Development of an optical character recognition pipeline for handwritten form fields from an electronic health record
Identifieur interne : 000114 ( Ncbi/Merge ); précédent : 000113; suivant : 000115Development of an optical character recognition pipeline for handwritten form fields from an electronic health record
Auteurs : Luke V. Rasmussen [États-Unis] ; Peggy L. Peissig [États-Unis] ; Catherine A. Mccarty [États-Unis] ; Justin Starren [États-Unis]Source :
- Journal of the American Medical Informatics Association : JAMIA [ 1067-5027 ] ; 2011.
Abstract
Although the penetration of electronic health records is increasing rapidly, much of the historical medical record is only available in handwritten notes and forms, which require labor-intensive, human chart abstraction for some clinical research. The few previous studies on automated extraction of data from these handwritten notes have focused on monolithic, custom-developed recognition systems or third-party systems that require proprietary forms.
We present an optical character recognition processing pipeline, which leverages the capabilities of existing third-party optical character recognition engines, and provides the flexibility offered by a modular custom-developed system. The system was configured and run on a selected set of form fields extracted from a corpus of handwritten ophthalmology forms.
The processing pipeline allowed multiple configurations to be run, with the optimal configuration consisting of the Nuance and LEADTOOLS engines running in parallel with a positive predictive value of 94.6% and a sensitivity of 13.5%.
While limitations exist, preliminary experience from this project yielded insights on the generalizability and applicability of integrating multiple, inexpensive general-purpose third-party optical character recognition engines in a modular pipeline.
Url:
DOI: 10.1136/amiajnl-2011-000182
PubMed: 21890871
PubMed Central: 3392858
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<front><div type="abstract" xml:lang="en"><sec><title>Background</title>
<p>Although the penetration of electronic health records is increasing rapidly, much of the historical medical record is only available in handwritten notes and forms, which require labor-intensive, human chart abstraction for some clinical research. The few previous studies on automated extraction of data from these handwritten notes have focused on monolithic, custom-developed recognition systems or third-party systems that require proprietary forms.</p>
</sec>
<sec><title>Methods</title>
<p>We present an optical character recognition processing pipeline, which leverages the capabilities of existing third-party optical character recognition engines, and provides the flexibility offered by a modular custom-developed system. The system was configured and run on a selected set of form fields extracted from a corpus of handwritten ophthalmology forms.</p>
</sec>
<sec><title>Observations</title>
<p>The processing pipeline allowed multiple configurations to be run, with the optimal configuration consisting of the Nuance and LEADTOOLS engines running in parallel with a positive predictive value of 94.6% and a sensitivity of 13.5%.</p>
</sec>
<sec><title>Discussion</title>
<p>While limitations exist, preliminary experience from this project yielded insights on the generalizability and applicability of integrating multiple, inexpensive general-purpose third-party optical character recognition engines in a modular pipeline.</p>
</sec>
</div>
</front>
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<pmc article-type="research-article"><pmc-comment>The publisher of this article does not allow downloading of the full text in XML form.</pmc-comment>
<front><journal-meta><journal-id journal-id-type="nlm-ta">J Am Med Inform Assoc</journal-id>
<journal-id journal-id-type="iso-abbrev">J Am Med Inform Assoc</journal-id>
<journal-id journal-id-type="publisher-id">jamia</journal-id>
<journal-id journal-id-type="hwp">amiajnl</journal-id>
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<issn pub-type="epub">1527-974X</issn>
<publisher><publisher-name>BMJ Group</publisher-name>
<publisher-loc>BMA House, Tavistock Square, London, WC1H 9JR</publisher-loc>
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<article-meta><article-id pub-id-type="pmid">21890871</article-id>
<article-id pub-id-type="pmc">3392858</article-id>
<article-id pub-id-type="publisher-id">amiajnl-2011-000182</article-id>
<article-id pub-id-type="doi">10.1136/amiajnl-2011-000182</article-id>
<article-categories><subj-group subj-group-type="heading"><subject>Research and Applications</subject>
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<series-title>FOCUS on clinical research informatics</series-title>
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<title-group><article-title>Development of an optical character recognition pipeline for handwritten form fields from an electronic health record</article-title>
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<contrib-group><contrib contrib-type="author" corresp="yes"><name><surname>Rasmussen</surname>
<given-names>Luke V</given-names>
