an instrument for physician-level performance. We evaluated medication order alerts generated by a knowledge-based electronic prescribing system occurring in one large academic medical center's acute care facilities for patient encounters between 2009 and 2012. We developed and validated an instrument to detect medication prescribing errors through a clinical expert panel consensus process to assess physician quality of care. Six medication prescribing alert categories were evaluated for inclusion, one of which - dose - was included in the algorithm to detect prescribing errors. The instrument was 93% sensitive (recall), 51% specific, 40% precise, 62% accurate, with an F1 score of 55%, positive predictive value of 96%, and a negative predictive value of 32%. Using repurposed electronic prescribing system data, dose alert overrides can be used to systematically detect medication prescribing errors occurring in an inpatient setting with high sensitivity.The notion that procedural learning and memory is spared in Alzheimer's disease (AD) has important implications for interventions aiming to build on intact cognitive functions. However, despite these clinical implications, there are mixed findings in the literature about whether or not procedural learning remains intact. This meta-analysis examines the standard mean difference of all published studies regarding procedural learning in AD dementia or amnestic Mild Cognitive Impairment (aMCI) compared to cognitively healthy older adults. Additionally, we conducted statistical equivalence analyses. Our s

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