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01.11.2007 | Short Communication

Intraclass correlation metrics for the accuracy of algorithmic definitions in a computerized decision support system for supportive cancer care

verfasst von: Matti Aapro, Ivo Abraham, Karen MacDonald, Pierre Soubeyran, Jan Foubert, Carsten Bokemeyer, Michael Muenzberg, Joanna Van Erps, Matthew Turner

Erschienen in: Supportive Care in Cancer | Ausgabe 11/2007

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Abstract

As part of the development of a computerized clinical decision support system for anemia management in cancer patients, we applied psychometric principles and techniques to assess the accuracy of the algorithmic operationalizations of a set of evidence-based practice guidelines. In an iterative rating process, five medical and nursing experts rated 27 algorithmic sets derived from 18 guidelines, the objective being an intraclass coefficient (ICC) exceeding 0.90. The first round of review yielded an ICC of 1.00 for 22 sets. After revision and resubmission to the expert panel, an ICC of 1.00 was obtained for the additional five sets. The evolving decision support system is based on algorithms that accurately specify evidence-based guidelines for anemia management in cancer patients.

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Titel: Intraclass correlation metrics for the accuracy of algorithmic definitions in a computerized decision support system for supportive cancer care
verfasst von: Matti Aapro
Ivo Abraham
Karen MacDonald
Pierre Soubeyran
Jan Foubert
Carsten Bokemeyer
Michael Muenzberg
Joanna Van Erps
Matthew Turner
Publikationsdatum: 01.11.2007
Verlag: Springer-Verlag
Erschienen in: Supportive Care in Cancer / Ausgabe 11/2007
Print ISSN: 0941-4355
Elektronische ISSN: 1433-7339
DOI: https://doi.org/10.1007/s00520-007-0246-7

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Intraclass correlation metrics for the accuracy of algorithmic definitions in a computerized decision support system for supportive cancer care

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