Decision-support systems in dentistry

Abstract

Decision-support systems hold a specialized body of knowledge in computerized form such that the non- specialist can obtain expert-level information. The goal of these systems in clinical sciences is usually to assist patient care by providing the clinician with improved diagnosis or treatment planning. Decision-support systems consist of three components: the user interface through which the clinician or patient enters signs or symptoms, the set of data describing clinical knowledge in the domain of the program, and an inference engine to manipulate the data set in light of a patient's specific signs or symptoms to arrive at a diagnosis or treatment plan. Such systems usually use one of three mechanisms of analysis alone or in combination: classification trees, Bayesian conditional probabilities, or rule-based (heuristic) systems. Numerous problems must be solved before decision-support systems will become commonplace in clinical practice. Data entry of patients' signs and symptoms is often tedious. The quality of the clinician's initial observations is of great importance in determining the quality of the output. It is also often difficult to convey to a program the subtlety of clinical information observed. Knowledge required in clinical data bases is often unavailable or imprecise. As these and other challenges are addressed we can anticipate increased utility of decision support programs in the future.