Extracting Clinical Features From Dictated Ambulatory Consult Notes Using a Commercially Available Natural Language Processing Tool: Pilot, Retrospective, Cross-Sectional Validation Study.

ABSTRACT: BACKGROUND:The increasing adoption of electronic health records (EHRs) in clinical practice holds the promise of improving care and advancing research by serving as a rich source of data, but most EHRs allow clinicians to enter data in a text format without much structure. Natural language processing (NLP) may reduce reliance on manual abstraction of these text data by extracting clinical features directly from unstructured clinical digital text data and converting them into structured data. OBJECTIVE:This study aimed to assess the performance of a commercially available NLP tool for extracting clinical features from free-text consult notes. METHODS:We conducted a pilot, retrospective, cross-sectional study of the accuracy of NLP from dictated consult notes from our tuberculosis clinic wit

SUBMITTER: Petch J 

PROVIDER: S-EPMC6913750 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature