Coded Chief Complaints—Automated Analysis of Free‐text Complaints

Abstract

Objectives: To describe a new chief-complaint categorization schema, the development of a computer textparsing 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 nonnull 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.ACADEMIC EMERGENCY MEDICINE 2006; 13:774-782 ª

Semantic-enhanced models to support timely admission prediction at emergency departments

“…Once raw free-text CCs are mapped to standard terms in one of these coding schemes, semantic information embedded in the scheme can be captured to enhance subsequent analysis and prediction. Several studies have proposed approaches to pre-processing and mapping free-text CCs into standardized complaint categories (Chapman et al 2005; Thompson et al 2006;Travers and Haas 2004). However, there is not yet a universally accepted approach for CC standardization.…”

“…It is worth noting that any existing CC coding scheme should be applicable in this framework. In our study, we chose to use the Coded Chief Complaints for Emergency Department Systems (CCC-EDS), a comprehensive and granular scheme of chief complaints describing the reason for an ED visit (Thompson et al 2006). The CCC-EDS was developed based on the study of various ED CC literature and coding schemes, including RVC and ICD-9.…”

“…Time information was used to generate a Boolean variable ''night_shift'' (i.e., a night shift is between 2300 and 0659 hours). Because chief complaints are available as free text and may vary due to misspelling, abbreviations, and synonyms, the chief complaint information from paper charts was preprocessed by CCC-EDS and mapped to a coded CC (Thompson et al 2006). Admission information was obtained from charts and was regardless of destination or how long the patient may have stayed in the ED.…”

Evaluation of a hospital admission prediction model adding coded chief complaint data using neural network methodology

“…The neural network methodology has particular application to determining the value of case mix (chief complaint) data in that it can be used to perform classification of this variable without knowledge of the underlying probability model and can automatically include nonlinear and combination (interaction) terms [12,13]. Our objective was to apply the neural network methodology to determine whether adding coded chief complaint (CCC) data, a previously studied marker of ED patient case mix [14], to demographic, operational, and acuity data available at triage would result in an improved hospital admission prediction model than one using demographic, operational, and acuity data alone.…”

Revision of the Canadian Emergency Department Information System (CEDIS) Presenting Complaint List Version 1.1

“…They employ strategies to convert free text presenting complaints, using novel and complex algorithms, into a structured classification system. 13,14 If one has historical free text data, there is an obvious advantage in using this strategy, in that it allows one to categorize unstructured data. However, the perceived need to document a patient's complaint in their own words often drives the decision to use this type of coding system.…”