Phase 1:Our study team completed Phase I of the project in June, 2011. Our initial plans called for our team to analyze RT data that was collected by Delaware treatment programs to determine specific predictors for early attrition from treatment. Eleven counselors were trained to use RT as part of their standard practice in May, 2011. In an effort to generate a predictive algorithm for Phase II, we analyzed data from three datasets available from IRB approved research projects. However, the results were inconclusive, possibly due to small sample sizes and differences in the recruitment and definition of drop out across samples. In order to proceed with Phase II, we created an algorithm based largely on severity of substance use, a consistent predictor of drop-out across the alcohol and illicit substance literature; the algorithm also addresses when clients are not using but their Risk factors greatly outweigh the influence of Protective factors. We determined that the algorithm did work to identify clients who self-reported using substances or high levels of risk. However, the identification rate of high-risk cases was too low, and so we modified the algorithm to identify: 1) clients with high use at baseline, 2) clients with positive urine samples, 3) clients with any use at subsequent [post-baseline] interviews, and 4) clients whose risk factors outweighed their protective factors. This increased our rate of identification of risk cases by about 32%.