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Title page for ETD etd-03262018-133815

Type of Document Master's Thesis
Author Hedda, Monica Satyanarayan
URN etd-03262018-133815
Title Flagging and Ranking Suspicious Accesses in Electronic Health Record Systems.
Degree Master of Science
Department Computer Science
Advisory Committee
Advisor Name Title
Daniel Fabbri, PhD Committee Chair
Bradley A. Malin, PhD Committee Member
  • Electronic Health Records Auditing
Date of Defense 2018-03-13
Availability unrestricted
Hospitals are facing steep challenges to protect privacy of patient data in Electronic Health Records (EHR) from insider threats. To achieve fast detection of insider misuse and reduce further harm, large hospitals need automated suspicious access detection mechanisms. Currently, the use of rule-based auditing systems is prevalent across several healthcare organizations. However, rule-based auditing systems have not been evaluated empirically. Hence in this work, we first propose a principled approach to evaluate the effectiveness of rule-based methods in identifying suspicious behavior. Furthermore, rule-based auditing systems rely on predefined rules and are oblivious to the statistical properties of the EHR data. To this end, we propose an auditing method based on supervised machine learning techniques which utilizes clinical context to identify suspicious behavior. Experiment results show the effectiveness of our approach to identify suspicious behavior in EHR systems.
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