Multi-view deviation detection under privacy for medical business processes: Enhancing patient-centered care via digital transformation

Deviation detection has emerged as a critical research focus for business processes, enabling enterprises to prevent fraud, monitor anomalies, and safeguard the security of processes and data, particularly in the medical field. Despite its importance, existing methods face significant limitations. Some approaches focus solely on control flow deviations while neglecting data-induced deviations, whereas others rely on specific data, risking the exposure of personal privacy information. Consequently, a major challenge lies in balancing data availability for deviation detection with the imperative of preserving data privacy and security. To address this challenge, this paper proposes a multi-view deviation detection method based on privacy protection. First, data attributes critical to business processes are extracted using a random field model. Next, an identity and purpose-based data matching algorithm ensures the security of user identities and validates the intended use of data for privacy protection. Furthermore, the business process activity view regulates legally permissible data operations, while decision logic analysis links processes and data through decision tables to detect deviations. Beyond detecting deviations within each perspective, this method uncovers hidden deviations arising from the interplay of business process, data flow, and privacy perspectives. The evaluation using real-world medical event data demonstrates the method's effectiveness. Notably, it outperforms existing approaches by accurately identifying deviations that other methods fail to detect.