Skip to main content

Advertisement

Log in

Optimal cost adjustment for a selfish routing healthcare network

  • Published:
Health Care Management Science Aims and scope Submit manuscript

Abstract

South Korea’s large hospitals are severely burdened by patient congestion because patients throng to these places expecting to get treated better given their higher-quality healthcare. Effective cost management of the healthcare system is one way to reduce patient congestion in a large hospital. This study proposes methods that can help direct patient flows in a desirable direction and suggests ways to effectively manage the cost of healthcare. The study also discusses how selfish patients act in ways that maximize their benefits by choosing a specific hospital and in turn forcing the hospital and the healthcare network to bear more costs than is necessary. The study proposes a model describing the need for intervention from the government to control the cost escalation resulting from selfish routing. The study proposes two heuristic algorithms to solve the suggested model. The flow-based algorithm addresses the target quantum of flows, and the utility-based algorithm targets the value of cost functions. Performances of heuristics are evaluated through numerical experiments. The utility-based algorithm yields higher values for objectives, while the flow-based algorithm controls the extent of investment. A case study based on data from the Seoul city database is also analyzed. The cost adjustment policy is compared with simple, uniformly improved network policies, and findings show that such policies have the strength needed to improve the cost-effectiveness of the healthcare system if implemented fully and effectively.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Kang HJ (2014) Policy direction for decreasing the concentration of patients to extra-large hospitals. Health Welfare Policy Forum 4(210):65–76

    Google Scholar 

  2. Kwon SH (1999) Price regulation in the health care industry: effects and the directions for reform. Korean Policy Stud Rev 8(2):255–271

    Google Scholar 

  3. http://www.mohw.go.kr/react/policy/index.jsp?PAR_MENU_ID=06&MENU_ID=06320106&PAGE=6&topT, homepage of the Korean Ministry of Health and Welfare

  4. Kim YG (2010) Optimal increase rate of medical charge through the management evaluation of clinics. Healthc Policy Forum 8(3):43–53

    Google Scholar 

  5. Lee MW (2016) A study on the problems and solutions of demand concentration to large hospitals. Master’s dissertation, Korea University

  6. Mestre AM, Oliveira MD, Barbosa-Póvoa AP (2015) Location–allocation approaches for hospital network planning under uncertainty. Eur J Oper Res 240(3):791–806

    Article  Google Scholar 

  7. Fo ARAV, da Silva MI (2012) Optimization models in the location of healthcare facilities: a real case in Brazil. J Appl Oper Res 4(1):37–50

    Google Scholar 

  8. Shariff SR, Moin NH, Omar M (2012) Location allocation modeling for healthcare facility planning in Malaysia. Comput Ind Eng 62(4):1000–1010

    Article  Google Scholar 

  9. Cardoso T, Oliveira MD, Barbosa-Póvoa A, Nickel S (2015) An integrated approach for planning a long-term care network with uncertainty, strategic policy and equity considerations. Eur J Oper Res 247(1):321–334

    Article  Google Scholar 

  10. Martin CA (2014) Accounting for individual choice in public health emergency response planning. Kansas State University, Doctoral dissertation

    Google Scholar 

  11. Wardrop JG (1952) Road paper: some theoretical aspects of road traffic research. Proc Inst Civil Eng 1(3):325–362

    Google Scholar 

  12. Beckmann M, McGuire CB, Winsten CB (1956) Studies in the economics of transportation, pp:226

  13. Braess D (1968) Über ein Paradoxon aus der Verkehrsplanung. Unternehmensforschung 12(1):258–268

    Google Scholar 

  14. Roughgarden T (2002). The price of anarchy is independent of the network topology. In: Proceedings of the thirty-fourth annual ACM symposium on theory of computing. ACM, pp. 428–437

