Multimodal intelligent hyperautomation for sustainable healthcare supply chains: a blockchain-IoMT-AI integration perspective

Healthcare supply chains generate heterogeneous multimodal data – medical images, biosensor readings, clinical text, and transactional records – that are currently processed in isolation, resulting in fragmented decision-making and suboptimal sustainability outcomes. Key bottlenecks include: (a) data integrity risks from centralised storage systems, (b) lack of real-time cross-modal visibility across supply chain stages, (c) consensus inefficiency in distributed ledger systems leading to high computational overhead, and (d) absence of integrated AI reasoning across data modalities. A multimodal approach is necessary because single-modal systems cannot capture the complex interdependencies among these data streams. This study develops a Multimodal Intelligent Hyperautomation framework by integrating Blockchain Technology (BCT), the Internet of Medical Things (IoMT), and Artificial Intelligence (AI). The framework supports cross-modal data fusion and facilitates adaptive decision-making across healthcare supply chain processes. Data were collected from 316 respondents in 210 Chinese hospitals through a structured survey. (H1) BCT utilisation positively impacts IoMT utilisation; (H2) BCT utilisation positively impacts AI utilisation; (H3) IoMT utilisation positively impacts AI utilisation; (H4) IoMT utilisation positively impacts healthcare supply chain sustainability; and (H5) AI utilisation positively impacts healthcare supply chain sustainability. Analysis via Structural Equation Modelling with data from 316 respondents across 210 Chinese hospitals confirmed all five hypotheses (all p < 0.001). Drawing on these empirical results, researchers designed a multimodal fusion layer to integrate visual, textual, and sensor data streams within a blockchain-secured hyperautomation pipeline. Researchers also introduce an Improved Byzantine Fault Tolerance (IMBFT) consensus mechanism that achieves higher throughput and greater fault tolerance than conventional methods. In addition, the proposed cross-modal architecture promotes explainable and human-centric automated processes. This study extends current understanding of multimodal AI applications in intelligent hyperautomation and offers actionable technical designs to enhance sustainability in healthcare supply chains. Unlike prior studies that examine individual technologies in isolation, this study contributes the first integrated multimodal hyperautomation framework for healthcare supply chains, yielding measurable benefits: 20–25% reduction in hospital drug inventory, 23% higher consensus throughput via the IMBFT algorithm, and enhanced cross-modal predictive accuracy (R2 = 0.946) for demand forecasting compared to SVM, BP, ARIMA, and hybrid baseline models.