IDEAS home Printed from https://ideas.repec.org/a/pal/jorsoc/v60y2009i10d10.1057_jors.2008.109.html
   My bibliography  Save this article

The costs and benefits of bowel cancer service developments using discrete event simulation

Author

Listed:
  • H Pilgrim

    (University of Sheffield)

  • P Tappenden

    (University of Sheffield)

  • J Chilcott

    (University of Sheffield)

  • M Bending

    (University of York)

  • P Trueman

    (University of York)

  • A Shorthouse

    (St Mary's Hospital)

  • J Tappenden

    (Barnsley District General Hospital)

Abstract

Colorectal cancer includes cancerous growths in the colon, rectum and appendix and affects around 30 000 people in England each year. Maximizing health benefits for patients with colorectal cancer requires consideration of costs and outcomes across the whole service. In an era of scarce healthcare resources, there is a need to consider not only whether technologies and services may be considered clinically effective, but also whether they are cost-effective, that is, whether they represent value for money for the health service. Through the development of a whole disease model, it is possible to evaluate the cost-effectiveness of a range of options for service development consistently within a common framework. Discrete event simulation has been used to model the complete colorectal cancer patient pathway from patient presentation through to referral and diagnosis, treatment, follow-up, potential recurrence, treatment of metastases and end-of-life care. This simulation model has been used to examine the potential cost-effectiveness of different options for change across the entire colorectal cancer pathway. This paper provides an empirical demonstration of the potential application of modelling entire disease areas to inform clinical policy and resource allocation decision-making.

Suggested Citation

  • H Pilgrim & P Tappenden & J Chilcott & M Bending & P Trueman & A Shorthouse & J Tappenden, 2009. "The costs and benefits of bowel cancer service developments using discrete event simulation," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(10), pages 1305-1314, October.
  • Handle: RePEc:pal:jorsoc:v:60:y:2009:i:10:d:10.1057_jors.2008.109
    DOI: 10.1057/jors.2008.109
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1057/jors.2008.109
    File Function: Abstract
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1057/jors.2008.109?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. M D Stevenson & J E Brazier & N W Calvert & M Lloyd-Jones & J E Oakley & J A Kanis, 2005. "Description of an individual patient methodology for calculating the cost-effectiveness of treatments for osteoporosis in women," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(2), pages 214-221, February.
    2. James C. Felli & Gordon B. Hazen, 1999. "A Bayesian approach to sensitivity analysis," Health Economics, John Wiley & Sons, Ltd., vol. 8(3), pages 263-268, May.
    3. R Ceglowski & L Churilov & J Wasserthiel, 2007. "Combining Data Mining and Discrete Event Simulation for a value-added view of a hospital emergency department," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 58(2), pages 246-254, February.
    4. H Pilgrim & J Chilcott, 2008. "Assessment of a 7-day turn-around for the reporting of cervical smear results using discrete event simulation," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(7), pages 902-910, July.
    5. A Fletcher & D Halsall & S Huxham & D Worthington, 2007. "The DH Accident and Emergency Department model: a national generic model used locally," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 58(12), pages 1554-1562, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Eren Demir & Christos Vasilakis & Reda Lebcir & David Southern, 2015. "A simulation-based decision support tool for informing the management of patients with Parkinson’s disease," International Journal of Production Research, Taylor & Francis Journals, vol. 53(24), pages 7238-7251, December.