  15. Roughgarden T, Tardos É (2002) How bad is selfish routing? J ACM (JACM) 49(2):236–259

    Article  Google Scholar 

  16. Roughgarden T (2005) Selfish routing and the price of anarchy, vol 174. MIT Press, Cambridge

    Google Scholar 

  17. Hoefer M, Olbrich L, Skopalik A (2008). Taxing subnetworks. In: International workshop on internet and network economics. Springer, Berlin, Heidelberg, pp. 286–294

  18. Bonifaci V, Salek M, Schäfer G (2011). Efficiency of restricted tolls in non-atomic network routing games. In: International symposium on algorithmic game theory. Springer, Berlin, Heidelberg, pp. 302–313

  19. Jelinek T, Klaas M, Schäfer G (2014) Computing optimal tolls with arc restrictions and heterogeneous players. In LIPIcs-Leibniz international proceedings in informatics, Vol. 25. Schloss Dagstuhl-Leibniz-Zentrum fuer Informatik

  20. Andritsos DA, Tang CS (2013) The impact of cross-border patient movement on the delivery of healthcare services. Int J Prod Econ 145(2):702–712

    Article  Google Scholar 

  21. Knight VA, Harper PR (2013) Selfish routing in public services. Eur J Oper Res 230(1):122–132

    Article  Google Scholar 

  22. Baek JS (2016) Applying game theory and queueing system on measuring efficiency of healthcare system. Yonsei University, Masters dissertation

    Google Scholar 

  23. McIntosh E, Ryan M (2002) Using discrete choice experiments to derive welfare estimates for the provision of elective surgery: implications of discontinuous preferences. J Econ Psychol 23(3):367–382

    Article  Google Scholar 

  24. Hanson K, Yip WC, Hsiao W (2004) The impact of quality on the demand for outpatient services in Cyprus. Health Econ 13(12):1167–1180

    Article  Google Scholar 

  25. Coulter A, Le Maistre N, Henderson L (2005) Patients' experience of choosing where to undergo surgical treatment. Evaluation of London Patient Choice Scheme

  26. Varkevisser M, van der Geest SA (2007) Why do patients bypass the nearest hospital? An empirical analysis for orthopaedic care and neurosurgery in the Netherlands. Eur J Health Econ 8(3):287–295

    Article  Google Scholar 

  27. Kim S, Oh C (2012) Factors that affect decisions for selecting hospitals and different awareness-focusing on inpatient, care-giver, nurse in university hospital using AHP. J Korea Inst Healthc Archit 18(4):39–51

    Google Scholar 

  28. Hart M (1999) The quantification of patient satisfaction. In: Managing quality: strategic issues in health care management. Lavoisier, Aldershot

  29. Choi KS, Cho WH, Lee S, Lee H, Kim C (2004) The relationships among quality, value, satisfaction and behavioral intention in health care provider choice: a south Korean study. J Bus Res 57(8):913–921

    Article  Google Scholar 

  30. Roughgarden T (2006, August) Potential functions and the inefficiency of equilibria. Proceedings of the international congress of mathematicians (ICM) 3:1071–1094

    Google Scholar 

  31. http://data.seoul.go.kr/, public data about Seoul city

Download references

Acknowledgements

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (2017R1E1A1A03070757). We also would like to thank Editage (www.editage.co.kr) for English language editing.

Availability of data and material

All of data and material can be provided if requested.

Code availability

The mathematical models are solved using ILOG CPLEX 12.4, and the heuristic mechanism is coded in JAVA. JAVA code can be provided if requested.

Funding

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (2017R1E1A1A03070757).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Yong Ho Choi.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethics approval

Ethics approval was not needed.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix

Appendix

Detailed parameter estimates in section 6.1 are as follows:

Table 10 Demand and capacity allocation for each region
Table 11 Total capacity of hospitals for each patient group
Table 12 Accessibility costs for the five closest hospitals
Table 13 Crowdedness costs of each hospitals

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lee, Y.H., Choi, Y.H. Optimal cost adjustment for a selfish routing healthcare network. Health Care Manag Sci 23, 585–604 (2020). https://doi.org/10.1007/s10729-020-09512-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10729-020-09512-6

Keywords

Navigation