Automated linking of free-text complaints to reason-for-visit categories and International Classification of Diseases diagnoses in emergency department patient record databases.

STUDY
OBJECTIVE: The use of the International Classification of Diseases system to describe emergency department (ED) case mix has disadvantages. We therefore developed computer algorithms that recognize a combination of words, word fragments, and word patterns to link free-text complaint fields to 20 reason-for-visit categories. We examine the feasibility and reliability of applying these reason-for-visit categories to ED patient-visit databases.
METHODS: We analyzed a database (containing complaints and International Classification of Diseases diagnoses for 1 year's visits to a single ED) using a 3-step process (create initial terms, maximize sensitivity, maximize specificity) to define inclusion and exclusion terms for 20 reason-for-visit categories. To assess the reliability of the reason-for-visit assignment algorithm, we repeated the final 2 steps on a second database, composed of visits sampled from 21 EDs. For each database, we determined the prevalence of complaints that link to each reason-for-visit category and the distributions of International Classification of Diseases, Ninth Revision diagnoses that resulted for all patients and patients stratified by age.
RESULTS: The 20 reason-for-visit categories capture 77% of all patients in database 1 (mean age 33.5 years) and 67% of all patients in database 2 (mean age 38.9 years). The percentage of visits captured by the 20 reason-for-visit categories, by age range, for databases 1 and 2 are (respectively) 0 to 2 years (84% and 76%), 3 to 10 years (82% and 74%), 11 to 65 years (76% and 68%), and 66 years or older (69% and 60%). The proportions of all complaints that link to each reason-for-visit category are largely similar between databases. Every complaint field that is linked to each reason-for-visit category includes at least 1 term that relates it to the category title, and the most frequently assigned diagnoses in each reason-for-visit category are those that one would expect to be associated with the reason-for-visit category complaints.
CONCLUSION: The method by which free-text complaint fields are parsed into reason-for-visit categories is feasible and reasonably reliable; the finalized database 1 reason-for-visit category inclusion/exclusion terms lists required only modest changes to work well in database 2. The reason-for-visit categories used here are broadly defined to maximize the proportion of visits that they capture; more narrowly defined reason-for-visit categories will require more extensive revision of their inclusion/exclusion terms lists when used in different databases. A prospective, reason-for-visit-based ED classification system could have several useful applications (including syndromic surveillance), although content validity analysis will be necessary to investigate this hypothesis.