    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. Brailsford, Sally & Vissers, Jan, 2011. "OR in healthcare: A European perspective," European Journal of Operational Research, Elsevier, vol. 212(2), pages 223-234, July.
    2. Boyle, Laura M. & Marshall, Adele H. & Mackay, Mark, 2022. "A framework for developing generalisable discrete event simulation models of hospital emergency departments," European Journal of Operational Research, Elsevier, vol. 302(1), pages 337-347.
    3. Yiting Xing & Ling Li & Zhuming Bi & Marzena Wilamowska‐Korsak & Li Zhang, 2013. "Operations Research (OR) in Service Industries: A Comprehensive Review," Systems Research and Behavioral Science, Wiley Blackwell, vol. 30(3), pages 300-353, May.
    4. A. E. Ades & Karl Claxton & Mark Sculpher, 2006. "Evidence synthesis, parameter correlation and probabilistic sensitivity analysis," Health Economics, John Wiley & Sons, Ltd., vol. 15(4), pages 373-381, April.
    5. Nicky Welton & A. E. Ades, 2012. "Research Decisions In The Face Of Heterogeneity: What Can A New Study Tell Us?," Health Economics, John Wiley & Sons, Ltd., vol. 21(10), pages 1196-1200, October.
    6. Eren Demir & David Southern, 2017. "Enabling better management of patients: discrete event simulation combined with the STAR approach," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(5), pages 577-590, May.
    7. Jonathan Karnon, 2003. "Alternative decision modelling techniques for the evaluation of health care technologies: Markov processes versus discrete event simulation," Health Economics, John Wiley & Sons, Ltd., vol. 12(10), pages 837-848, October.
    8. Gouveia, Catarina & Kalakou, Sofia & Cardoso-Grilo, Teresa, 2023. "How to forecast mental healthcare needs? Distinguishing between perceived and unperceived needs and their impact on capacity requirements," Socio-Economic Planning Sciences, Elsevier, vol. 87(PA).
    9. Eric Jutkowitz & Fernando Alarid-Escudero & Hyon K. Choi & Karen M. Kuntz & Hawre Jalal, 2017. "Prioritizing Future Research on Allopurinol and Febuxostat for the Management of Gout: Value of Information Analysis," PharmacoEconomics, Springer, vol. 35(10), pages 1073-1085, October.
    10. Doug Coyle & Jeremy Oakley, 2008. "Estimating the expected value of partial perfect information: a review of methods," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 9(3), pages 251-259, August.
    11. Alan Brennan & Stephen E. Chick & Ruth Davies, 2006. "A taxonomy of model structures for economic evaluation of health technologies," Health Economics, John Wiley & Sons, Ltd., vol. 15(12), pages 1295-1310, December.
    12. M. M. Malik & S. Abdallah & M. Ala’raj, 2018. "Data mining and predictive analytics applications for the delivery of healthcare services: a systematic literature review," Annals of Operations Research, Springer, vol. 270(1), pages 287-312, November.
    13. Emanuele Borgonovo & Alessandra Cillo & Curtis L. Smith, 2018. "On the Relationship between Safety and Decision Significance," Risk Analysis, John Wiley & Sons, vol. 38(8), pages 1541-1558, August.
    14. 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.
    15. Emanuele Borgonovo & Alessandra Cillo, 2017. "Deciding with Thresholds: Importance Measures and Value of Information," Risk Analysis, John Wiley & Sons, vol. 37(10), pages 1828-1848, October.
    16. P R Harper & N H Powell & J E Williams, 2010. "Modelling the size and skill-mix of hospital nursing teams," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(5), pages 768-779, May.
    17. Willoughby, Keith A. & Chan, Benjamin T.B. & Marques, Shauna, 2016. "Using simulation to test ideas for improving speech language pathology services," European Journal of Operational Research, Elsevier, vol. 252(2), pages 657-664.
    18. Carlo Drago & Matteo Ruggeri, 2019. "Setting research priorities in the field of emergency management: which piece of information are you willing to pay more?," Quality & Quantity: International Journal of Methodology, Springer, vol. 53(4), pages 2103-2115, July.
    19. Fumie Yokota & Kimberly M. Thompson, 2004. "Value of Information Literature Analysis: A Review of Applications in Health Risk Management," Medical Decision Making, , vol. 24(3), pages 287-298, June.
    20. Monks, Thomas & Robinson, Stewart & Kotiadis, Kathy, 2014. "Learning from discrete-event simulation: Exploring the high involvement hypothesis," European Journal of Operational Research, Elsevier, vol. 235(1), pages 195-205.

    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:pal:jorsoc:v:60:y:2009:i:10:d:10.1057_jors.2008.109. 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.palgrave-journals.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.