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.