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Detecting Inappropriate Access to Electronic Health Records Using Collaborative Filtering.

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This study introduces a new machine learning approach to detect inappropriate access to electronic health records (EHRs). The method improves security by analyzing user and patient historical data for better prediction of policy violations.

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Area of Science:

  • Computer Science
  • Health Informatics
  • Machine Learning

Background:

  • Healthcare facilities use post-access audits for electronic health record (EHR) security.
  • Current auditing is inefficient, retrospective, and fails to leverage user/patient history.
  • Existing machine learning models overlook individual access patterns.

Purpose of the Study:

  • To develop an automated system for predicting inappropriate EHR access.
  • To improve upon existing supervised learning methods by incorporating user and patient identity.
  • To enhance EHR security through more effective, proactive detection of policy violations.

Main Methods:

  • Proposed a collaborative filtering-inspired machine learning approach.
  • Integrated explicit and latent features for staff and patients.
  • Utilized historical access patterns to create personalized user/patient 'fingerprints'.

Main Results:

  • Achieved significantly improved performance over existing methods on real-world EHR and file-access datasets.
  • Demonstrated the effectiveness of incorporating user and patient identity features.
  • Identified key indicators of inappropriate access behavior.

Conclusions:

  • The proposed method offers a more effective and proactive approach to EHR security.
  • Integrating user-specific historical data enhances the accuracy of detecting inappropriate access.
  • This approach can provide valuable insights for healthcare security policies.