Development of an optical character recognition pipeline for handwritten form fields from an electronic health record

• Published in: Journal of the American Medical Informatics Association : JAMIA Vol. 19; no. e1; pp. e90 - e95
• Main Authors: Rasmussen, Luke V, Peissig, Peggy L, McCarty, Catherine A, Starren, Justin
• Format: Journal Article
• Language: English
• Published: England BMJ Group 01.06.2012
• Series: FOCUS on clinical research informatics
• Subjects: Electronic Data Processing; Electronic Health Records; Humans; Research and Applications
• ISSN: 1067-5027; 1527-974X; 1527-974X
• DOI: 10.1136/amiajnl-2011-000182

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.