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Coded Chief Complaints—Automated Analysis of Free‐text Complaints
Author(s) -
Thompson David A.,
Eitel David,
Fernandes Christopher M.B.,
Pines Jesse M.,
Amsterdam James,
Davidson Steven J.
Publication year - 2006
Publication title -
academic emergency medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.221
H-Index - 124
eISSN - 1553-2712
pISSN - 1069-6563
DOI - 10.1197/j.aem.2006.02.013
Subject(s) - emergency department , parsing , medicine , text messaging , complaint , schema (genetic algorithms) , artificial intelligence , natural language processing , machine learning , computer science , world wide web , nursing , political science , law
Objectives:To describe a new chief‐complaint categorization schema, the development of a computer text‐parsing algorithm to automatically classify free‐text chief complaints into this schema, and use of these coded chief complaints to describe the case mix of a community emergency department (ED).Methods:Coded Chief Complaints for Emergency Department Systems (CCC‐EDS) is a new and untested schema of 228 chief complaints, grouped within dimensions of type and system. A computerized text‐parsing algorithm for automatically reading and classifying free‐text chief complaints into 1 of these 228 coded chief complaints was developed by using a consecutive derivation sample of 46,602 patients who presented to a community teaching‐hospital ED in 2004. Descriptive statistics included frequency of patients presenting with the 228 coded chief complaints; percentage of free‐text complaints not categorizable by the CCC‐EDS; and admission rate, age, and gender differences by chief complaint.Results:In the derivation sample, the text‐parsing algorithm classified 87.5% of 45,329 ED visits with non‐null free‐text chief complaints into 1 of 194 coded chief complaints. The text‐parsing algorithm successfully classified 87.3% of the free‐text chief complaints in a validation sample. The five most common coded chief complaints were Abdominal Pain (3,734 visits), Fever (2,234), Chest Pain (2,183), Breathing Difficulty (2,030), and Cuts‐Lacerations (2,028).Conclusions:The CCC‐EDS is a new comprehensive, granular, and useful classification schema for categorizing chief complaints in an ED. A CCC‐EDS text‐parsing algorithm successfully classified the majority of free‐text chief complaints from an ED computer log. These coded chief complaints were used to describe the case mix of a community teaching‐hospital ED.