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<xref ref-type="aff" rid="aff1">1</xref>
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<contrib contrib-type="author"><name><surname>Peissig</surname>
<given-names>Peggy L</given-names>
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<xref ref-type="aff" rid="aff1">1</xref>
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<contrib contrib-type="author"><name><surname>McCarty</surname>
<given-names>Catherine A</given-names>
</name>
<xref ref-type="aff" rid="aff2">2</xref>
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<contrib contrib-type="author"><name><surname>Starren</surname>
<given-names>Justin</given-names>
</name>
<xref ref-type="aff" rid="aff1">1</xref>
<xref ref-type="aff" rid="aff3">3</xref>
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<aff id="aff1"><label>1</label>
Biomedical Informatics Research Center, Marshfield Clinic Research Foundation, Marshfield, Wisconsin, USA</aff>
<aff id="aff2"><label>2</label>
Essentia Institute of Rural Health, Duluth, Minnesota, USA</aff>
<aff id="aff3"><label>3</label>
Division of Health and Biomedical Informatics, Department of Preventive Medicine, Northwestern University, Chicago, Illinois, USA</aff>
<author-notes><corresp><bold>Correspondence to</bold>
Luke V Rasmussen, Marshfield Clinic Research Foundation, 1000 North Oak Avenue, BIRC/ML8, Marshfield, WI 54449, USA; <email>rasmussen.luke@mcrf.mfldclin.edu</email>
</corresp>
<fn fn-type="other"><p>Preliminary results from this work were presented as an abstract at the 2009 AMIA Summit on Translational Bioinformatics</p>
</fn>
</author-notes>
<pub-date pub-type="epub"><day>2</day>
<month>9</month>
<year>2011</year>
</pub-date>
<pub-date pub-type="ppub"><month>6</month>
<year>2012</year>
</pub-date>
<pub-date pub-type="pmc-release"><day>1</day>
<month>6</month>
<year>2013</year>
</pub-date>
<pmc-comment> PMC Release delay is 12 months and 0 days and was based on the
. </pmc-comment>
<volume>19</volume>
<issue>e1</issue>
<fpage>e90</fpage>
<lpage>e95</lpage>
<history><date date-type="received"><day>13</day>
<month>2</month>
<year>2011</year>
</date>
<date date-type="accepted"><day>16</day>
<month>6</month>
<year>2011</year>
</date>
</history>
<permissions><copyright-statement>© 2012, Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.</copyright-statement>
<copyright-year>2012</copyright-year>
</permissions>
<self-uri xlink:title="pdf" xlink:type="simple" xlink:href="amiajnl-2011-000182.pdf"></self-uri>
<abstract><sec><title>Background</title>
<p>Although the penetration of electronic health records is increasing rapidly, much of the historical medical record is only available in handwritten notes and forms, which require labor-intensive, human chart abstraction for some clinical research. The few previous studies on automated extraction of data from these handwritten notes have focused on monolithic, custom-developed recognition systems or third-party systems that require proprietary forms.</p>
</sec>
<sec><title>Methods</title>
<p>We present an optical character recognition processing pipeline, which leverages the capabilities of existing third-party optical character recognition engines, and provides the flexibility offered by a modular custom-developed system. The system was configured and run on a selected set of form fields extracted from a corpus of handwritten ophthalmology forms.</p>
</sec>
<sec><title>Observations</title>
<p>The processing pipeline allowed multiple configurations to be run, with the optimal configuration consisting of the Nuance and LEADTOOLS engines running in parallel with a positive predictive value of 94.6% and a sensitivity of 13.5%.</p>
</sec>
<sec><title>Discussion</title>
<p>While limitations exist, preliminary experience from this project yielded insights on the generalizability and applicability of integrating multiple, inexpensive general-purpose third-party optical character recognition engines in a modular pipeline.</p>
</sec>
</abstract>
<kwd-group><kwd>Luke</kwd>
<kwd>bioinformatics</kwd>
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</front>
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<affiliations><list><country><li>États-Unis</li>
</country>
<region><li>Illinois</li>
<li>Minnesota</li>
<li>Wisconsin</li>
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<tree><country name="États-Unis"><region name="Wisconsin"><name sortKey="Rasmussen, Luke V" sort="Rasmussen, Luke V" uniqKey="Rasmussen L" first="Luke V" last="Rasmussen">Luke V. Rasmussen</name>
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<name sortKey="Mccarty, Catherine A" sort="Mccarty, Catherine A" uniqKey="Mccarty C" first="Catherine A" last="Mccarty">Catherine A. Mccarty</name>
<name sortKey="Peissig, Peggy L" sort="Peissig, Peggy L" uniqKey="Peissig P" first="Peggy L" last="Peissig">Peggy L. Peissig</name>
<name sortKey="Starren, Justin" sort="Starren, Justin" uniqKey="Starren J" first="Justin" last="Starren">Justin Starren</name>
<name sortKey="Starren, Justin" sort="Starren, Justin" uniqKey="Starren J" first="Justin" last="Starren">Justin Starren</name>
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