Development of an optical character recognition pipeline for handwritten form fields from an electronic health record
Identifieur interne : 000114 ( Ncbi/Curation ); 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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