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Activity Number:
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158
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Type:
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Topic Contributed
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Date/Time:
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Monday, August 3, 2009 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Health Policy Statistics
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| Abstract - #303529 |
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Title:
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Learning from Near Misses in Medication Errors: A Bayesian Approach
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Author(s):
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Jessica A. Myers*+ and Francesca Dominici and Laura Morlock
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Companies:
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Johns Hopkins University and Johns Hopkins University and Johns Hopkins University
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Address:
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615 N. Wolfe St., Baltimore, MD, 21205,
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Keywords:
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Bayesian hierarchical models ; Correlation ; Medical error ; Voluntary error reports
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Abstract:
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Medical errors originating in healthcare facilities are a significant source of preventable morbidity, mortality, and healthcare costs. Voluntary error report systems that collect information on the circumstances of medical errors may be useful for developing effective harm prevention strategies, but some patient safety experts question the utility of data from near misses, or errors that did not lead to patient harm. We use data from a large voluntary reporting system of medication errors to provide evidence that the causes and contributing factors of errors that result in harm are similar to the causes and contributing factors of near misses. We develop Bayesian hierarchical models to estimate an overall measure of the evidence and uncertainty for this hypothesis in the data. We also identify the causes and contributing factors that have the highest or lowest log-odds ratio of harm.
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