The use of analytic hierarchy process for measuring the complexity of medical diagnosis. (March 2020)

• Record Type: Journal Article
• Authors: Ben-Assuli, Ofir; Kumar, Nanda; Arazy, Ofer; Shabtai, Itamar
• Other Names: Bian Jiang guest-editor.; Modave Francois guest-editor.
• Is Part Of: Health informatics journal. Volume 26:Number 1(2020)
• Page Start: 218
• Page End: 232
• Publication Date: 2020-03
• Subjects: analytic hierarchy process -- diagnosis complexity -- International Classification of Diseases -- medical informatics -- task complexity; Medical informatics -- Periodicals; 610.285
• DOI: 10.1177/1460458218824708
• Languages: English
• ISSNs: 1460-4582

Abstract

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).