Identifying Work-Related Injuries Using Text Analysis
Author Information
Author(s): McKenzie Kirsten, Campbell Margaret A, Scott Deborah A, Discoll Tim R, Harrison James E, McClure Roderick J
Primary Institution: National Centre for Health Information Research and Training, Queensland University of Technology
Hypothesis
Can different text interrogation methods effectively identify work-related injuries in emergency department data?
Conclusion
The study supports the development of text searching methods to enhance injury surveillance from emergency department data.
Supporting Evidence
- The keyword search detected 58% of work-related cases with high specificity.
- The index search improved detection to 62% but had lower specificity.
- Content analytic text mining detected 77% of cases with good specificity.
- Overall, 10.3% of cases were coded as 'Working for an income'.
- False positives were identified in all methods, highlighting the need for careful review.
Takeaway
This study looked at different ways to find work-related injuries in hospital records by analyzing the words used in injury descriptions.
Methodology
Three methods were used: keyword search, index search, and content analytic text mining to analyze injury description text fields.
Potential Biases
Potential biases may arise from the subjective nature of coding and the accuracy of the Activity codes used as a standard.
Limitations
The study relied on the quality of information recorded in text fields, which can be limited in busy emergency departments.
Participant Demographics
Data was collected from emergency departments in Queensland, Australia, covering a wide range of injury types and demographics.
Statistical Information
P-Value
p<0.01
Statistical Significance
p<0.01
Digital Object Identifier (DOI)
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