Patient Scheduling System for Medical Treatment Using Genetic Algorithm

Main Article Content

Karpagam.M
Department of Computational Intelligence, SRM Institute of Science and Technology, Kattankulathur, Chennai, 603203, India

Kanipriya.M
Department of Computational Intelligence, SRM Institute of Science and Technology, Kattankulathur, Chennai, 603203, India

K. Suresh
School of Computing Science and Engineering, Galgotias University, Greater Noida, Uttarpradesh

Briskilal Joseph
Department of Computing Technologies, SRM Institute of Science and Technology, Kattankulathur, Chennai, 603203, India

Keywords

genetic algorithm, rTMS, list scheduling, partially mapped crossover

Abstract

The manual scheduling of medical treatment in a health center is a complex, time consuming, and error prone task. The system takes into account various constraints such as patient preferences, physician availability, and resource allocation. The GA is used to optimize the scheduling of patients to physicians and to allocate resources to minimize the waiting time. The proposed system is tested using real-world data, and the results demonstrate that it can effectively reduce the total waiting time of patients and improve the efficiency of healthcare providers. This study contributes to the optimization of patient scheduling systems in the healthcare industry, and provides a valuable tool for healthcare providers to improve patient satisfaction and operational efficiency. Furthermore, there is no guarantee a manually generated schedule maximizes the operational efficiency of the center. Scheduling problems have seen extensive research across several domains. The current work presents a novel genetic algorithm for the scheduling of repetitive Transcranial Magnetic Stimulation (rTMS) appointments.

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Published
Apr 20, 2023
DOI https://doi.org/10.47750/jptcp.2023.30.08.029

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How to Cite
Karpagam.M, Kanipriya.M, K. Suresh, & Briskilal Joseph. (2023). Patient Scheduling System for Medical Treatment Using Genetic Algorithm. Journal of Population Therapeutics and Clinical Pharmacology, 30(8), 268–273. https://doi.org/10.47750/jptcp.2023.30.08.029
Issue
Vol. 30 No. 8 (2023): JPTCP
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