@article{smithburger_grading_2010,
	title = {Grading the severity of drug-drug interactions in the intensive care unit: a comparison between clinician assessment and proprietary database severity rankings},
	volume = {44},
	issn = {1542-6270},
	shorttitle = {Grading the severity of drug-drug interactions in the intensive care unit},
	doi = {10.1345/aph.1P377},
	abstract = {BACKGROUND: Computerized provider order entry with decision support software offers an opportunity to identify and prevent medication-related errors, including drug-drug interactions (DDIs), through alerting mechanisms. However, the number of alerts generated can overwhelm and lead to "alert fatigue." A DDI alert system based on severity rankings has been shown to reduce alert fatigue; however, the best method to populate this type of database is unclear.
OBJECTIVE: To compare the severity ranking of proprietary databases to clinician assessment for DDIs occurring in critically ill patients.
METHODS: This observational, prospective study was conducted over 8 weeks in the cardiac and cardiothoracic intensive care unit. Medication profiles of patients were screened for the presence of DDIs and a severity evaluation was conducted using rankings of proprietary databases and clinician opinion using a DDI severity assessment tool. The primary outcome measure was the number of DDIs considered severe by both evaluation methods.
RESULTS: A total of 1150 DDIs were identified after 400 patient medication profiles were evaluated. Of these, 458 were unique drug pairs. Overall, 7.4\% (34/458) were considered a severe interaction based upon proprietary database ratings. The assessment by clinicians ranked 6.6\% (30/458) of the unique DDIs as severe. Only 3 interactions, atazanavir-simvastatin, atazanavir-tenofovir, and aspirin-warfarin, were considered severe by both evaluation methods.
CONCLUSIONS: Since proprietary databases and clinician assessment of severe DDIs do not agree, developing a knowledge base for a DDI alert system likely requires proprietary database information in conjunction with clinical opinion.},
	language = {eng},
	number = {11},
	journal = {The Annals of pharmacotherapy},
	author = {Smithburger, Pamela L and Kane-Gill, Sandra L and Benedict, Neal J and Falcione, Bonnie A and Seybert, Amy L},
	month = nov,
	year = {2010},
	pmid = {20959499},
	keywords = {Coronary Care Units, Critical Illness, Databases, Factual, Decision Support Systems, Clinical, Drug Interactions, Drug Therapy, Computer-Assisted, Humans, Medical Order Entry Systems, Medication Errors, Prospective Studies, Reminder Systems, Severity of Illness Index},
	pages = {1718--1724}
}

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L"],"year":2010,"bibtype":"article","biburl":"https://bibbase.org/zotero/emmanuel.chazard","bibdata":{"bibtype":"article","type":"article","title":"Grading the severity of drug-drug interactions in the intensive care unit: a comparison between clinician assessment and proprietary database severity rankings","volume":"44","issn":"1542-6270","shorttitle":"Grading the severity of drug-drug interactions in the intensive care unit","doi":"10.1345/aph.1P377","abstract":"BACKGROUND: Computerized provider order entry with decision support software offers an opportunity to identify and prevent medication-related errors, including drug-drug interactions (DDIs), through alerting mechanisms. However, the number of alerts generated can overwhelm and lead to \"alert fatigue.\" A DDI alert system based on severity rankings has been shown to reduce alert fatigue; however, the best method to populate this type of database is unclear. OBJECTIVE: To compare the severity ranking of proprietary databases to clinician assessment for DDIs occurring in critically ill patients. METHODS: This observational, prospective study was conducted over 8 weeks in the cardiac and cardiothoracic intensive care unit. Medication profiles of patients were screened for the presence of DDIs and a severity evaluation was conducted using rankings of proprietary databases and clinician opinion using a DDI severity assessment tool. The primary outcome measure was the number of DDIs considered severe by both evaluation methods. RESULTS: A total of 1150 DDIs were identified after 400 patient medication