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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.

In business process management, business process change analysis is the key link to ensure the flexibility and adaptability of the system. The existing methods mostly focus on the change analysis of a single business process from the perspective of control flow, ignoring the influence of data changes on collaborative processes with information interaction. In order to compensate for this deficiency, this paper proposes a rule-driven consistency analysis method for data changes in collaborative processes. Firstly, it analyzes the influence of data changes on other elements (such as activities, data, roles, and guards) in collaborative processes, and gives the definition of data influence. Secondly, the optimal alignment technology is used to explore how data changes interfere with the expected behavior of deviation activities, and decision rules are integrated into the Petri net model to accurately evaluate and screen out the effective expected behavior that conforms to business logic and established rules. Finally, the initial optimal alignment is repaired according to the screened effective expected behavior, and the consistency of business processes is recalculated. The experimental results show that the introduced rule constraint mechanism can effectively avoid the misjudgment of abnormal behavior. Compared with the traditional method, the average accuracy, recall rate, and F1-score of effective expected behavior are improved by 4%, 4.7%, and 4.3%, respectively. In addition, the repaired optimal alignment significantly enhances the system's ability to respond quickly and self-adjust to data changes, providing a strong support for the intelligent and automated transformation of business process management. [ABSTRACT FROM AUTHOR]