Designing and Undertaking a Health Economics Study of Digital Health Interventions

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• 作者：Paul McNamee ; PhD¹ ; ^{p.mcnamee@abdn.ac.uk" class="auth_mail" title="E-mail the corresponding author} ; Elizabeth Murray ; PhD² ; Michael P. Kelly ; PhD³ ; Laura Bojke ; PhD⁴ ; Jim Chilcott ; MSc⁵ ; Alastair Fischer ; PhD⁶ ; Robert West ; PhD⁷ ; Lucy Yardley ; PhD⁸
• 刊名：American Journal of Preventive Medicine
• 出版年：2016
• 出版时间：November 2016
• 年：2016
• 卷：51
• 期：5
• 页码：852-860
• 全文大小：368 K

文摘

This paper introduces and discusses key issues in the economic evaluation of digital health interventions. The purpose is to stimulate debate so that existing economic techniques may be refined or new methods developed. The paper does not seek to provide definitive guidance on appropriate methods of economic analysis for digital health interventions.

This paper describes existing guides and analytic frameworks that have been suggested for the economic evaluation of healthcare interventions. Using selected examples of digital health interventions, it assesses how well existing guides and frameworks align to digital health interventions. It shows that digital health interventions may be best characterized as complex interventions in complex systems. Key features of complexity relate to intervention complexity, outcome complexity, and causal pathway complexity, with much of this driven by iterative intervention development over time and uncertainty regarding likely reach of the interventions among the relevant population. These characteristics imply that more-complex methods of economic evaluation are likely to be better able to capture fully the impact of the intervention on costs and benefits over the appropriate time horizon. This complexity includes wider measurement of costs and benefits, and a modeling framework that is able to capture dynamic interactions among the intervention, the population of interest, and the environment. The authors recommend that future research should develop and apply more-flexible modeling techniques to allow better prediction of the interdependency between interventions and important environmental influences.