A. Suner (1), G. Karakülah (2, 3), O. Dicle (3, 4), S. Sökmen (5), C. C. Çelikoğlu (6)
(1) Ege University, School of Medicine, Department of Biostatistics and Medical Informatics, Bornova-Izmir, 35040, Turkey; (2) Neurobiology-Neurodegeneration and Repair Laboratory, National Eye Institute, National Institutes of Health, Bethesda, Maryland, 20892, USA; (3) Dokuz Eylül University, Health Sciences Institute, Department of Medical Informatics, Inciraltı-Izmir, 35340, Turkey; (4) Dokuz Eylül University, School of Medicine, Department of Radiology, Inciraltı-Izmir, 35340, Turkey; (5) FACS, FASCRS, FASPSM Member from Dokuz Eylül University, School of Medicine, Department of General Surgery, Colorectal and Pelvic Surgery Unit, Inciraltı-Izmir, 35340, Turkey; (6) Dokuz Eylül University, Faculty of Science, Department of Statistics, Buca-Izmir, 35160, Turkey
clinical decision support, Rectal cancer, analytic hierarchy process, decision tree
Background: The selection of appropriate rectal cancer treatment is a complex multi-criteria decision making process, in which clinical decision support systems might be used to assist and enrich physicians’ decision making.
Objective: The objective of the study was to develop a web-based clinical decision support tool for physicians in the selection of potentially beneficial treatment options for patients with rectal cancer.
Methods: The updated decision model contained 8 and 10 criteria in the first and second steps respectively. The decision support model, developed in our previous study by combining the Analytic Hierarchy Process (AHP) method which determines the priority of criteria and decision tree that formed using these priorities, was updated and applied to 388 patients data collected retrospectively. Later, a web-based decision support tool named corRECTreatment was developed. The compatibility of the treatment recommendations by the expert opinion and the decision support tool was examined for its consistency. Two surgeons were requested to recommend a treatment and an overall survival value for the treatment among 20 different cases that we selected and turned into a scenario among the most common and rare treatment options in the patient data set.
Results: In the AHP analyses of the criteria, it was found that the matrices, generated for both decision steps, were consistent (consistency ratio<0.1). Depending on the decisions of experts, the consistency value for the most frequent cases was found to be 80% for the first decision step and 100% for the second decision step. Similarly, for rare cases consistency was 50% for the first decision step and 80% for the second decision step.
Conclusions: The decision model and corRECTreatment, developed by applying these on real patient data, are expected to provide potential users with decision support in rectal cancer treatment processes and facilitate them in making projections about treatment options.
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