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Les «ADE Scorecards»: Un outil de détection par data mining des effets indésirables liés aux médicaments dans les dossiers médicaux (projet PSIP)

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Systèmes d’information pour l’amélioration de la qualité en santé

Part of the book series: Informatique et Santé ((INFORMATIQUE,volume 1))

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Abstract

The automated detection of Adverse Drug Events (ADE) is an important issue in medical informatics. The objective of this work is to automatically detect ADEs and to present the results to physicians. 90,000 stays are extracted from the EHR of 5 French and Danish hospitals and loaded into a common repository, using a common data model. Then data mining procedures such as decision trees are used in order to get ADE detection rules that are filtered and validated by an expert committee. The procedure enables to produce 236 ADE detection rules that are able to detect 27 different kinds of outcomes. Contextualized statistics are computed for every rule in every medical department separately. The physicians of the medical departments are provided with that information by means of a web-based tool named “ADE Scorecards”. The tool is presented in the article through a use case and several screenshots. Based on a list of rules and a repository of stays, it allows for displaying the important rules, the related statistics, and the complete information about the suspicious cases. The knowledge is contextualized, i.e. it depends on the medical department. The tool is deployed in a French hospital and is currently being evaluated through a prospective impact assessment.

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Authors and Affiliations

  1. Service d’information et des archives médicales, Clinique de santé publique, CHRU de Lille, 150 rue du Dr Yersin, 59037, Lille Cedex, France

    Emmanuel Chazard, Grégoire Ficheur & Régis Beuscart

  2. IDEEA Advertising, Bucarest, Roumanie

    Adrian Baceanu

  3. Evalab Inserm CIC-IT, Université de Lille Nord de France, EA 2694, Lille, France

    Romaric Marcilly

Authors
  1. Emmanuel Chazard
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  2. Adrian Baceanu
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  3. Grégoire Ficheur
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  4. Romaric Marcilly
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  5. Régis Beuscart
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Corresponding author

Correspondence to Emmanuel Chazard .

Editor information

Editors and Affiliations

  1. Département d’Information et d’Informatique Médicale, Centre Hospitalier Universitaire de Nice Hôpital de Cimiez, 4 Avenue Reine Victoria, B.P. 1179, 06003, Nice Cedex 1, France

    Pascal M. Staccini

  2. Service de Médecine Interne, C.H.U. Mongi Slim - La Marsa, 2046, Sidi-Daoud, Tunis, Tunisie

    Ali Harmel

  3. Equipe CISMeF, Cour Leschevin Porte 21, 3ème étage 1 rue de Germont, 76031, Rouen Cedex, France

    Stéfan J. Darmoni

  4. Service de Neurologie, C.H.U. Razi, 2010, La Manouba, Tunis, Tunisie

    Riadh Gouider

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© 2011 Springer-Verlag France

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Chazard, E., Baceanu, A., Ficheur, G., Marcilly, R., Beuscart, R. (2011). Les «ADE Scorecards»: Un outil de détection par data mining des effets indésirables liés aux médicaments dans les dossiers médicaux (projet PSIP). In: Staccini, P.M., Harmel, A., Darmoni, S.J., Gouider, R. (eds) Systèmes d’information pour l’amélioration de la qualité en santé. Informatique et Santé, vol 1. Springer, Paris. https://doi.org/10.1007/978-2-8178-0285-5_16

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  • DOI: https://doi.org/10.1007/978-2-8178-0285-5_16

  • Publisher Name: Springer, Paris

  • Print ISBN: 978-2-8178-0284-8

  • Online ISBN: 978-2-8178-0285-5

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