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

A research study presents a readiness framework and practical deployment strategy for AI-based anomaly detection in multi-provider healthcare environments. The research combines organizational assessment criteria with machine learning performance evaluation, demonstrating that hybrid rule-based and isolation forest approaches optimize both detection coverage and alert efficiency in cross-provider EHR systems.

This study addresses a critical infrastructure gap in healthcare data security by examining how AI-based anomaly detection can be systematically implemented across multiple provider organizations sharing electronic health records. The research recognizes that technical capability alone is insufficient; successful deployment requires organizational readiness across governance, infrastructure, workforce competency, and AI integration maturity. The four-pillar framework operationalizes abstract readiness concepts into a measurable 10-item checklist, providing healthcare systems with concrete benchmarks for implementation planning.

The empirical evaluation reveals important trade-offs in anomaly detection approaches. Rule-based methods deliver comprehensive coverage with high sensitivity but generate substantial alert volumes that risk analyst fatigue and false-positive overhead. Isolation Forest algorithms reduce this burden by prioritizing anomalies, though at the cost of lower recall rates that could miss genuine security incidents. The use of SHAP explainability analysis identifies provider mismatch and off-hours access patterns as primary anomaly signals, validating domain expertise about high-risk access scenarios.

For healthcare organizations managing shared patient data, this research provides actionable guidance for implementation sequencing. The proposed staged deployment combining rules for broad coverage with machine learning for intelligent triage reflects practical realities of clinical environments where alert fatigue undermines security effectiveness. Continuous monitoring and model retraining mechanisms address the challenge of evolving attack patterns and legitimate usage variations across providers.

The study's contribution extends beyond healthcare; the methodology of combining organizational readiness assessment with explainable AI evaluation offers a replicable model for multi-party data-sharing environments in other regulated sectors including financial services and telecommunications.

• →A four-pillar readiness framework covering governance, infrastructure, workforce, and AI integration enables systematic evaluation of organizational capacity for anomaly detection implementation.
• →Hybrid deployment combining rule-based detection for coverage and machine learning for prioritization optimizes both sensitivity and alert management in cross-provider environments.
• →SHAP explainability analysis identifies provider mismatch and off-hours access as dominant anomaly signals, validating domain-driven feature selection over purely statistical approaches.
• →Alert volume reduction through machine learning prioritization addresses analyst fatigue and operational sustainability concerns in continuous monitoring environments.
• →Staged deployment strategies with continuous monitoring and model retraining mechanisms enable gradual scaling while maintaining security effectiveness across multiple provider organizations.