Estimating future health technology diffusion using expert beliefs calibrated to an established diffusion model

Objectives: Estimates of future health technology diffusion, or future uptake over time, are a requirement for different analyses performed within health technology assessments. Methods for obtaining such estimates include constant uptake estimates based on expert opinion or analogous technologies, and extrapolation from initial data points using parametric curves – but remain divorced from established diffusion theory and modelling. We propose an approach to obtaining diffusion estimates using experts’ beliefs calibrated to an established diffusion model to address this methodological gap.

Methods: We performed an elicitation of experts’ beliefs on future diffusion of a new preterm birth screening illustrative case study technology. The elicited quantities were chosen such that they could be calibrated to yield the parameters of the Bass model of new product growth, which was chosen based on a review of the diffusion literature.

Results: With the elicitation of only three quantities per diffusion curve, our approach enabled us to quantify uncertainty about diffusion of the new technology in different scenarios. Pooled results showed that the attainable number of adoptions was predicted to be relatively low compared with what was thought possible. Further research evidence improved the attainable number of adoptions only slightly but resulted in greater speed of diffusion.

Conclusions: The proposed approach of eliciting experts’ beliefs about diffusion and informing the Bass model has the potential to fill the methodological gap evident in value of implementation and research, as well as budget impact and some cost-effectiveness analyses.