@article{chazard_statistics-based_2012,
	title = {A statistics-based approach of contextualization for adverse drug events detection and prevention},
	volume = {180},
	copyright = {All rights reserved},
	issn = {0926-9630},
	url = {http://www.chazard.org/emmanuel/pdf_articles/paper_2012_mie_contextualization.pdf},
	abstract = {Several papers propose to take contexts into account for adverse drug events (ADE) detection and prevention, notably to decrease over-alerting of clinical decision support systems (CDSS). However, no statistical argument has been published till now. This works demonstrates, based on statistical analysis, that contextualization is necessary for ADE detection and prevention by 3 steps. A database of 115,447 inpatients stays from 6 hospitals, and 236 ADE detection rules are used. Step 1: the patients differ significantly between and within hospitals, regarding their medical background, their medication and several outcomes. Step 2: The estimated ADE rates vary between and within hospitals. Step 3: even when comparable conditions are present, the probability of ADE occurrence may differ between and within hospitals. Those 3 steps demonstrate that contextualization is necessary, and pave the way for a statistics-based method to contextualize ADE prevention (CDSS) and ADE detection tools.},
	language = {eng},
	journal = {Studies in health technology and informatics},
	author = {Chazard, Emmanuel and Bernonville, Stéphanie and Ficheur, Grégoire and Beuscart, Régis},
	year = {2012},
	pmid = {22874295},
	keywords = {Adverse Drug Reaction Reporting Systems, Data Interpretation, Statistical, Data Mining, Decision Support Systems, Clinical, Drug Toxicity, Drug-Related Side Effects and Adverse Reactions, Electronic Health Records, France, Health Records, Personal, Humans, Prevalence, Sensitivity and Specificity},
	pages = {766--770},
}

{"_id":"p94tksiWqyrJFMKy2","bibbaseid":"chazard-bernonville-ficheur-beuscart-astatisticsbasedapproachofcontextualizationforadversedrugeventsdetectionandprevention-2012","author_short":["Chazard, E.","Bernonville, S.","Ficheur, G.","Beuscart, R."],"bibdata":{"bibtype":"article","type":"article","title":"A statistics-based approach of contextualization for adverse drug events detection and prevention","volume":"180","copyright":"All rights reserved","issn":"0926-9630","url":"http://www.chazard.org/emmanuel/pdf_articles/paper_2012_mie_contextualization.pdf","abstract":"Several papers propose to take contexts into account for adverse drug events (ADE) detection and prevention, notably to decrease over-alerting of clinical decision support systems (CDSS). However, no statistical argument has been published till now. This works demonstrates, based on statistical analysis, that contextualization is necessary for ADE detection and prevention by 3 steps. A database of 115,447 inpatients stays from 6 hospitals, and 236 ADE detection rules are used. Step 1: the patients differ significantly between and within hospitals, regarding their medical background, their medication and several outcomes. Step 2: The estimated ADE rates vary between and within hospitals. Step 3: even when comparable conditions are present, the probability of ADE occurrence may differ between and within hospitals. Those 3 steps demonstrate that contextualization is necessary, and pave the way for a statistics-based method to contextualize ADE prevention (CDSS) and ADE detection tools.","language":"eng","journal":"Studies in health technology and informatics","author":[{"propositions":[],"lastnames":["Chazard"],"firstnames":["Emmanuel"],"suffixes":[]},{"propositions":[],"lastnames":["Bernonville"],"firstnames":["Stéphanie"],"suffixes":[]},{"propositions":[],"lastnames":["Ficheur"],"firstnames":["Grégoire"],"suffixes":[]},{"propositions":[],"lastnames":["Beuscart"],"firstnames":["Régis"],"suffixes":[]}],"year":"2012","pmid":"22874295","keywords":"Adverse Drug Reaction Reporting Systems, Data Interpretation, Statistical, Data Mining, Decision Support Systems, Clinical, Drug Toxicity, Drug-Related Side Effects and Adverse Reactions, Electronic Health Records, France, Health Records, Personal, Humans, Prevalence, Sensitivity and Specificity","pages":"766–770","bibtex":"@article{chazard_statistics-based_2012,\n\ttitle = {A statistics-based approach of contextualization for adverse drug events detection and prevention},\n\tvolume = {180},\n\tcopyright = {All rights reserved},\n\tissn = {0926-9630},\n\turl = {http://www.chazard.org/emmanuel/pdf_articles/paper_2012_mie_contextualization.pdf},\n\tabstract = {Several papers propose to take contexts into account for adverse drug events (ADE) detection and prevention, notably to decrease over-alerting of clinical decision support systems (CDSS). However, no statistical argument has been published till now. This works demonstrates, based on statistical analysis, that contextualization is necessary for ADE detection and prevention by 3 steps. A database of 115,447 inpatients stays from 6 hospitals, and 236 ADE detection rules are used. Step 1: the patients differ significantly between and within hospitals, regarding their medical background, their medication and several outcomes. Step 2: The estimated ADE rates vary between and within hospitals. Step 3: even when comparable conditions are present, the probability of ADE occurrence may differ between and within hospitals. Those 3 steps demonstrate that contextualization is necessary, and pave the way for a statistics-based method to contextualize ADE prevention (CDSS) and ADE detection tools.},\n\tlanguage = {eng},\n\tjournal = {Studies in health technology and informatics},\n\tauthor = {Chazard, Emmanuel and Bernonville, Stéphanie and Ficheur, Grégoire and Beuscart, Régis},\n\tyear = {2012},\n\tpmid = {22874295},\n\tkeywords = {Adverse Drug Reaction Reporting Systems, Data Interpretation, Statistical, Data Mining, Decision Support Systems, Clinical, Drug Toxicity, Drug-Related Side Effects and Adverse Reactions, Electronic Health Records, France, Health Records, Personal, Humans, Prevalence, Sensitivity and Specificity},\n\tpages = {766--770},\n}\n\n","author_short":["Chazard, E.","Bernonville, S.","Ficheur, G.","Beuscart, R."],"key":"chazard_statistics-based_2012","id":"chazard_statistics-based_2012","bibbaseid":"chazard-bernonville-ficheur-beuscart-astatisticsbasedapproachofcontextualizationforadversedrugeventsdetectionandprevention-2012","role":"author","urls":{"Paper":"http://www.chazard.org/emmanuel/pdf_articles/paper_2012_mie_contextualization.pdf"},"keyword":["Adverse Drug Reaction Reporting Systems","Data Interpretation","Statistical","Data Mining","Decision Support Systems","Clinical","Drug Toxicity","Drug-Related Side Effects and Adverse Reactions","Electronic Health Records","France","Health Records","Personal","Humans","Prevalence","Sensitivity and Specificity"],"metadata":{"authorlinks":{}}},"bibtype":"article","biburl":"https://api.zotero.org/groups/2266462/items?key=MgKoXciZhHmJ176339ZdCynJ&format=bibtex&limit=100","dataSources":["Ad3P6FkzWSCKrZQXc","PSBFFbnPhFKwYx7yq"],"keywords":["adverse drug reaction reporting systems","data interpretation","statistical","data mining","decision support systems","clinical","drug toxicity","drug-related side effects and adverse reactions","electronic health records","france","health records","personal","humans","prevalence","sensitivity and specificity"],"search_terms":["statistics","based","approach","contextualization","adverse","drug","events","detection","prevention","chazard","bernonville","ficheur","beuscart"],"title":"A statistics-based approach of contextualization for adverse drug events detection and prevention","year":2012}