Identifying work related injuries : comparison of methods for interrogating text fields

Abstract

Background:
Work-related injuries in Australia are estimated to cost around $57.5 billion annually,
however there are currently insufficient surveillance data available to support an
evidence-based public health response. Emergency departments (ED) in Australia are
a potential source of information on work-related injuries though most ED’s do not
have an ‘Activity Code’ to identify work-related cases with information about the
presenting problem recorded in a short free text field. This study compared methods
for interrogating text fields for identifying work-related injuries presenting at
emergency departments to inform approaches to surveillance of work-related injury.----------
Methods:
Three approaches were used to interrogate an injury description text field to classify
cases as work-related: keyword search, index search, and content analytic text mining.
Sensitivity and specificity were examined by comparing cases flagged by each
approach to cases coded with an Activity code during triage. Methods to improve the
sensitivity and/or specificity of each approach were explored by adjusting the
classification techniques within each broad approach.----------
Results:
The basic keyword search detected 58% of cases (Specificity 0.99), an index search
detected 62% of cases (Specificity 0.87), and the content analytic text mining (using
adjusted probabilities) approach detected 77% of cases (Specificity 0.95).----------
Conclusions
The findings of this study provide strong support for continued development of text
searching methods to obtain information from routine emergency department data, to
improve the capacity for comprehensive injury surveillance.