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

被引:5
作者
Aapro, Matti
Abraham, Ivo
MacDonald, Karen
Soubeyran, Pierre
Foubert, Jan
Bokemeyer, Carsten
Muenzberg, Michael
Van Erps, Joanna
Turner, Matthew
机构
[1] Matrix45, Earlysville, VA 22936 USA
[2] Clin Genolier, Inst Multidisciplinaire Oncol, CH-1272 Genolier, Switzerland
[3] Univ Penn, Inst Aging, Philadelphia, PA 19104 USA
[4] Univ Penn, Leonard Davis Inst Hlth Econ, Ctr Hlth Outcomes & Policy Res, Philadelphia, PA 19104 USA
[5] Univ Bordeaux 2, Inst Bergonie, F-33076 Bordeaux, France
[6] Erasmushogesch, B-1090 Brussels, Belgium
[7] Univ Klinikum Hamburg Eppendorf, D-20246 Hamburg, Germany
[8] F Hoffmann La Roche & Cie AG, CH-4070 Basel, Switzerland
[9] Algemeen Stedelijk Ziekenhuis Aalst, Afdeling Oncol & Hematol, B-9300 Aalst, Belgium
[10] Roche Prod Ltd, Welwyn Garden City AL7 1TW, Herts, England
关键词
anemia; computerized decision support; practice guidelines; supportive cancer care; erythropoietic proteins;
D O I
10.1007/s00520-007-0246-7
中图分类号
R73 [肿瘤学];
学科分类号
100214 [肿瘤学];
摘要
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.
引用
收藏
页码:1325 / 1329
页数:5
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