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

TL;DR

This paper introduces an advanced multicriteria optimization approach using parallelized epsilon constraint method and KD-Trees to efficiently analyze and visualize the complex case mix landscape in hospitals, aiding capacity utilization decisions.

Contribution

It presents a novel parallel random epsilon constraint method and the application of KD-Trees for high-dimensional Pareto frontier management in hospital case mix optimization.

Findings

Faster generation of Pareto optimal solutions.

Effective visualization and querying of complex case mix data.

Enhanced decision support for hospital capacity planning.

Abstract

Various medical and surgical units operate in a typical hospital and to treat their patients these units compete for infrastructure like operating rooms (OR) and ward beds. How that competition is regulated affects the capacity and output of a hospital. This article considers the impact of treating different patient case mix (PCM) in a hospital. As each case mix has an economic consequence and a unique profile of hospital resource usage, this consideration is important. To better understand the case mix landscape and to identify those which are optimal from a capacity utilisation perspective, an improved multicriteria optimization (MCO) approach is proposed. As there are many patient types in a typical hospital, the task of generating an archive of non-dominated (i.e., Pareto optimal) case mix is computationally challenging. To generate a better archive, an improved parallelised epsilon constraint method (ECM) is introduced. Our parallel random corrective approach is significantly faster than prior methods and is not restricted to evaluating points 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 perform proximity testing and to store the high dimensional Pareto frontier (PF). For generating, viewing, navigating, and querying an archive, the development of a suitable decision support tool (DST) is proposed and demonstrated.