IDEAS home Printed from https://ideas.repec.org/a/kap/hcarem/v25y2022i3d10.1007_s10729-021-09574-0.html
   My bibliography  Save this article

On the timing and probability of Presurgical Teledermatology: how it becomes the dominant strategy

Author

Listed:
  • Felipa de Mello-Sampayo

    (ISCTE -Instituto Universitário de Lisboa; BRU_ISCTE Business Research Unit)

Abstract

Health level fluctuations make the outcome of any treatment option uncertain, so that decision-makers might have to wait for more information before optimally choosing treatments, especially when time spent in treatment cannot be fully recovered later in terms of health outcome. To examine whether or not, and when decision-makers should use presurgical teledermatology, a dynamic stochastic model is applied to patients waiting for dermatology surgical intervention. The theoretical model suggests that health uncertainty discourages using teledermatology. As teledermatology becomes less cost competitive, the uncertainty becomes more dominant. The results of the model were then tested empirically with the teledermatology network covering the area served by one Portuguese regional hospital, which links the primary care centers of 24 health districts with the hospital’s dermatology department via the corporate intranet of the Portuguese healthcare system. Under uncertainty and irreversibility, presurgical teledermatology becomes the dominant strategy for younger patients and with lower probability of developing skin cancer.

Suggested Citation

  • Felipa de Mello-Sampayo, 2022. "On the timing and probability of Presurgical Teledermatology: how it becomes the dominant strategy," Health Care Management Science, Springer, vol. 25(3), pages 389-405, September.
  • Handle: RePEc:kap:hcarem:v:25:y:2022:i:3:d:10.1007_s10729-021-09574-0
    DOI: 10.1007/s10729-021-09574-0
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10729-021-09574-0
    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/s10729-021-09574-0?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. Anika Reichert & Rowena Jacobs, 2018. "The impact of waiting time on patient outcomes: Evidence from early intervention in psychosis services in England," Health Economics, John Wiley & Sons, Ltd., vol. 27(11), pages 1772-1787, November.
    2. Claxton, Karl, 1999. "The irrelevance of inference: a decision-making approach to the stochastic evaluation of health care technologies," Journal of Health Economics, Elsevier, vol. 18(3), pages 341-364, June.
    3. Karl Claxton & Simon Eggington & Laura Ginnelly & Susan Griffin & Christopher McCabe & Zoe Philips & Paul Tappenden & Alan Wailoo, 2005. "A Pilot Study of Value of Information Analysis to Support Research Recommendations for the National Institute for Health and Clinical Excellence," Working Papers 004cherp, Centre for Health Economics, University of York.
    4. Meyer, Elisabeth & Rees, Ray, 2012. "Watchfully waiting: Medical intervention as an optimal investment decision," Journal of Health Economics, Elsevier, vol. 31(2), pages 349-358.
    5. Jatrana, Santosh & Crampton, Peter, 2009. "Primary health care in New Zealand: Who has access?," Health Policy, Elsevier, vol. 93(1), pages 1-10, November.
    6. Silviya Nikolova & Mark Harrison & Matt Sutton, 2016. "The Impact of Waiting Time on Health Gains from Surgery: Evidence from a National Patient‐reported Outcome Dataset," Health Economics, John Wiley & Sons, Ltd., vol. 25(8), pages 955-968, August.
    7. Richard D. Smith, 2007. "Use, option and externality values: are contingent valuation studies in health care mis‐specified?," Health Economics, John Wiley & Sons, Ltd., vol. 16(8), pages 861-869, August.
    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. de Mello-Sampayo, F.;, 2024. "Uncertainty in Healthcare Policy Decisions: An Epidemiological Real Options Approach to COVID-19 Lockdown Exits," Health, Econometrics and Data Group (HEDG) Working Papers 24/01, HEDG, c/o Department of Economics, University of York.

