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. 2023 Jun 29;11(1):30.
doi: 10.1007/s13755-023-00230-1. eCollection 2023 Dec.

Patient assignment optimization in cloud healthcare systems: a distributed genetic algorithm

Xinyu Pang  1 , Yong-Feng Ge  2 , Kate Wang  3 , Agma J M Traina  4 , Hua Wang  2
Affiliations

Patient assignment optimization in cloud healthcare systems: a distributed genetic algorithm

Xinyu Pang et al. Health Inf Sci Syst. .

Abstract

Integrating Internet technologies with traditional healthcare systems has enabled the emergence of cloud healthcare systems. These systems aim to optimize the balance between online diagnosis and offline treatment to effectively reduce patients' waiting times and improve the utilization of idle medical resources. In this paper, a distributed genetic algorithm (DGA) is proposed as a means to optimize the balance of patient assignment (PA) in cloud healthcare systems. The proposed DGA utilizes individuals as solutions for the PA optimization problem and generates better solutions through the execution of crossover, mutation, and selection operators. Besides, the distributed framework in the DGA is proposed to improve its population diversity and scalability. Experimental results demonstrate the effectiveness of the proposed DGA in optimizing the PA problem within the cloud healthcare systems.

Keywords: Cloud healthcare system; Distributed genetic algorithm; Evolutionary algorithm; Patient assignment.

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Conflict of interest statement

Conflict of interestThe authors have no competing interests to declare that are relevant to the content of this article.

Figures

Fig. 1
Fig. 1
Illustration of the patient assignment in the cloud healthcare systems
Fig. 2
Fig. 2
Illustration of the representation manner in DGA
Fig. 3
Fig. 3
Illustration of distributed framework in DGA
Fig. 4
Fig. 4
Illustration of crossover operator in DGA
Fig. 5
Fig. 5
Illustration of mutation operator in DGA
Fig. 6
Fig. 6
Convergence curves of DGA and compared algorithms on six typical test instances
See this image and copyright information in PMC

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