Advertisement

Journal of Combinatorial Optimization

, Volume 37, Issue 1, pp 221–247 | Cite as

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 LiangEmail author
  • Kin Keung Lai
Article
  • 184 Downloads

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) 

Notes

Acknowledgements

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.

References

  1. 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
  2. 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
  3. Begen MA, Queyranne M (2011) Appointment scheduling with discrete random durations. Math Oper Res 36(36):845–854MathSciNetzbMATHGoogle Scholar
  4. Begen MA, Levi R, Queyranne M (2012) Technical note-a sampling-based approach to appointment scheduling. Oper Res 60(3):675–681MathSciNetCrossRefzbMATHGoogle Scholar
  5. Castner J (2011) Emergency department triage: what data are nurses collecting. J Emerg Nurs 37(4):417–422CrossRefGoogle Scholar
  6. Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evolut Comput 6(2):182–197CrossRefGoogle Scholar
  7. Elalouf A, Wachtel G (2016) An alternative scheduling approach for improving emergency department performance. Int J Prod Econ 178:65–71CrossRefGoogle Scholar
  8. Güler MG (2013) A hierarchical goal programming model for scheduling the outpatient clinics. Expert Syst Appl 40(12):4906–4914CrossRefGoogle Scholar
  9. He C, Fan X, Li Y (2012) Toward ubiquitous healthcare services with a novel efficient cloud platform. IEEE Trans Bio-med Eng 60(1):230–234CrossRefGoogle Scholar
  10. Liu N (2016) Optimal choice for appointment scheduling window under patient no-show behavior. Prod Oper Manag 25(1):128–142CrossRefGoogle Scholar
  11. Legrain A, Fortin MA, Lahrichi N, Rousseau LM (2015) Online stochastic optimization of radiotherapy patient scheduling. Health Care Manag Sci 18(2):110–123CrossRefGoogle Scholar
  12. Ma X, Sauré A, Puterman ML, Taylor M, Tyldesley S (2016) Capacity planning and appointment scheduling for new patient oncology consults. Health Care Manag Sci 19(4):347–361CrossRefGoogle Scholar
  13. Ogulata SN, Koyuncu M, Karakas E (2008) Personnel and patient scheduling in the high demanded hospital services: a case study in the physiotherapy service. J Healthc Syst 32(3):221–228Google Scholar
  14. Saaty TL (1980) The analytic hierarchy process. McGraw-Hill, New YorkzbMATHGoogle Scholar
  15. 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
  16. Tudela P, Mòdol JM (2015) On hospital emergency department crowding. Emergencias 27(2):113–120Google Scholar
  17. Tang J, Yan C, Cao P (2014) Appointment scheduling algorithm considering routine and urgent patients. Expert Syst Appl 41(10):4529–4541CrossRefGoogle Scholar
  18. Vatsalan D, Christen P (2016) Privacy-preserving matching of similar patients. J Biomed Inform 59:285–298CrossRefGoogle Scholar
  19. 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
  20. 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
  21. 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
  22. 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
  23. Yang Y, Shen B, Gao W, Liu Y, Zhong L (2015) A surgical scheduling method considering surgeons’ preferences. J Comb Optim 30(4):1016–1026MathSciNetCrossRefzbMATHGoogle Scholar
  24. Yan C, Tang J, Jiang B, Fung RYK (2015) Sequential appointment scheduling considering patient choice and service fairness. Int J Prod Res 53(24):7376–7395CrossRefGoogle Scholar
  25. Zadeh LA (1983) A computational approach to fuzzy quantifiers in natural languages. Comput Math Appl 9(1):149–184MathSciNetCrossRefzbMATHGoogle Scholar
  26. Zhong L, Luo S, Wu L, Xu L, Yang J, Tang G (2014) A two-stage approach for surgery scheduling. J Comb Optim 27(3):545–556MathSciNetCrossRefzbMATHGoogle Scholar
  27. Zander A, Mohring U (2016) Dynamic appointment scheduling with patient time preferences and different service time lengths. Int Conf Appl Oper Res 8:72–77Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2017

Authors and Affiliations

  • Xi Chen
    • 1
  • Liu Zhao
    • 1
  • Haiming Liang
    • 1
    Email author
  • Kin Keung Lai
    • 2
  1. 1.School of Economics and ManagementXidian UniversityXi’anChina
  2. 2.Department of Management SciencesCity University of Hong KongHong KongChina

Personalised recommendations