The market access of innovative technology is perceptibly tensing up. Beyond cost-effectiveness, payers increasingly scrutinize budget impact (BI) and contemplate risk-sharing (RS) agreements. In these conditions, accurate forecasting of an innovation uptake and diffusion (IUD) is decisive as it determines the new technology mix evolution over time. The IUD rate is a key feature of BI modelling and cannot strategically be ignored in potential RS proposals in which the number of patients being given the innovation over time may determine RS agreement conditions. However, in the BI analysis and RS experiences carried out so far, it is not necessarily clear how IUD rates were factored in. This contribution builds upon methods developed in the technological forecasting science to predict new technology diffusion with little or no data. We describe the use of sigmoid growth curves (Logistic or Gompertz functions for instance) to account for the gradual and continuous process of an IUD within a specific market. Growth curves are mathematically tractable. They provide modellers and decision makers with remarkable flexibility and transparency to run a broad range of adaptations, sensitivity and scenario analysis on an IUD path in BI models or RS proposals i.e., in simulating and comparing different market dynamics and new technology mix evolution. Bayesian updating is also described to improve curve fitting in order to increase the accuracy of IUD forecasts as expert opinions and preliminary claims or sales data become available.