TY - JOUR
T1 - Coded Chief Complaints-Automated Analysis of Free-text Complaints
AU - Thompson, David A.
AU - Eitel, David
AU - Fernandes, Christopher M.B.
AU - Pines, Jesse M.
AU - Amsterdam, James
AU - Davidson, Steven J.
PY - 2006/7
Y1 - 2006/7
N2 - 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.
AB - 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.
KW - algorithm
KW - chief complaint
KW - emergency department
KW - emergency medicine
KW - free-text
KW - triage
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U2 - 10.1197/j.aem.2006.02.013
DO - 10.1197/j.aem.2006.02.013
M3 - Article
C2 - 16723726
AN - SCOPUS:33745445345
SN - 1069-6563
VL - 13
SP - 774
EP - 782
JO - Academic Emergency Medicine
JF - Academic Emergency Medicine
IS - 7
ER -