Detecting Inappropriate Access to Electronic Health Records Using Collaborative Filtering.

ABSTRACT: Many healthcare facilities enforce security on their electronic health records (EHRs) through a corrective mechanism: some staff nominally have almost unrestricted access to the records, but there is a strict ex post facto audit process for inappropriate accesses, i.e., accesses that violate the facility's security and privacy policies. This process is inefficient, as each suspicious access has to be reviewed by a security expert, and is purely retrospective, as it occurs after damage may have been incurred. This motivates automated approaches based on machine learning using historical data. Previous attempts at such a system have successfully applied supervised learning models to this end, such as SVMs and logistic regression. While providing benefits over manual auditing, these ap

SUBMITTER: Menon AK 

PROVIDER: S-EPMC3967851 | biostudies-literature | 2014 Apr

REPOSITORIES: biostudies-literature