profiles were evaluated. Of these, 458 were unique drug pairs. Overall, 7.4% (34/458) were considered a severe interaction based upon proprietary database ratings. The assessment by clinicians ranked 6.6% (30/458) of the unique DDIs as severe. Only 3 interactions, atazanavir-simvastatin, atazanavir-tenofovir, and aspirin-warfarin, were considered severe by both evaluation methods. CONCLUSIONS: Since proprietary databases and clinician assessment of severe DDIs do not agree, developing a knowledge base for a DDI alert system likely requires proprietary database information in conjunction with clinical opinion.","language":"eng","number":"11","journal":"The Annals of pharmacotherapy","author":[{"propositions":[],"lastnames":["Smithburger"],"firstnames":["Pamela","L"],"suffixes":[]},{"propositions":[],"lastnames":["Kane-Gill"],"firstnames":["Sandra","L"],"suffixes":[]},{"propositions":[],"lastnames":["Benedict"],"firstnames":["Neal","J"],"suffixes":[]},{"propositions":[],"lastnames":["Falcione"],"firstnames":["Bonnie","A"],"suffixes":[]},{"propositions":[],"lastnames":["Seybert"],"firstnames":["Amy","L"],"suffixes":[]}],"month":"November","year":"2010","pmid":"20959499","keywords":"Coronary Care Units, Critical Illness, Databases, Factual, Decision Support Systems, Clinical, Drug Interactions, Drug Therapy, Computer-Assisted, Humans, Medical Order Entry Systems, Medication Errors, Prospective Studies, Reminder Systems, Severity of Illness Index","pages":"1718–1724","bibtex":"@article{smithburger_grading_2010,\n\ttitle = {Grading the severity of drug-drug interactions in the intensive care unit: a comparison between clinician assessment and proprietary database severity rankings},\n\tvolume = {44},\n\tissn = {1542-6270},\n\tshorttitle = {Grading the severity of drug-drug interactions in the intensive care unit},\n\tdoi = {10.1345/aph.1P377},\n\tabstract = {BACKGROUND: Computerized provider order entry with decision support software offers an opportunity to identify and prevent medication-related errors, including drug-drug interactions (DDIs), through alerting mechanisms. However, the number of alerts generated can overwhelm and lead to \"alert fatigue.\" A DDI alert system based on severity rankings has been shown to reduce alert fatigue; however, the best method to populate this type of database is unclear.\nOBJECTIVE: To compare the severity ranking of proprietary databases to clinician assessment for DDIs occurring in critically ill patients.\nMETHODS: This observational, prospective study was conducted over 8 weeks in the cardiac and cardiothoracic intensive care unit. Medication profiles of patients were screened for the presence of DDIs and a severity evaluation was conducted using rankings of proprietary databases and clinician opinion using a DDI severity assessment tool. The primary outcome measure was the number of DDIs considered severe by both evaluation methods.\nRESULTS: A total of 1150 DDIs were identified after 400 patient medication profiles were evaluated. Of these, 458 were unique drug pairs. Overall, 7.4\\% (34/458) were considered a severe interaction based upon proprietary database ratings. The assessment by clinicians ranked 6.6\\% (30/458) of the unique DDIs as severe. Only 3 interactions, atazanavir-simvastatin, atazanavir-tenofovir, and aspirin-warfarin, were considered severe by both evaluation methods.\nCONCLUSIONS: Since proprietary databases and clinician assessment of severe DDIs do not agree, developing a knowledge base for a DDI alert system likely requires proprietary database information in conjunction with clinical opinion.},\n\tlanguage = {eng},\n\tnumber = {11},\n\tjournal = {The Annals of pharmacotherapy},\n\tauthor = {Smithburger, Pamela L and Kane-Gill, Sandra L and Benedict, Neal J and Falcione, Bonnie A and Seybert, Amy L},\n\tmonth = nov,\n\tyear = {2010},\n\tpmid = {20959499},\n\tkeywords = {Coronary Care Units, Critical Illness, Databases, Factual, Decision Support Systems, Clinical, Drug Interactions, Drug Therapy, Computer-Assisted, Humans, Medical Order Entry Systems, Medication Errors, Prospective Studies, Reminder Systems, Severity of Illness Index},\n\tpages = {1718--1724}\n}\n\n","author_short":["Smithburger, P. 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