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

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

Research output: Contribution to journalArticle

6 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)1325-1329
Number of pages5
JournalSupportive Care in Cancer
Volume15
Issue number11
DOIs
StatePublished - Nov 1 2007
Externally publishedYes

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Keywords

  • Anemia
  • Computerized decision support
  • Erythropoietic proteins
  • Practice guidelines
  • Supportive cancer care

ASJC Scopus subject areas

  • Oncology

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