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Anonymization of DICOM Electronic Medical Records for Radiation Therapy

Identifieur interne : 000142 ( Pmc/Curation ); précédent : 000141; suivant : 000143

Anonymization of DICOM Electronic Medical Records for Radiation Therapy

Auteurs : Wayne Newhauser [États-Unis] ; Timothy Jones [États-Unis] ; Stuart Swerdloff [États-Unis] ; Warren Newhauser [États-Unis] ; Mark Cilia [États-Unis] ; Robert Carver [États-Unis] ; Andy Halloran [États-Unis] ; Rui Zhang [États-Unis]

Source :

RBID : PMC:4170049

Abstract

Electronic medical records (EMR) and treatment plans are used in research on patient outcomes and radiation effects. In many situations researchers must remove protected health information (PHI) from EMRs. The literature contains several studies describing the anonymization of generic Digital Imaging and Communication in Medicine (DICOM) files and DICOM image sets but no publications were found that discuss the anonymization of DICOM radiation therapy plans, a key component of an EMR in a cancer clinic. In addition to this we were unable to find a commercial software tool that met the minimum requirements for anonymization and preservation of data integrity for radiation therapy research. The purpose of this study was to develop a prototype software code to meet the requirements for the anonymization of radiation therapy treatment plans and to develop a way to validate that code and demonstrate that it properly anonymized treatment plans and preserved data integrity. We extended an open-source code to process all relevant PHI and to allow for the automatic anonymization of multiple EMRs. The prototype code successfully anonymized multiple treatment plans in less than 1 minute per patient. We also tested commercial optical character recognition (OCR) algorithms for the detection of burned-in text on the images, but they were unable to reliably recognize text. In addition, we developed and tested an image filtering algorithm that allowed us to isolate and redact alpha-numeric text from a test radiograph. Validation tests verified that PHI was anonymized and data integrity, such as the relationship between DICOM unique identifiers (UID) was preserved.


Url:
DOI: 10.1016/j.compbiomed.2014.07.010
PubMed: 25147130
PubMed Central: 4170049

