[Decision trees in psychiatric therapy]

The main objective of decision analysis is to offer a theoretical representation of choices made in an environment of uncertainty. This technique is currently under development in a great variety of fields, particularly in medicine, where aid in decision making is the topic of much research. Psychiatry, in turn, is very much concerned by these new developments which could be of particular interest to therapeutics-an area where the large number of studies and date are in great contrast with the lack of consensus concerning the various solutions proposed to patients. Decision analysis utilizes different techniques among which are decision trees. The technique of decision trees goes far beyond a simple graphic representation of reasoning in the form of a chart. Its basic principles is to measure the uncertainty associated with decision making in the hopes of better understanding the rationale of decisions while optimizing the gain versus cost ratio. The goal is to calculate, within a series of decisions, the weight of their importance expressed in terms of usefulness or unpleasantness. In psychiatric therapeutics, only three studies have been published which incorporate the technique of decision trees. Two of these deal with treating depression (Schulberg et al., 1989; Koenig et al., 1993) while the third deals with schizophrenia (Hatcher, 1995). The limits of these techniques are, on one hand, due to their feasibility in that their complexity renders them inapplicable when a great number of variables have to be taken into account or when the amount of necessary data is still insufficient. Moreover, the use of these techniques remains relatively restricted as their expansion depends upon their acceptance by clinical physicians. Also, their use raises questions as to what extent it is possible to rationalize decisions in psychiatry. From a larger perspective, one must consider that these techniques may eventually furnish certain elements which could be integrated to help further the field of decision-making representations for clinical use. These decision-making techniques are still in the experimental stages and remains difficult to apply to clinical practice. However they appear to be of a great interest, not only in communicating knowledge both in teaching and training, but in research as well. They allow us to view the results of epidemiological studies and clinical research from a more global perspective; to make evident the grey areas of our science and to determine new priorities in research.