Matching patients and healthcare service providers: a novel two-stage method based on knowledge rules and OWA-NSGA-II algorithm

Xi Chen (), Liu Zhao (), Haiming Liang () and Kin Keung Lai ()
Additional contact information
Xi Chen: Xidian University
Liu Zhao: Xidian University
Haiming Liang: Xidian University
Kin Keung Lai: City University of Hong Kong

Journal of Combinatorial Optimization, 2019, vol. 37, issue 1, No 12, 247 pages

Abstract: Abstract The matching between patients and healthcare service providers is an important issue in healthcare. Searching an appropriate matching for both patients and healthcare service providers can not only facilitate efficiency of diagnosis and treatment, but also make both of them more satisfied with the matching results. This paper proposes a two-stage method for searching the optimal matching between the patients and healthcare service providers. In the first stage, where a large number of patients are involved in the matching problem, the knowledge rules are proposed to classify the patients with similar categories of disease into the same group. In the second stage, patients in each group are compared in terms of aspiration levels and the evaluation levels of the healthcare service providers, and satisfaction degrees of patients are calculated. Then, a multi-objective optimization model is built by maximizing the satisfaction degrees of patients, maximizing the number of treated patients and balancing the workload of healthcare service providers. To solve this model, the ordinal weighting average non-dominated sorting genetic algorithm II (OWA-NSGA-II) is developed. Furthermore, a practical example of service in rehabilitation therapy is used to illustrate the feasibility of the proposed method. Additionally, several simulation experiments in different large scale problems are conducted to test the performance of OWA-NSGA-II. Simulation results show that the proposed NSGA-II algorithm has better convergence in the large scale problem, yields a more stable distribution of non-dominated solutions, as well as non-dominated solutions much faster.

Keywords: Matching patients and healthcare service providers; Knowledge rules; Multi-objective optimization model; Ordinal weighting average non-dominated sorting genetic algorithm II (OWA-NSGA-II) (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

Downloads: (external link)
http://link.springer.com/10.1007/s10878-017-0221-2 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:jcomop:v:37:y:2019:i:1:d:10.1007_s10878-017-0221-2

Ordering information: This journal article can be ordered from
https://www.springer.com/journal/10878

DOI: 10.1007/s10878-017-0221-2

Access Statistics for this article

Journal of Combinatorial Optimization is currently edited by Thai, My T.

More articles in Journal of Combinatorial Optimization from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().