Abstract
Decision-theoretic criteria are presented for optimizing the information gathered from a series of interviews over time. It is shown that the optimum interviewing strategy depends strongly on assumptions about the covariation of behavior over time. Standard interviewing strategies, including the major-problem/target-complaints approach, are optimal only under extreme assumptions about behavior. An interviewing strategy based on dynamic programming is presented that will provide optimal information return from a series of interviews under assumptions that are realistic for mental health applications. A system using this approach can tailor its interviewing strategy to adapt to differences in interview content, item importance, and individual response patterns, selecting the optimally informative questions to ask each subject at each point in time. Simulation results show that this approach achieves a 34% reduction in the false negatives obtained with the major-problem/target-complaints method, and, depending on the acceptable error rate, a reduction of 47 % or more in the questions that are needed in standard interviewing.
Highlights
Recent research on psychopathology suggests that many questions about the classification of psychiatric disorders, and their prognoses, can be answered only by monitoring changes in symptom patterns over time (Grossman, Harrow, Fudala, & Meltzer, 1984; Stout, 1984)
The Problem Appraisal Scale (PAS) data used in this research were produced under National Institute of Mental Health Grant MH 26012, "Problems as Predictors of Treatment and Outcome," to Richard Longabaugh and Robert Stout
Our results show that this technique is capable of producing data of high quality under live conditions, with a population of acute psychiatric patients interviewed about topics common to most follow-up instruments
Summary
A system using this approach can tailor its interviewing strategy to adapt to differences in interview content, item importance, and individual response patterns, selecting the optimally informative questions to ask each subject at each point in time. Researchers who wish to do repeated assessments have found themselves forced to make painful compromises with respect to the number of interviews per patient, the number of questions per interview, and/or the quality of the data gathered because of the limitations imposed by time, cost, and human tolerance. 142 STOUT, STEVENSON, FARAONE, AND SIMPSON many compromises in research design. Among these compromises are the elimination of desirable but nonessential questions, a reduction in the number and/or increase in the spacing between interviews, and the acceptance of less reliable information. Reliability is often compromised by a reliance on relatively general, abstract questions when specific, concrete questions would provide data of higher validity (Angle, Ellinwood, & Carroll, 1978)
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