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• W2022388542 abstract "Abstract Rationale, aims and objective Manual chart review is an effective but expensive method for adverse drug event (ADE) detection. Building an expert system capable of mimicking the human expert's decision pathway, to deduce the occurrence of an ADE, can improve efficiency and lower cost. As a first step to build such an expert system, this study explores pharmacist's decision‐making processes for ADE detection. Methods Think‐aloud procedures were used to elicit verbalizations as pharmacists read through ADE case scenarios. Two types of information were extracted, firstly pharmacists' decision‐making strategies regarding ADEs and secondly information regarding pharmacists' unmet information needs for ADE detection. Verbal protocols were recorded and analysed qualitatively to extract ADE information signals. Inter‐reviewer agreement for classification of ADE information signals was calculated using Cohen's kappa. Results We extracted a total of 110 information signals, of which 73% consisted of information that was interpreted by the pharmacists from the case scenario and only about half (53%, n = 32) of the information signals were considered relevant for the detection of the ADEs. Excellent reliability was demonstrated between the reviewers for classifying signals. Fifty information signals regarding unmet information needs were extracted and grouped into themes based on the type of missing information. Conclusions Pharmacists used a forward reasoning approach to make implicit deductions and validate hypotheses about possible ADEs. Verbal protocols also indicated that pharmacists' unmet information needs occurred frequently. Developing alerting systems that meet pharmacists' needs adequately will enhance their ability to reduce preventable ADEs, thus improving patient safety." @default.
• W2022388542 created "2016-06-24" @default.
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• W2022388542 date "2009-03-19" @default.
• W2022388542 modified "2023-10-16" @default.
• W2022388542 title "Understanding pharmacist decision making for adverse drug event (ADE) detection" @default.
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• W2022388542 doi "https://doi.org/10.1111/j.1365-2753.2008.00992.x" @default.
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