The use of analytic hierarchy process for measuring the complexity of medical diagnosis
The use of analytic hierarchy process for measuring the complexity of medical diagnosis
复制标题
DOI：
10.1177/1460458218824708
复制
发表时间：
2019-01-23
影响因子：
3
通讯作者：
Shabtai, Itamar
中科院分区：
文献类型：
--
作者：
Ben-Assuli, Ofir;Kumar, Nanda;Shabtai, Itamar
诊断复杂性是影响各种医疗结果的重要背景因素。现有的诊断复杂性测量要么依赖于粗糙的代理，要么使用细粒度的测量，这些测量使用的指标来自不易获得的专有数据。因此，在诸如医学信息学等领域对这一重要结构的研究一直受到测量诊断复杂性的困难的阻碍。本文提出了一种将诊断任务复杂性概念化和操作化为多维结构的新方法，该方法采用了医院医疗遭遇中现成的国际疾病分类代码，并使用了层次分析法。我们证明了所提出方法的可靠性，并表明尽管使用了相对简单的程序，但它能够预测再入院率，与最近研究中使用的一些复杂措施（即LaCE评分指数）一样好（甚至更好）。
Diagnostic complexity is an important contextual factor affecting a variety of medical outcomes. Existing measurements of diagnosis complexity either rely on crude proxies or use fine-grained measures that employ indicators from proprietary data that are not readily available. Hence, the study of this important construct in fields such as medical informatics has been hampered by the difficulty of measuring diagnostic complexity. This article presents a novel approach for conceptualizing and operationalizing diagnostic task complexity as a multi-dimensional construct, which employs the readily available International Classification of Diseases codes from medical encounters in hospitals and uses Analytic Hierarchical Process methodology. We demonstrate the reliability of the proposed approach and show that despite using a relatively simple procedure, it is able to predict readmission rates just as well as (or even better) than some of the sophisticated measures that have been used in recent studies (namely, the LaCE score index).