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Evaluation of PHI Hunter in Natural Language Processing Research.
Andrew Redd1, Steve Pickard2, Stephane Meystre3
1Andrew Redd, PhD, is an Assistant Professor at the University of Utah and a statistician in the IDEAS Center at the VA Salt Lake City Health Care System in Salt Lake City, UT.
Perspectives in Health Information Management
|January 26, 2016
Summary
A new tool, PHI Hunter, uses SAS to remove protected health information (PHI) from text documents, enhancing patient privacy in research. It effectively removes identification numbers but needs improvement for names and locations.
Area of Science:
- Health Informatics
- Natural Language Processing (NLP)
- Data Privacy
Background:
- Increasing use of text documents in research necessitates robust methods for protecting patient privacy.
- Compliance with human subjects' right to privacy requires removal of unnecessary protected health information (PHI).
- Existing de-identification processes are not identical to the specific needs of removing PHI for research data.
Purpose of the Study:
- To introduce and evaluate PHI Hunter, an accessible SAS-based tool for removing specific PHI from text documents.
- To assess the tool's effectiveness in enhancing patient privacy for research purposes.
- To identify areas for improvement in PHI removal from research data.
Main Methods:
- PHI Hunter was developed as a set of rules within SAS to identify and remove patterns of PHI from free-form text.
- The tool was applied to a corpus of 473 unstructured text documents from the Department of Veterans Affairs (VA).
- Performance was evaluated based on the accuracy of PHI removal, including identification numbers, names, and locations.
Main Results:
- PHI Hunter demonstrated strong performance in removing identification numbers like Social Security numbers, phone numbers, and medical record numbers.
- The tool exhibited limitations, with names and locations being the most frequently missed PHI items.
- Instances of incorrect information removal occurred when text patterns mimicked identification numbers.
Conclusions:
- PHI Hunter serves a distinct role in enhancing patient privacy for research, complementing but not replacing de-identification tools.
- The tool is effective for highly sensitive PHI categories, though improvements are needed for name and location data.
- Tailoring PHI Hunter with linked demographic tables from electronic health records (EHRs) can significantly improve its precision and performance across different datasets.
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