Format

Send to

Choose Destination
Pediatr Emerg Care. 2015 Jul;31(7):536-41. doi: 10.1097/PEC.0000000000000484.

An Introduction to Natural Language Processing: How You Can Get More From Those Electronic Notes You Are Generating.

Author information

1
From the *Department of Medicine, Division of Emergency Medicine, Boston Children's Hospital; †Children's Informatics Program; and ‡Boston Children's Hospital IT, and Department of Infectious Diseases, Boston, MA.

Abstract

Electronically stored clinical documents may contain both structured data and unstructured data. The use of structured clinical data varies by facility, but clinicians are familiar with coded data such as International Classification of Diseases, Ninth Revision, Systematized Nomenclature of Medicine-Clinical Terms codes, and commonly other data including patient chief complaints or laboratory results. Most electronic health records have much more clinical information stored as unstructured data, for example, clinical narrative such as history of present illness, procedure notes, and clinical decision making are stored as unstructured data. Despite the importance of this information, electronic capture or retrieval of unstructured clinical data has been challenging. The field of natural language processing (NLP) is undergoing rapid development, and existing tools can be successfully used for quality improvement, research, healthcare coding, and even billing compliance. In this brief review, we provide examples of successful uses of NLP using emergency medicine physician visit notes for various projects and the challenges of retrieving specific data and finally present practical methods that can run on a standard personal computer as well as high-end state-of-the-art funded processes run by leading NLP informatics researchers.

PMID:
26148107
DOI:
10.1097/PEC.0000000000000484
[Indexed for MEDLINE]

Supplemental Content

Full text links

Icon for Wolters Kluwer
Loading ...
Support Center