A decision support system for partial drug testing: DSS-DT

Abstract

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.