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 inJournal of biomedical informatics Vol. 73; pp. 148 - 158
Main Authors Chen, Ping-Shun, Lin, Ming-Han
Format Journal Article
LanguageEnglish
Published United States Elsevier Inc 01.09.2017
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Online AccessGet full text
ISSN1532-0464
1532-0480
1532-0480
DOI10.1016/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.
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
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Keywords Hospital collaboration
Patient referral
Heuristic algorithm
Simulation optimization
Particle swarm optimization
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Snippet [Display omitted] •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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StartPage 148
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
URI https://dx.doi.org/10.1016/j.jbi.2017.08.004
https://www.ncbi.nlm.nih.gov/pubmed/28802837
https://www.proquest.com/docview/1928783654
Volume 73
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