The incorporation of Bayesian logic into diagnostic interviewing may assist with empirically based diagnostic assessment strategies in practice settings, balancing cost effectiveness, administration demands, and accuracy, yet few demonstrations of such a system have been undertaken in the context of mental health diagnosis. The present study represented an initial feasibility demonstration of whether a simplified Bayesian approach offered comparative advantages in interview accuracy and efficiency against a standard assessment procedure. Two different diagnostic algorithms were compared targeting three selected diagnoses: generalized anxiety disorder (GAD), major depressive disorder (MDD), and social phobia (SP). The first algorithm was from a standard semi-structured diagnostic interview, and the second was from a dynamic system using diagnostic base rate information to select interview content. The dynamic algorithm reduced administration time and uniformly matched or improved accuracy over standard procedures.