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PMC:4170049

Le document en format XML

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<p id="P1">Electronic medical records (EMR) and treatment plans are used in research on patient outcomes and radiation effects. In many situations researchers must remove protected health information (PHI) from EMRs. The literature contains several studies describing the anonymization of generic Digital Imaging and Communication in Medicine (DICOM) files and DICOM image sets but no publications were found that discuss the anonymization of DICOM radiation therapy plans, a key component of an EMR in a cancer clinic. In addition to this we were unable to find a commercial software tool that met the minimum requirements for anonymization and preservation of data integrity for radiation therapy research. The purpose of this study was to develop a prototype software code to meet the requirements for the anonymization of radiation therapy treatment plans and to develop a way to validate that code and demonstrate that it properly anonymized treatment plans and preserved data integrity. We extended an open-source code to process all relevant PHI and to allow for the automatic anonymization of multiple EMRs. The prototype code successfully anonymized multiple treatment plans in less than 1 minute per patient. We also tested commercial optical character recognition (OCR) algorithms for the detection of burned-in text on the images, but they were unable to reliably recognize text. In addition, we developed and tested an image filtering algorithm that allowed us to isolate and redact alpha-numeric text from a test radiograph. Validation tests verified that PHI was anonymized and data integrity, such as the relationship between DICOM unique identifiers (UID) was preserved.</p>
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<given-names>Wayne</given-names>
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<xref ref-type="aff" rid="A1">a</xref>
<xref ref-type="aff" rid="A2">b</xref>
<xref ref-type="corresp" rid="cor1">*</xref>
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<name>
<surname>Jones</surname>
<given-names>Timothy</given-names>
</name>
<xref ref-type="aff" rid="A1">a</xref>
<xref ref-type="aff" rid="A2">b</xref>
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<name>
<surname>Swerdloff</surname>
<given-names>Stuart</given-names>
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<xref ref-type="aff" rid="A3">c</xref>
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<contrib contrib-type="author">
<name>
<surname>Newhauser</surname>
<given-names>Warren</given-names>
</name>
<xref ref-type="aff" rid="A4">d</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Cilia</surname>
<given-names>Mark</given-names>
</name>
<xref ref-type="aff" rid="A4">d</xref>
<xref ref-type="aff" rid="A5">e</xref>
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<contrib contrib-type="author">
<name>
<surname>Carver</surname>
<given-names>Robert</given-names>
</name>
<xref ref-type="aff" rid="A1">a</xref>
<xref ref-type="aff" rid="A2">b</xref>
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<contrib contrib-type="author">
<name>
<surname>Halloran</surname>
<given-names>Andy</given-names>
</name>
<xref ref-type="aff" rid="A1">a</xref>
<xref ref-type="aff" rid="A2">b</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Zhang</surname>
<given-names>Rui</given-names>
</name>
<xref ref-type="aff" rid="A1">a</xref>
<xref ref-type="aff" rid="A2">b</xref>
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<aff id="A1">
<label>a</label>
Department of Physics and Astronomy, Medical Physics Program, Louisiana State University, 202 Nicholson Hall, Baton Rouge, LA 70803 USA</aff>
<aff id="A2">
<label>b</label>
Department of Medical Physics, Mary Bird Perkins Cancer Center, 4950 Essen Lane, Baton Rouge, LA 70809 USA</aff>
<aff id="A3">
<label>c</label>
ELEKTA Impac Software, 100 South Mathilda Ave, Sunnyvale, California 94086 USA</aff>
<aff id="A4">
<label>d</label>
Mill Creek Systems Inc, 3233 N. Arlington Heights Rd., Arlington Heights, IL 60004 USA</aff>
<aff id="A5">
<label>e</label>
Department of Industrial and Operations Engineering, University of Michigan, 1205 Beal Ave., Ann Arbor, MI 48109 USA</aff>
<author-notes>
<corresp id="cor1">
<label>*</label>
Corresponding author: Department of Physics and Astronomy, Louisiana State University, Baton Rouge, LA, USA
<italic>Tel.</italic>
: 225-578-2762;
<italic>Fax</italic>
: 225-215-1376
<email>newhauser@lsu.edu</email>
</corresp>
</author-notes>
<pub-date pub-type="nihms-submitted">
<day>30</day>
<month>8</month>
<year>2014</year>
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<pub-date pub-type="epub">
<day>26</day>
<month>7</month>
<year>2014</year>
</pub-date>
<pub-date pub-type="ppub">
<day>1</day>
<month>10</month>
<year>2014</year>
</pub-date>
<pub-date pub-type="pmc-release">
<day>01</day>
<month>10</month>
<year>2015</year>
</pub-date>
<volume>0</volume>
<fpage>134</fpage>
<lpage>140</lpage>
<pmc-comment>elocation-id from pubmed: 10.1016/j.compbiomed.2014.07.010</pmc-comment>
<permissions>
<copyright-statement>© 2014 Elsevier Ltd. All rights reserved.</copyright-statement>
<copyright-year>2014</copyright-year>
</permissions>
<abstract>
<p id="P1">Electronic medical records (EMR) and treatment plans are used in research on patient outcomes and radiation effects. In many situations researchers must remove protected health information (PHI) from EMRs. The literature contains several studies describing the anonymization of generic Digital Imaging and Communication in Medicine (DICOM) files and DICOM image sets but no publications were found that discuss the anonymization of DICOM radiation therapy plans, a key component of an EMR in a cancer clinic. In addition to this we were unable to find a commercial software tool that met the minimum requirements for anonymization and preservation of data integrity for radiation therapy research. The purpose of this study was to develop a prototype software code to meet the requirements for the anonymization of radiation therapy treatment plans and to develop a way to validate that code and demonstrate that it properly anonymized treatment plans and preserved data integrity. We extended an open-source code to process all relevant PHI and to allow for the automatic anonymization of multiple EMRs. The prototype code successfully anonymized multiple treatment plans in less than 1 minute per patient. We also tested commercial optical character recognition (OCR) algorithms for the detection of burned-in text on the images, but they were unable to reliably recognize text. In addition, we developed and tested an image filtering algorithm that allowed us to isolate and redact alpha-numeric text from a test radiograph. Validation tests verified that PHI was anonymized and data integrity, such as the relationship between DICOM unique identifiers (UID) was preserved.</p>
</abstract>
<kwd-group>
<kwd>Digital Imaging and Communication in Medicine (DICOM)</kwd>
<kwd>anonymize</kwd>
<kwd>radiation oncology</kwd>
<kwd>protected health information</kwd>
</kwd-group>
</article-meta>
</front>
</pmc>
</record>

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