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. 2020 Aug 2;44(9):157.
doi: 10.1007/s10916-020-01608-4.

Agent-oriented Decision Support System for Business Processes Management with Genetic Algorithm Optimization: an Application in Healthcare

Emilio Sulis  1 , Pietro Terna  2 , Antonio Di Leva  3 , Guido Boella  3 , Adriana Boccuzzi  4
Affiliations

Agent-oriented Decision Support System for Business Processes Management with Genetic Algorithm Optimization: an Application in Healthcare

Emilio Sulis et al. J Med Syst. .

Abstract

Agent-based approaches have been known to be appropriate as systems and methods in medical administration in recent years. The increased attention to processes led to the recent growth of Business Process Management discipline, which quite exclusively adopt discrete-event modeling and simulation. This paper proposes a medical agent-oriented decision support system to integrate the achievements from management science, agent-based modeling, and artificial intelligence. In particular, we performed a practical application concerning a hospital emergency department medical system. We adopt the widely used multi-agent programmable modeling environment NetLogo. First, we demonstrated the ability to perform a clear representation of healthcare processes where agents (i.e., patients and hospital staff) operate in a 3D environment. This model allows performing a traditional what-if scenario analysis. Second, we explore how performing intelligent management of patients by applying genetic algorithms to find the criteria for the selection process of the subjects in the admission procedure. The results are encouraging towards a more extensive application of agent-oriented methodologies in healthcare management.

Keywords: Agent-based decision support system; Business process management; Emergency department planning; Genetic algorithm.

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Conflict of interest statement

The authors declare that they have no conflicts of interest.

Figures

Fig. 1
Fig. 1
Interface with buttons, monitors and the output area in a 3D version of the Emergency Department to better appreciate operators and patients movements
Fig. 2
Fig. 2
Triage and registration process in BPMN
Fig. 3
Fig. 3
Parameter sweeping results of ten runs by varying “priority-criteria” to minimize QS
Fig. 4
Fig. 4
GA fitness Quality Score reaches a minimum value of 2,941, greatly improving the value obtained with the brute-force approach
See this image and copyright information in PMC

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