IDEAS home Printed from https://ideas.repec.org/a/kap/hcarem/v25y2022i1d10.1007_s10729-021-09582-0.html
   My bibliography  Save this article

Stroke care networks and the impact on quality of care

Author

Listed:
  • Jan Schoenfelder

    (University of Augsburg)

  • Mansour Zarrin

    (University of Augsburg)

  • Remo Griesbaum

    (University of Augsburg)

  • Ansgar Berlis

    (University Hospital Augsburg)

Abstract

Lack of rapidly available neurological expertise, especially in rural areas, is one of the key obstacles in stroke care. Stroke care networks attempt to address this challenge by connecting hospitals with specialized stroke centers, stroke units, and hospitals of lower levels of care. While the benefits of stroke care networks are well-documented, travel distances are likely to increase when patients are transferred almost exclusively between members of the same network. This is particularly important for patients who require mechanical thrombectomy, an increasingly employed treatment method that requires equipment and expertise available in specialized stroke centers. This study aims to analyze the performance of the current design of stroke care networks in Bavaria, Germany, and to evaluate the improvement potential when the networks are redesigned to minimize travel distances. To this end, we define three fundamental criteria for assessing network design performance: 1) average travel distances, 2) the populace in the catchment area relative to the number of stroke units, and 3) the ratio of stroke units to lower-care hospitals. We generate several alternative stroke network designs using an analytical approach based on mathematical programming and clustering. Finally, we evaluate the performance of the existing networks in Bavaria via simulation. The results show that the current network design could be significantly improved concerning the average travel distances. Moreover, the existing networks are unnecessarily imbalanced when it comes to their number of stroke units per capita and the ratio of stroke units to lower-care hospitals.

