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It has been established that high no-show rates of publicly supported health systems in economically depressed areas are largely due to a lack of inexpensive, reliable transportation. The purpose of this paper is to determine the financial feasibility of offering transportation and investigate the net cost savings by reducing no-show rates. The approach starts with a data analysis on 636 patients at the Family Health Center (FHC) in San Antonio, Texas, followed by logistic regression to determine the impact of various transportation factors on cancellations/no-shows and late arrivals. We then investigate the costs savings that could be realised by reducing the no-show rate from 24.3% by up to 60%. Finally, we analyse the expenses that would be incurred should the FHC provide transportation. The full analysis indicates a cost reduction of more than $15,000 per month can be achieved when the no-show rate is reduced by 25% down to 18.2%.

The purpose of this paper is to investigate the problem of constructing an appointment template for scheduling patients at a specific type of multidisciplinary outpatient clinic called an integrated practice unit (IPU). The focus is on developing and solving a stochastic optimization model for a back pain IPU in the face of random arrivals, an uncertain patient mix, and variable service times. The deterministic version of the problem is modeled as a mixed integer program with the objective of minimizing a weighted combination of clinic closing time (duration) and total patient waiting time (length of stay). A two‐stage stochastic program is then derived to account for the randomness and the sequential nature of the decisions. Although it was not possible to solve the two‐stage problem for even a limited number of scenarios, the wait‐and‐see (WS) problem was sufficiently tractable to provide a lower bound on the stochastic solution. The introduction of valid inequalities, limiting indices, and the use of special ordered sets helped to speed up the computations. A greedy heuristic was also developed to obtain solutions much more quickly. Out of practical considerations, it was necessary to develop appointment templates with time slots at fixed intervals, which are not available from the WS solution. The first to be derived was the expected value (EV) template that is used to find the expected value of the EV solution (EEV). This solution provides an upper bound on the objective function value of the two‐stage stochastic program. The average gap between the EEV and WS solutions was 18%. Results from extensive computational testing are presented for the EV template and for our adaptation of three other templates found in the literature. Depending on the relative importance of the two objective function metrics, the results demonstrate the trade‐off that exists between them. For the templates investigated, the “closing time” ranged from an average of 235 to 275 minutes for a 300‐minute session, while the corresponding “total patient time in clinic” ranged from 80 to 71 minutes.