Predicting Addiction Severity Index (ASI) Interviewer Severity Ratings fora computer-administered ASI

The Addiction Severity Index (ASI) is a reliable and valid measure of probl em severity among addicted patients. Concerns have been raised about the re liability of the Interviewer Severity Rating (ISR), a summary score for eac h of 7 domains. As part of an effort to build a computer-administered ASI, regression equations were developed to predict the ISR. Repeated resampling of a large dataset, consisting of 1,124 ASIs conducted by trained intervie wers, permitted derivation of stable regression equations predicting the IS R for each ASI domain from patients' answers to preselected interview items . The resulting 7 Predicted Severity Ratings (PSRs) were tested on 8, stand ardized vignettes, with "gold standard," expert-generated ISRs. Reliabiliti es compared well with those of intensively trained interviewers. The PSRs c ould provide an alternative to potentially unreliable interviewer ratings, enhancing the ASI's role in treatment planning and treatment matching and m ake possible a computer-administered version of the ASI.