It’s Not the Model, It’s the Way You Use It: Exploratory Early Health Economics Amid Complexity; Comment on “Problems and Promises of Health Technologies: The Role of Early Health Economic Modelling”

Document Type : Commentary

Authors

1 Flinders Health and Medical Research Institute, Flinders University, Adelaide, SA, Australia

2 Macquarie University Centre for the Health Economy, Macquarie University, Macquarie Park, NSW, Australia

Abstract

In a review recently published in this journal, Grutters et al outline the scope and impact of their early health economic modelling of healthcare innovations. Their reflections shed light on ways that health economists can shift-away from traditional reimbursement decision-support, towards a broader role of facilitating the exploration of existing care pathways, and the design of options to implement or discontinue healthcare services. This is a crucial role in organisations that face constant pressure to react and adapt with changes to their existing service configurations, but where there may exist significant disagreement and uncertainty on the extent to which change is warranted. Such dynamics are known to create complex implementation environments, where changes risk being poorly implemented or fail to be sustained. In this commentary, we extend the discussion by Grutters et al on early health economic modelling, to the evaluation of complex interventions and systems. We highlight how early health economic modelling can contribute to a participatory approach for ongoing learning and development within healthcare organisations.

Keywords

References

  1. Grutters JPC, Govers T, Nijboer J, Tummers M, van der Wilt GJ, Rovers MM. Problems and Promises of Health Technologies: The Role of Early Health Economic Modeling. Int J Health Policy Manag. 2019;8(10):575-582. doi:10.15171/ijhpm.2019.36
  2. Scotland G, Bryan S. Why do health economists promote technology adoption rather than the search for efficiency? A proposal for a change in our approach to economic evaluation in health care. Med Decis Making. 2017;37(2):139-147. doi:10.1177/0272989X16653397
  3. Merlo G, Page K, Ratcliffe J, Halton K, Graves N. Bridging the gap: exploring the barriers to using economic evidence in healthcare decision making and strategies for improving uptake. Appl Health Econ an Health Policy. 2015;13(3):303-309. doi:10.1007/s40258-014-0132-7
  4. Sutton M, Garfield-Birkbeck S, Martin G, et al. Economic analysis of service and delivery interventions in health care. Health Serv Deliv Res. 2018;6(5). doi:10.3310/hsdr06050.
  5. Rapport F, Clay-Williams R, Churruca K, Shih P, Hogden A, Braithwaite J. The struggle of translating science into action: Foundational concepts of implementation science. J Eval Clin Pract. 2018;24(1):117-126. doi:10.1111/jep.12741
  6. Braithwaite J, Churruca K, Long JC, Ellis LA, Herkes J. When complexity science meets implementation science: a theoretical and empirical analysis of systems change. BMC Med. 2018;16(1):63. doi:10.1186/s12916-018-1057-z
  7. Stacey RD. Strategic Management and Organisational Dynamics: The Challenge of Complexity. 3rd ed. Harlow: Prentice Hall; 2000.
  8. Stirling A. Keep it complex. Nature. 2010;468(7327):1029-1031. doi:10.1038/4681029a
  9. Sculpher M, Drummond M, Buxton M. The iterative use of economic evaluation as part of the process of health technology assessment. J Health Serv Res Policy. 1997;2(1):26-30.
  10. Braithwaite J. Changing how we think about healthcare improvement. BMJ. 2018;361:k2014. doi:10.1136/bmj.k2014
  11. National Institute for Health and Care Excellence. Interim Methods Guide for Developing Service Guidance. 2014; www.nice.org.uk/process/pmg8.  Accessed October 23, 2019.
  12. Love-Koh J. How useful are early economic models? Comment on “Problems and promises of health technologies: the role of early health economic modelling.” Int J Health Policy Manag. 2020; In Press. doi:10.15171/ijhpm.2019.119
  13. Wolpert M, Rutter H. Using flawed, uncertain, proximate and sparse (FUPS) data in the context of complexity: learning from the case of child mental health. BMC Med. 2018;16(1):82. doi:10.1186/s12916-018-1079-6
  14. O'Hagan A. Expert Knowledge Elicitation: Subjective but Scientific. Am Stat. 2019;73:69-81. doi:10.1080/00031305.2018.1518265
  15. Afzali HH, Karnon J. Exploring structural uncertainty in model-based economic evaluations. Pharmacoeconomics. 2015;33(5):435-443. doi:10.1007/s40273-015-0256-0
International Journal of Health Policy and Management
Volume 10, Issue 1
January 2021
Pages 36-38

Files

History

  • Receive Date: 29 October 2019
  • Revise Date: 05 January 2020
  • Accept Date: 07 January 2020
  • First Publish Date: 01 January 2021

Share

How to cite

Statistics