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

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. Th...

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Vydáno v:Journal of medical systems Ročník 44; číslo 9; s. 157
Hlavní autoři: Sulis, Emilio, Terna, Pietro, Di Leva, Antonio, Boella, Guido, Boccuzzi, Adriana
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer US 01.09.2020
Springer Nature B.V
Témata:
Agents (artificial intelligence)
Algorithms
Artificial intelligence
Business process management
Computer simulation
Decision support systems
Education & Training
Emergency medical care
Emergency medical services
Environment models
Genetic algorithms
Health care
Health Informatics
Health Sciences
Healthcare Intelligent Multi-Agent Systems (HIMAS2020)
Medicine
Medicine & Public Health
Multiagent systems
Optimization
Patients
Statistics for Life Sciences
Three dimensional models
Business process management
Agent-based decision support system
Genetic algorithm
Emergency department planning
ISSN:0148-5598, 1573-689X, 1573-689X
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Shrnutí: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.
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ISSN:0148-5598
1573-689X
1573-689X
DOI:10.1007/s10916-020-01608-4