Forecasting Digital-Health Technology Adoption Curves to Inform Strategic Investment and Innovation Portfolio Management

Abstract

This paper presents a novel probabilistic framework for forecasting adoption trajectories of emerging digital health technologies that integrates multi-dimensional signal analysis with dynamic Bayesian network modeling. The methodological approach combines temporal data streams across five distinct domains: clinical validation signals, regulatory progression indicators, reimbursement pathway evolution, competitive landscape dynamics, and consumer/provider sentiment indices. By applying recurrent neural networks with attention mechanisms to historical adoption patterns of 218 digital health innovations launched between 2010-2024, our framework demonstrates superior predictive accuracy compared to traditional bass diffusion and Gompertz models. Validation against held-out test cases reveals a mean absolute percentage error reduction of 37.8\% for five-year adoption forecasts. Additionally, we introduce a quantitative methodology for identifying strategic inflection points along adoption curves, enabling more targeted investment timing decisions. The framework's sensitivity analysis reveals differential weighting of signal importance across seven digital health market segments, with regulatory signals demonstrating highest predictive value for therapeutic devices but diminished importance for consumer wellness applications. This analytical engine provides healthcare technology investors, innovators, and strategic planners with empirically-grounded, probabilistic forecasting capabilities to inform portfolio optimization decisions under uncertainty.