Adoption and Effectiveness of AI-Based Anomaly Detection for Cross Provider Health Data Exchange

• URL: http://arxiv.org/abs/2604.09630v1
• Date: Thu, 19 Mar 2026 15:22:02 GMT
• Title: Adoption and Effectiveness of AI-Based Anomaly Detection for Cross Provider Health Data Exchange
• Abstract summary: The study proposes a staged deployment strategy combining rules for coverage and machine learning for prioritisation, supported by explainability and continuous monitoring.<n>Results show that rule-based methods achieve high recall but generate higher alert volumes, while Isolation Forest reduces alert burden at the cost of lower sensitivity.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This study investigates the adoption and effectiveness of AI-based anomaly detection in cross-provider electronic health record (EHR) environments. It aims to (1) identify the organisational and digital capabilities required for successful implementation and (2) evaluate the performance and interpretability of lightweight anomaly detection approaches using contextual audit data. A semi-systematic scoping synthesis is conducted to derive a four-pillar readiness framework covering governance, infrastructure/interoperability, workforce, and AI integration, operationalised as a 10-item checklist with measurable indicators. This is complemented by a simulation of cross-provider audit logs incorporating contextual features such as provider mismatch, time of access, days since discharge, session duration, and access frequency. A rule-based approach is benchmarked against Isolation Forest, with SHAP used to explain model behaviour. Results show that rule-based methods achieve high recall but generate higher alert volumes, while Isolation Forest reduces alert burden at the cost of lower sensitivity. SHAP analysis highlights provider mismatch and off-hours access as dominant anomaly drivers. The study proposes a staged deployment strategy combining rules for coverage and machine learning for prioritisation, supported by explainability and continuous monitoring. The findings contribute a practical readiness framework and empirical insights to guide the implementation of AI-based anomaly detection in multi-provider healthcare environments.

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