Routine narrative analysis as a screening tool to improve data quality

Abstract

Aim: To determine whether narrative information in emergency department surveillance systems can be systematically interrogated to improve our understanding of the causes of injury. Methods: Screening algorithms for location, intent, and activity were developed from structured analysis of narrative data from 98 999 records. The algorithms were then tested on a 50 000 record database containing entries in both of the two narrative fields. A proxy gold standard was defined as the total extract using both code and narrative. Sensitivity and specificity of the emergency department coding and narrative algorithms was calculated. Results: The proportion of records carrying an informative emergency department code was higher in records containing narrative - the percentage of causes coded "not know" dropped by 28.3%. The sensitivity of coded data varied from 42% to 98 % and from 33% to 99% for narrative data. Narrative analysis increased the percentage of home injuries identified by 19%, assaults by 26%, and rugby injuries by 137%. Conclusions: Using a small amount of narrative is a practical and effective means of developing more informative injury causation data in an emergency department based surveillance system. It allows for internal validation of the codes and for the identification of emerging hazards without adding more "tick boxes" or further burdening data entry clerks.