  In the era of big data, there is an increasing need for healthcare providers,
communities, and researchers to share data and collaborate to improve health
outcomes, generate valuable insights, and advance research. The Health
Insurance Portability and Accountability Act of 1996 (HIPAA) is a federal law
designed to protect sensitive health information by defining regulations for
protected health information (PHI). However, it does not provide efficient
tools for detecting or removing PHI before data sharing. One of the challenges
in this area of research is the heterogeneous nature of PHI fields in data
across different parties. This variability makes rule-based sensitive variable
identification systems that work on one database fail on another. To address
this issue, our paper explores the use of machine learning algorithms to
identify sensitive variables in structured data, thus facilitating the
de-identification process. We made a key observation that the distributions of
metadata of PHI fields and non-PHI fields are very different. Based on this
novel finding, we engineered over 30 features from the metadata of the original
features and used machine learning to build classification models to
automatically identify PHI fields in structured Electronic Health Record (EHR)
data. We trained the model on a variety of large EHR databases from different
data sources and found that our algorithm achieves 99% accuracy when detecting
PHI-related fields for unseen datasets. The implications of our study are
significant and can benefit industries that handle sensitive data.
