A comparison of actuarial methods for identifying repetitively violent patients with mental illnesses.

Abstract

This is a progress report on the development of practical methods for the actuarial prediction of violence. The literature indicates that actuarial prediction is more accurate than clinical prediction, but in practice actuarial methods seem to be used rarely. Here we address two obstacles to the clinical use of actuarial prediction methods. First, clinicians may be averse to actuarial methods that require calculations. To remedy this, we developed a regression tree screen that presents actuarial information about violence in a series of yes/no questions. Second, using actuarial methods to identify the small minority of violent patients in a general psychiatric population may be too costly. To remedy this, we developed a method to prescreen patients for intensive evaluation using an inexpensive assessment. We evaluated regression trees and two-stage screening by comparing their accuracies against conventional actuarial methods. The results showed that actuarial predictions based on regression trees and two-stage screens were as accurate as regression-based methods in identifying repetitively violent patients. These easier-to-use methods may therefore be useful techniques for actuarial predictions.