Automated misspelling detection and correction in clinical free-text records.
Accurate electronic health records are important for clinical care and research as well as ensuring patient safety. It is crucial for misspelled words to be corrected in order to ensure that medical records are interpreted correctly. This paper describes the development of a spelling correction system for medical text. Our spell checker is based on Shannon's noisy channel model, and uses an extensive dictionary compiled from many sources. We also use named entity recognition, so that names are not wrongly corrected as misspellings. We apply our spell checker to three different types of free-text data: clinical notes, allergy entries, and medication orders; and evaluate its performance on both misspelling detection and correction. Our spell checker achieves detection performance of up to 94.4% and correction accuracy of up to 88.2%. We show that high-performance spelling correction is possible on a variety of clinical documents.
Division of General Internal Medicine and Primary Care, Brigham and Womens Hospital, Boston, MA, USA. Electronic address: klai5@partners.org.
Kenneth H
KH
Lai
Division of General Internal Medicine and Primary Care, Brigham and Womens Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA.
Maxim
M
Topaz
Department of Emergency Medicine, University of Colorado, Aurora, CO, USA.
Foster R
FR
Goss
Division of General Internal Medicine and Primary Care, Brigham and Womens Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA; Clinical Informatics, Partners eCare, Partners HealthCare System, Boston, MA, USA.
Li
L
Zhou
D000068598:Data Accuracy; D057286:Electronic Health Records / Q000458:organization & administration*; D000069550:Machine Learning; D062527:Meaningful Use / Q000458:organization & administration; D009323:Natural Language Processing; D011785:Quality Assurance, Health Care / Q000379:methods*; D018875:Vocabulary, Controlled; D015443:Word Processing / Q000379:methods* / Q000592:standards
Electronic health record; Named entity recognition; Natural language processing; Spelling correction