A decision support system for partial drug testing

This paper presents a Decision Support System (DSS) for the application of partial drug testing to a population of individuals with a history of drug abuse. The need for such a system arose in response to a 40% reduction in drug testing funds allocated to probation offices in the State of Illinois' Intensive Drug Supervision Programs (IDSP) in 1995. Recent work in adapting single-attribute Bayesian acceptance sampling to the problem of drug testing in ‘at risk’ populations has shown that the total cost of sampling can be reduced without adversely affecting the proportion of users in the population. The DSS for Drug Testing (DSS-DT) allows users the opportunity to: (1) readily access information about the prior distribution of drug use by population and drug type; (2) generate optimal sampling plans based on current population inputs; (3) generate near-optimal sampling plans using a heuristic; and (4) evaluate the sensitivity of the solution to changes in various input parameters for the drug testing model. Use of DSS-DT expedites the dissemination of the partial drug testing results while offering information and budget planning support to planners charged with implementing a random drug testing procedure.