Suggested Citation

  • Jan Schoenfelder & Mansour Zarrin & Remo Griesbaum & Ansgar Berlis, 2022. "Stroke care networks and the impact on quality of care," Health Care Management Science, Springer, vol. 25(1), pages 24-41, March.
  • Handle: RePEc:kap:hcarem:v:25:y:2022:i:1:d:10.1007_s10729-021-09582-0
    DOI: 10.1007/s10729-021-09582-0
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10729-021-09582-0
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s10729-021-09582-0?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Boujelben, Mohamed Ayman, 2017. "A unicriterion analysis based on the PROMETHEE principles for multicriteria ordered clustering," Omega, Elsevier, vol. 69(C), pages 126-140.
    2. Sarrazin, R. & De Smet, Y. & Rosenfeld, J., 2018. "An extension of PROMETHEE to interval clustering," Omega, Elsevier, vol. 80(C), pages 12-21.
    3. De Smet, Yves & Nemery, Philippe & Selvaraj, Ramkumar, 2012. "An exact algorithm for the multicriteria ordered clustering problem," Omega, Elsevier, vol. 40(6), pages 861-869.
    4. M Pidd, 2010. "Why modelling and model use matter," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(1), pages 14-24, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Sarah Ben Amor & Fateh Belaid & Ramzi Benkraiem & Boumediene Ramdani & Khaled Guesmi, 2023. "Multi-criteria classification, sorting, and clustering: a bibliometric review and research agenda," Annals of Operations Research, Springer, vol. 325(2), pages 771-793, June.
    2. Pereira, Javier & Contreras, Pedro & Morais, Danielle C. & Arroyo-López, Pilar, 2022. "Multi-criteria ordered clustering of countries in the Global Health Security Index," Socio-Economic Planning Sciences, Elsevier, vol. 84(C).
    3. Díaz, Raymundo & Fernández, Eduardo & Figueira, José-Rui & Navarro, Jorge & Solares, Efrain, 2023. "A new hierarchical multiple criteria ordered clustering approach as a complementary tool for sorting and ranking problems," Omega, Elsevier, vol. 117(C).
    4. Tang, Ming & Liao, Huchang, 2021. "From conventional group decision making to large-scale group decision making: What are the challenges and how to meet them in big data era? A state-of-the-art survey," Omega, Elsevier, vol. 100(C).
    5. Pereira, Javier & Contreras, Pedro & Morais, Danielle C. & Arroyo-López, Pilar, 2022. "A multi-criteria and stochastic robustness analysis approach to compare nations sustainability," Socio-Economic Planning Sciences, Elsevier, vol. 80(C).
    6. Ishizaka, Alessio & Lokman, Banu & Tasiou, Menelaos, 2021. "A Stochastic Multi-criteria divisive hierarchical clustering algorithm," Omega, Elsevier, vol. 103(C).
    7. Gong, Zaiwu & Guo, Weiwei & Słowiński, Roman, 2021. "Transaction and interaction behavior-based consensus model and its application to optimal carbon emission reduction," Omega, Elsevier, vol. 104(C).
    8. Lund, Henrik & Mathiesen, Brian Vad, 2012. "The role of Carbon Capture and Storage in a future sustainable energy system," Energy, Elsevier, vol. 44(1), pages 469-476.
    9. Khaled Belahcène & Vincent Mousseau & Wassila Ouerdane & Marc Pirlot & Olivier Sobrie, 2023. "Multiple criteria sorting models and methods—Part I: survey of the literature," 4OR, Springer, vol. 21(1), pages 1-46, March.
    10. Kadziński, Miłosz & Tervonen, Tommi & Rui Figueira, José, 2015. "Robust multi-criteria sorting with the outranking preference model and characteristic profiles," Omega, Elsevier, vol. 55(C), pages 126-140.
    11. Gogi, Anastasia & Tako, Antuela A. & Robinson, Stewart, 2016. "An experimental investigation into the role of simulation models in generating insights," European Journal of Operational Research, Elsevier, vol. 249(3), pages 931-944.
    12. Liao, Huchang & Wu, Xingli & Mi, Xiaomei & Herrera, Francisco, 2020. "An integrated method for cognitive complex multiple experts multiple criteria decision making based on ELECTRE III with weighted Borda rule," Omega, Elsevier, vol. 93(C).
    13. Maria Ljunggren Söderman & Ola Eriksson & Anna Björklund & Göran Östblom & Tomas Ekvall & Göran Finnveden & Yevgeniya Arushanyan & Jan-Olov Sundqvist, 2016. "Integrated Economic and Environmental Assessment of Waste Policy Instruments," Sustainability, MDPI, vol. 8(5), pages 1-21, April.
    14. Alberto Franco, L., 2013. "Rethinking Soft OR interventions: Models as boundary objects," European Journal of Operational Research, Elsevier, vol. 231(3), pages 720-733.
    15. Shahab Shoar & Nicholas Chileshe, 2021. "Exploring the Causes of Design Changes in Building Construction Projects: An Interpretive Structural Modeling Approach," Sustainability, MDPI, vol. 13(17), pages 1-23, August.
    16. Stephan Onggo & Michael Pidd & Didier Soopramanien & Dave Worthington, 2010. "Simulation of Career Development in the European Commission," Interfaces, INFORMS, vol. 40(3), pages 184-195, June.
    17. Chakhar, Salem & Ishizaka, Alessio & Thorpe, Andy & Cox, Joe & Nguyen, Thang & Ford, Liz, 2020. "Calculating the relative importance of condition attributes based on the characteristics of decision rules and attribute reducts: Application to crowdfunding," European Journal of Operational Research, Elsevier, vol. 286(2), pages 689-712.
    18. Keshtkaran, Mahsa & Churilov, Leonid & Hearne, John & Abbasi, Babak & Meretoja, Atte, 2016. "Validation of a decision support model for investigation and improvement in stroke thrombolysis," European Journal of Operational Research, Elsevier, vol. 253(1), pages 154-169.
    19. Cascón, J.M. & González-Arteaga, T. & de Andrés Calle, R., 2022. "A new preference classification approach: The λ-dissensus cluster algorithm," Omega, Elsevier, vol. 111(C).
    20. Sarrazin, R. & De Smet, Y. & Rosenfeld, J., 2018. "An extension of PROMETHEE to interval clustering," Omega, Elsevier, vol. 80(C), pages 12-21.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:kap:hcarem:v:25:y:2022:i:1:d:10.1007_s10729-021-09582-0. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.