Development of simulation optimization methods for solving patient referral problems in the hospital-collaboration environment
[Display omitted] •We study a patient referral problem among multiple cooperative hospitals.•The decision is to assign the daily patients referred from one hospital to another.•We develop three patient referral mechanisms with the PSO to solve the problem.•The results show that Mechanism 2 have good...
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Published in | Journal of biomedical informatics Vol. 73; pp. 148 - 158 |
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Main Authors | , |
Format | Journal Article |
Language | English |
Published |
United States
Elsevier Inc
01.09.2017
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Subjects | |
Online Access | Get full text |
ISSN | 1532-0464 1532-0480 1532-0480 |
DOI | 10.1016/j.jbi.2017.08.004 |
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Abstract | [Display omitted]
•We study a patient referral problem among multiple cooperative hospitals.•The decision is to assign the daily patients referred from one hospital to another.•We develop three patient referral mechanisms with the PSO to solve the problem.•The results show that Mechanism 2 have good performance in both scenarios.
This research studied a patient referral problem among multiple cooperative hospitals for sharing imaging services’ referrals. The proposed problem consisted of many types of patients and the uncertainty associated with the number of patients of each type, patients’ arrival time, and patients’ medical operation time, leading to a difficulty in finding solutions due to the uncertain environment. This research used system simulation to construct a model and develop a simulation optimization method, combining the heuristic algorithm (patient referral mechanism) with the particle swarm optimization (PSO) method, to determine a better way to refer patients from one hospital (referring hospital) to another (recipient hospital) to receive certain imaging services. After the simulated model was verified and validated, three patient referral mechanisms to dispatch referring patients to the appropriate recipient hospitals were proposed. Based on the numerical results, the findings showed that Mechanism 2, transferring patients to the hospital with the shortest waiting time, had good performance in both scenarios: allowing patient referrals among all hospitals and limiting the patients’ waiting time. Finally, this study presents the conclusions and some directions for future research. |
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AbstractList | This research studied a patient referral problem among multiple cooperative hospitals for sharing imaging services' referrals. The proposed problem consisted of many types of patients and the uncertainty associated with the number of patients of each type, patients' arrival time, and patients' medical operation time, leading to a difficulty in finding solutions due to the uncertain environment. This research used system simulation to construct a model and develop a simulation optimization method, combining the heuristic algorithm (patient referral mechanism) with the particle swarm optimization (PSO) method, to determine a better way to refer patients from one hospital (referring hospital) to another (recipient hospital) to receive certain imaging services. After the simulated model was verified and validated, three patient referral mechanisms to dispatch referring patients to the appropriate recipient hospitals were proposed. Based on the numerical results, the findings showed that Mechanism 2, transferring patients to the hospital with the shortest waiting time, had good performance in both scenarios: allowing patient referrals among all hospitals and limiting the patients' waiting time. Finally, this study presents the conclusions and some directions for future research.This research studied a patient referral problem among multiple cooperative hospitals for sharing imaging services' referrals. The proposed problem consisted of many types of patients and the uncertainty associated with the number of patients of each type, patients' arrival time, and patients' medical operation time, leading to a difficulty in finding solutions due to the uncertain environment. This research used system simulation to construct a model and develop a simulation optimization method, combining the heuristic algorithm (patient referral mechanism) with the particle swarm optimization (PSO) method, to determine a better way to refer patients from one hospital (referring hospital) to another (recipient hospital) to receive certain imaging services. After the simulated model was verified and validated, three patient referral mechanisms to dispatch referring patients to the appropriate recipient hospitals were proposed. Based on the numerical results, the findings showed that Mechanism 2, transferring patients to the hospital with the shortest waiting time, had good performance in both scenarios: allowing patient referrals among all hospitals and limiting the patients' waiting time. Finally, this study presents the conclusions and some directions for future research. This research studied a patient referral problem among multiple cooperative hospitals for sharing imaging services' referrals. The proposed problem consisted of many types of patients and the uncertainty associated with the number of patients of each type, patients' arrival time, and patients' medical operation time, leading to a difficulty in finding solutions due to the uncertain environment. This research used system simulation to construct a model and develop a simulation optimization method, combining the heuristic algorithm (patient referral mechanism) with the particle swarm optimization (PSO) method, to determine a better way to refer patients from one hospital (referring hospital) to another (recipient hospital) to receive certain imaging services. After the simulated model was verified and validated, three patient referral mechanisms to dispatch referring patients to the appropriate recipient hospitals were proposed. Based on the numerical results, the findings showed that Mechanism 2, transferring patients to the hospital with the shortest waiting time, had good performance in both scenarios: allowing patient referrals among all hospitals and limiting the patients' waiting time. Finally, this study presents the conclusions and some directions for future research. [Display omitted] •We study a patient referral problem among multiple cooperative hospitals.•The decision is to assign the daily patients referred from one hospital to another.•We develop three patient referral mechanisms with the PSO to solve the problem.•The results show that Mechanism 2 have good performance in both scenarios. This research studied a patient referral problem among multiple cooperative hospitals for sharing imaging services’ referrals. The proposed problem consisted of many types of patients and the uncertainty associated with the number of patients of each type, patients’ arrival time, and patients’ medical operation time, leading to a difficulty in finding solutions due to the uncertain environment. This research used system simulation to construct a model and develop a simulation optimization method, combining the heuristic algorithm (patient referral mechanism) with the particle swarm optimization (PSO) method, to determine a better way to refer patients from one hospital (referring hospital) to another (recipient hospital) to receive certain imaging services. After the simulated model was verified and validated, three patient referral mechanisms to dispatch referring patients to the appropriate recipient hospitals were proposed. Based on the numerical results, the findings showed that Mechanism 2, transferring patients to the hospital with the shortest waiting time, had good performance in both scenarios: allowing patient referrals among all hospitals and limiting the patients’ waiting time. Finally, this study presents the conclusions and some directions for future research. |
Author | Lin, Ming-Han Chen, Ping-Shun |
Author_xml | – sequence: 1 givenname: Ping-Shun surname: Chen fullname: Chen, Ping-Shun email: pingshun@cycu.edu.tw – sequence: 2 givenname: Ming-Han surname: Lin fullname: Lin, Ming-Han email: john09lin@hotmail.com |
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Keywords | Hospital collaboration Patient referral Heuristic algorithm Simulation optimization Particle swarm optimization |
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•We study a patient referral problem among multiple cooperative hospitals.•The decision is to assign the daily patients referred from one... This research studied a patient referral problem among multiple cooperative hospitals for sharing imaging services' referrals. The proposed problem consisted... |
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SubjectTerms | Algorithms Computer Simulation Heuristic algorithm Hospital collaboration Hospitals Humans Particle swarm optimization Patient referral Problem Solving Referral and Consultation Simulation optimization |
Title | Development of simulation optimization methods for solving patient referral problems in the hospital-collaboration environment |
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