Assessing the conformity to clinical guidelines in oncology: An example for the multidisciplinary management of locally advanced colorectal cancer treatment. Issue 10 (8th October 2018)

• Record Type: Journal Article
• Authors: Lenkowicz, Jacopo; Gatta, Roberto; Masciocchi, Carlotta; Casà, Calogero; Cellini, Francesco; Damiani, Andrea; Dinapoli, Nicola; Valentini, Vincenzo
• Is Part Of: Management decision. Volume 56:Issue 10(2018)
• Page Start: 2172
• Page End: 2186
• Publication Date: 2018-10-08
• Subjects: Conformance checking -- Evidence-based practice -- Process mining -- Clinical guidelines -- Clinical pathway; Management -- Periodicals; 658.403
• DOI: 10.1108/MD-09-2017-0906
• Languages: English
• ISSNs: 0025-1747

Abstract

Abstract : Purpose: The purpose of this paper is to describe a methodology to deal with conformance checking through the implementation of computer-interpretable-clinical guidelines (CIGs), and present an application of the methodology to real-world data and a clinical pathway for radiotherapy-related oncological treatment. Design/methodology/approach: This methodology is implemented by a software able to use the hospital electronic health record data to assess the adherence of the actual executed clinical processes to a clinical pathway, monitoring at the same time management-related efficiency and performance parameters, and ideally, suggesting ways to improve them. Findings: Three use cases are presented, in which the results of conformance checking are used to compare different branches of the executed guidelines with respect to the adherence to ideal process, temporal distribution of state-to-state transitions, and overall treatment efficacy, in order to extract data-driven evidence that could be of interest for the hospital management. Originality/value: This approach has the result of applying management-oriented data mining technique on sequential data, typical of process mining, to the result of a conformity check between the preliminary knowledge defined by clinicians and the real-world data, typical of CIGs.