The review panel process: an algorithm for the conditional release of insanity acquittees

The release of insanity acquittees requires making informed decisions regarding both the presence and severity of an individuals' mental illness and the dangerousness of these individuals. This study evaluated the usefulness of employing structured assessments of mental health and violence risk factors in the conditional release decision-making process. All persons found Not Guilty by Reason of Insanity at East Louisiana Mental Health System, Forensic Division who underwent a review panel between July 1, 1997 and July 1, 1999 were included in this study. The Classification and Regression Tree analysis was utilized to arrive at cutpoints that would optimize the predictive ability of the decision tree analysis. The results indicated that the Community Outpatient Treatment Readiness Profile score was the strongest predictor -- all patients receiving a score of 62 or greater on this scale were recommended to remain at the facility. When women were recommended for release, it was to civil facilities and with moderate levels of symptoms. For males with moderate symptoms, low PCL-R scores were associated with recommendations for release, whereas high scores were associated with recommendations for continued commitment. Our data suggests that algorithms may be useful to governing bodies when making release decisions.