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To address the problem of business process prediction in smart healthcare involving the fusion of multimodal data from interactions among multiple systems, this paper proposes an object-centered explainable prediction method (OCPPP). The approach comprises three modules: 1) Utilizing control flow and data flow constraints between activities for process modelling, Algorithm 1 constructs a Petri net centered on the object to be predicted; 2) Recognizing that business activities share resources during system interactions, individual activities are analysed using AI models, with distinct models applied to different modalities of data in activity logs (e.g., multi-object detection for image data, time-series forecasting for text data); 3) Employing coloured Petri nets and Algorithm 2 for predicting activity durations, integrating outputs from various intelligent models to formulate predictions, including those for low-frequency events. The experimental outcomes indicate that during both individual component analysis and sequential forecasting phases, a versatile selection of AI models enables effective operation. Integrating Petri Nets for system-level predictions enhances explainability through six distinct service compositions. Furthermore, the introduction of precursor transitions and a waiting threshold facilitates the anticipation of infrequent behaviours, thereby augmenting the system’s predictive capabilities for a broader spectrum of occurrences.