The merits of decision modelling in the earliest stages of the IDEAL framework: An innovative case in DIEP flap breast reconstructions

Abstract

The IDEAL framework aims at improving the evidence base of available surgical innovations. However, the development of such innovations and collection of evidence is costly. Surgical innovation can provide more value for money if innovations are evaluated at an early stage, where evaluations can inform the decision whether to stop or to further develop an innovation. We illustrate how decision modelling can be readily adopted at the earliest stages (0-1) of the IDEAL framework, using an innovation in bilateral DIEP flap breast reconstruction as an example. We quantified expected costs and quality-adjusted life years (QALYs) of the current treatment and compared them with an innovation aimed at reducing complications and surgery time. The maximum effect of eliminating all complications (headroom analysis) was explored. Moreover, three scenarios with varying complications and surgery time reductions were modelled. Furthermore, the maximum price of the innovation was estimated in a threshold analysis according to its impact and societal willingness to pay. The headroom analysis showed that when all complications associated with the current treatment are prevented, up to €889 per patient is saved. Scenario analysis showed cost savings between €256 and €828 per patient. When surgery time is reduced by 15min and complications by 50%, the innovation will remain cost-effective at €671 per patient. In a field struggling with cost containment, decision modelling can help to separate promising innovations from costly failures at an early stage. In this example, decision modelling showed that it seems worthwhile to further develop the innovation.