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Various medical and surgical units operate in a typical hospital and to treattheir patients these units compete for infrastructure like operating rooms (OR)and ward beds. How that competition is regulated affects the capacity andoutput of a hospital. This article considers the impact of treating differentpatient case mix (PCM) in a hospital. As each case mix has an economicconsequence and a unique profile of hospital resource usage, this considerationis important. To better understand the case mix landscape and to identify thosewhich are optimal from a capacity utilisation perspective, an improvedmulticriteria optimization (MCO) approach is proposed. As there are manypatient types in a typical hospital, the task of generating an archive ofnon-dominated (i.e., Pareto optimal) case mix is computationally challenging.To generate a better archive, an improved parallelised epsilon constraintmethod (ECM) is introduced. Our parallel random corrective approach issignificantly faster than prior methods and is not restricted to evaluatingpoints on a structured uniform mesh. As such we can generate more solutions.The application of KD-Trees is another new contribution. We use them to performproximity testing and to store the high dimensional Pareto frontier (PF). Forgenerating, viewing, navigating, and querying an archive, the development of asuitable decision support tool (DST) is proposed and demonstrated.

Multicriteria Optimization Techniques for Understanding the Case Mix Landscape of a Hospital