Title |
Evaluating current automatic de-identification methods with Veteran’s health administration clinical documents
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Published in |
BMC Medical Research Methodology, July 2012
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DOI | 10.1186/1471-2288-12-109 |
Pubmed ID | |
Authors |
Oscar Ferrández, Brett R South, Shuying Shen, F Jeffrey Friedlin, Matthew H Samore, Stéphane M Meystre |
Abstract |
The increased use and adoption of Electronic Health Records (EHR) causes a tremendous growth in digital information useful for clinicians, researchers and many other operational purposes. However, this information is rich in Protected Health Information (PHI), which severely restricts its access and possible uses. A number of investigators have developed methods for automatically de-identifying EHR documents by removing PHI, as specified in the Health Insurance Portability and Accountability Act "Safe Harbor" method.This study focuses on the evaluation of existing automated text de-identification methods and tools, as applied to Veterans Health Administration (VHA) clinical documents, to assess which methods perform better with each category of PHI found in our clinical notes; and when new methods are needed to improve performance. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 1 | 100% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Practitioners (doctors, other healthcare professionals) | 1 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United Kingdom | 2 | 2% |
United States | 1 | 1% |
Germany | 1 | 1% |
Canada | 1 | 1% |
Unknown | 86 | 95% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 21 | 23% |
Student > Ph. D. Student | 15 | 16% |
Student > Master | 13 | 14% |
Other | 8 | 9% |
Professor > Associate Professor | 5 | 5% |
Other | 11 | 12% |
Unknown | 18 | 20% |
Readers by discipline | Count | As % |
---|---|---|
Medicine and Dentistry | 28 | 31% |
Computer Science | 21 | 23% |
Social Sciences | 4 | 4% |
Engineering | 4 | 4% |
Linguistics | 3 | 3% |
Other | 9 | 10% |
Unknown | 22 | 24% |