    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. Felipa, de Mello-Sampayo, 2014. "The Timing and Probability of Switching to Second-line Regimen - An application to Second-Line Antiretroviral Therapy in India," MPRA Paper 60997, University Library of Munich, Germany.
    2. de Mello-Sampayo, F.;, 2024. "Uncertainty in Healthcare Policy Decisions: An Epidemiological Real Options Approach to COVID-19 Lockdown Exits," Health, Econometrics and Data Group (HEDG) Working Papers 24/01, HEDG, c/o Department of Economics, University of York.
    3. Williams, Jenny & Bretteville-Jensen, Anne Line, 2022. "What's Another Day? The Effects of Wait Time for Substance Abuse Treatment on Health-Care Utilization, Employment and Crime," IZA Discussion Papers 15083, Institute of Labor Economics (IZA).
    4. Elisabeth Fenwick & Karl Claxton & Mark Sculpher, 2005. "The value of implementation and the value of information: combined and uneven development," Working Papers 005cherp, Centre for Health Economics, University of York.
    5. Zoe Philips & Karl Claxton & Stephen Palmer, 2008. "The Half-Life of Truth: What Are Appropriate Time Horizons for Research Decisions?," Medical Decision Making, , vol. 28(3), pages 287-299, May.
    6. Anika Reichert & Rowena Jacobs, 2018. "The impact of waiting time on patient outcomes: Evidence from early intervention in psychosis services in England," Health Economics, John Wiley & Sons, Ltd., vol. 27(11), pages 1772-1787, November.
    7. Alan Brennan & Samer Kharroubi & Anthony O'Hagan & Jim Chilcott, 2007. "Calculating Partial Expected Value of Perfect Information via Monte Carlo Sampling Algorithms," Medical Decision Making, , vol. 27(4), pages 448-470, July.
    8. Eldon Spackman & Stewart Richmond & Mark Sculpher & Martin Bland & Stephen Brealey & Rhian Gabe & Ann Hopton & Ada Keding & Harriet Lansdown & Sara Perren & David Torgerson & Ian Watt & Hugh MacPherso, 2014. "Cost-Effectiveness Analysis of Acupuncture, Counselling and Usual Care in Treating Patients with Depression: The Results of the ACUDep Trial," PLOS ONE, Public Library of Science, vol. 9(11), pages 1-12, November.
    9. Michael G Baker & Jason Gurney & Jane Oliver & Nicole J Moreland & Deborah A Williamson & Nevil Pierse & Nigel Wilson & Tony R Merriman & Teuila Percival & Colleen Murray & Catherine Jackson & Richard, 2019. "Risk Factors for Acute Rheumatic Fever: Literature Review and Protocol for a Case-Control Study in New Zealand," IJERPH, MDPI, vol. 16(22), pages 1-39, November.
    10. Qi Cao & Erik Buskens & Hans L. Hillege & Tiny Jaarsma & Maarten Postma & Douwe Postmus, 2019. "Stratified treatment recommendation or one-size-fits-all? A health economic insight based on graphical exploration," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 20(3), pages 475-482, April.
    11. Thomas Reinhold & Claudia Witt & Susanne Jena & Benno Brinkhaus & Stefan Willich, 2008. "Quality of life and cost-effectiveness of acupuncture treatment in patients with osteoarthritis pain," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 9(3), pages 209-219, August.
    12. Isaac Corro Ramos & Maureen P. M. H. Rutten-van Mölken & Maiwenn J. Al, 2013. "The Role of Value-of-Information Analysis in a Health Care Research Priority Setting," Medical Decision Making, , vol. 33(4), pages 472-489, May.
    13. Maiwenn Al, 2013. "Cost-Effectiveness Acceptability Curves Revisited," PharmacoEconomics, Springer, vol. 31(2), pages 93-100, February.
    14. Frank G. Sandmann & Julie V. Robotham & Sarah R. Deeny & W. John Edmunds & Mark Jit, 2018. "Estimating the opportunity costs of bed‐days," Health Economics, John Wiley & Sons, Ltd., vol. 27(3), pages 592-605, March.
    15. Katharina Fischer & Reiner Leidl, 2014. "Analysing coverage decision-making: opening Pandora’s box?," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 15(9), pages 899-906, December.
    16. Mark Sculpher & Amiram Gafni, 2001. "Recognizing diversity in public preferences: The use of preference sub‐groups in cost‐effectiveness analysis," Health Economics, John Wiley & Sons, Ltd., vol. 10(4), pages 317-324, June.
    17. Cristina Borra & Jerònia Pons-Pons & Margarita Vilar-Rodríguez, 2020. "Austerity, healthcare provision, and health outcomes in Spain," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 21(3), pages 409-423, April.
    18. Charles F. Manski, 2018. "Reasonable patient care under uncertainty," Health Economics, John Wiley & Sons, Ltd., vol. 27(10), pages 1397-1421, October.
    19. McKenna, Claire & Chalabi, Zaid & Epstein, David & Claxton, Karl, 2010. "Budgetary policies and available actions: A generalisation of decision rules for allocation and research decisions," Journal of Health Economics, Elsevier, vol. 29(1), pages 170-181, January.
    20. Niklas Zethraeus & Magnus Johannesson & Bengt Jönsson & Mickael Löthgren & Magnus Tambour, 2003. "Advantages of Using the Net-Benefit Approach for Analysing Uncertainty in Economic Evaluation Studies," PharmacoEconomics, Springer, vol. 21(1), pages 39-48, January.

    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:3:d:10.1007_s10729-021-09574-0. 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.