• Yasin Gocgun, Brian W Bresnahan, Archis Ghate, and Martin L Gunn.
• Operations and Logistics Division, Sauder School of Business, University of British Columbia, Vancouver, Canada. yasin.gocgun@sauder.ubc.ca
• Artif Intell Med. 2011 Oct 1;53(2):73-81.

ObjectivesTo develop a mathematical model for multi-category patient scheduling decisions in computed tomography (CT), and to investigate associated tradeoffs from economic and operational perspectives.MethodsWe modeled this decision-problem as a finite-horizon Markov decision process (MDP) with expected net CT revenue as the performance metric. The performance of optimal policies was compared with five heuristics using data from an urban hospital. In addition to net revenue, other patient-throughput and service-quality metrics were also used in this comparative analysis.ResultsThe optimal policy had a threshold structure in the two-scanner case - it prioritized one type of patient when the queue-length for that type exceeded a threshold. The net revenue gap between the optimal policy and the heuristics ranged from 5% to 12%. This gap was 4% higher in the more congested, single-scanner system than in the two-scanner system. The performance of the net revenue maximizing policy was similar to the heuristics, when compared with respect to the alternative performance metrics in the two-scanner case. Under the optimal policy, the average number of patients that were not scanned by the end of the day, and the average patient waiting-time, were both nearly 80% smaller in the two-scanner case than in the single-scanner case. The net revenue gap between the optimal policy and the priority-based heuristics was nearly 2% smaller as compared to the first-come-first-served and random selection schemes. Net revenue was most sensitive to inpatient (IP) penalty costs in the single-scanner system, whereas to IP and outpatient revenues in the two-scanner case.ConclusionsThe performance of the optimal policy is competitive with the operational and economic metrics considered in this paper. Such a policy can be implemented relatively easily and could be tested in practice in the future. The priority-based heuristics are next-best to the optimal policy and are much easier to implement.Copyright © 2011 Elsevier B.V. All rights reserved.

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