The lack of prospective outcomes studies for many types of incidental findings limits our understanding of both their natural history and the potential efficacy of treatment. To support decision making for the management of incidental findings, major sources of uncertainty in management pathways can be mapped and analyzed using mathematical models. This process yields important insights into how uncertainty influences the best treatment decision. Here, we consider a classification scheme, grounded in decision science, which exposes various levels and types of uncertainty in the management of incidental findings and addresses (1) disease-related risks, which are considered in context of a patient's competing causes of mortality; (2) potential degrees of intervention; (3) strength of evidence; and (4) patients' treatment-related preferences. Herein we describe how categorizing uncertainty by the sources, issues, and locus can build a framework from which to improve the management of incidental findings. Accurate and comprehensive handling of uncertainty will improve the quality of related decision making and will help guide future research priorities.
Keywords: Incidental finding; decision making; patient centered; uncertainty.
Copyright © 2018. Published by Elsevier Inc.