Using statistical and machine learning to help institutions detect suspicious access to electronic health records.

ABSTRACT:

Objective

To determine whether statistical and machine-learning methods, when applied to electronic health record (EHR) access data, could help identify suspicious (ie, potentially inappropriate) access to EHRs.

Methods

From EHR access logs and other organizational data collected over a 2-month period, the authors extracted 26 features likely to be useful in detecting suspicious accesses. Selected events were marked as either suspicious or appropriate by privacy officers, and served as the gold standard set for model evaluation. The authors trained logistic regression (LR) and support vector machine (SVM) models on 10-fold cross-validation sets of 1291 labeled events. The authors evaluated the sensitivity of final models on an external set of 58 events that were identified a

SUBMITTER: Boxwala AA 

PROVIDER: S-EPMC3128412 | biostudies-literature | 2011 Jul-Aug

REPOSITORIES: biostudies-literature