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

BMC Medical Informatics and Decision Making

Research Article

Roderick J McClure¹  Margaret A Campbell²  Kirsten McKenzie²  Deborah A Scott³  James E Harrison⁴  Tim R Discoll⁵ 

[1] Monash University Accident Research Centre, Monash University Clayton Campus, 3800, Melbourne, Victoria, Australia;National Centre for Health Information Research and Training, Queensland University of Technology, Victoria Park Road, 4059, Kelvin Grove, Queensland, Australia;National Centre for Health Information Research and Training, Queensland University of Technology, Victoria Park Road, 4059, Kelvin Grove, Queensland, Australia;Queensland Injury Surveillance Unit (QISU), Mater Hospital, Stanley Street, 4101, Brisbane, Queensland, Australia;Research Centre for Injury Studies, Flinders University, 5042, Laffer Drive, Bedford Park, South Australia, Australia;School of Public Health, University of Sydney, Fisher Road, 2050, Camperdown, New South Wales, Australia;

关键词: Positive Predictive Value;    Work Activity;    Keyword Search;    Activity Code;    Text Field;   

DOI  :  10.1186/1472-6947-10-19

 received in 2009-08-17, accepted in 2010-04-07,  发布年份 2010

来源: Springer

【 摘 要 】

BackgroundWork-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.MethodsThree 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.ResultsThe 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).ConclusionsThe 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.

【 授权许可】

CC BY   
© McKenzie et al; licensee BioMed Central Ltd. 2010