This paper is the fourth of a five-part series that describes the principles of construction and evaluation of valid decision models. In this review, the authors describe the key principles of detecting and eliminating structural and programming errors in decision trees (debugging). In addition, they offer guidelines to facilitate the interpretation of analytic results of decision models.