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.
Matching patients and healthcare service providers Knowledge rules Multi-objective optimization model Ordinal weighting average non-dominated sorting genetic algorithm II (OWA-NSGA-II)
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This work was partially supported by the National Natural Science Foundation of China under Grants 71473188, 71601133, the Fundamental Research Funds for the Central Universities under Grant JB170606.
Ashour OM, Kremer GEO (2016) Dynamic patient grouping and prioritization: a new approach to emergency department flow improvement. Health Care Manag Sci 19(2):192–205CrossRefGoogle Scholar
Anderson K, Zheng B, Yoon SW, Khasawneh MT (2015) An analysis of overlapping appointment scheduling model in an outpatient clinic. Oper Res Health Care 4:5–14CrossRefGoogle Scholar
Saremi A, Jula P, Elmekkawy T, Wang GG (2015) Bi-criteria appointment scheduling of patients with heterogeneous service sequences. Expert Syst Appl 42(8):4029–4041CrossRefGoogle Scholar
Tudela P, Mòdol JM (2015) On hospital emergency department crowding. Emergencias 27(2):113–120Google Scholar
Tang J, Yan C, Cao P (2014) Appointment scheduling algorithm considering routine and urgent patients. Expert Syst Appl 41(10):4529–4541CrossRefGoogle Scholar
Vatsalan D, Christen P (2016) Privacy-preserving matching of similar patients. J Biomed Inform 59:285–298CrossRefGoogle Scholar
Ward MJ, Baker O, Schuur J (2014) Abstract 16216: Emergency department crowding does not predict door-in-door-out time for acute myocardial infarction patients transferred for acute coronary intervention. Circulation 130:A16216–A16216Google Scholar
Wang B, Han X, Zhang X, Zhang S (2015a) Predictive-reactive scheduling for single surgical suite subject to random emergency surgery. J Comb Optim 30(4):949–966MathSciNetCrossRefzbMATHGoogle Scholar
Wang D, Liu F, Yin Y, Wang J, Wang Y (2015b) Prioritized surgery scheduling in face of surgeon tiredness and fixed off-duty period. J Comb Optim 30(4):967–981MathSciNetCrossRefzbMATHGoogle Scholar
Xu M, Wong TC, Chin KS (2014) A healthcare procedure-based patient grouping method for an emergency department. Appl Soft Comput 14(1):31–37CrossRefGoogle Scholar