Medical data mining: The search for knowledge in workers' compensation claims

Area of Science:

• Occupational health
• Data science
• Epidemiology

Background:

• Workers' compensation claims data represent an underutilized resource for scientific research.
• Data mining, a key component of knowledge discovery, offers potential for expanding research capabilities.
• Existing research has not fully explored the application of data mining to workplace injury claims.

Purpose of the Study:

• To investigate the utility of data mining techniques for enhancing the research potential of workers' compensation claims data.
• To introduce a novel descriptive data mining method, termed "claim beneficiation," for analyzing workplace injury claims.

Main Methods:

• Employed a descriptive data-mining methodology to ensure safety and avoid risks associated with predictive modeling.
• Focused on the selection of key descriptive variables from claims data.
• Developed a process named "claim beneficiation" to refine and extract meaningful information from claims.

Main Results:

• Identified four critical data forms within workplace injury claims: first report of injury, activity prescription form, functional capacity evaluation, and physical capability estimate.
• Demonstrated that combining data variables from these forms creates a comprehensive workplace injury case registry history.
• Established claim beneficiation as a novel method for refining claims data for research purposes.

Conclusions:

• Descriptive data mining, specifically claim beneficiation, significantly expands the research potential of workers' compensation claims.
• The developed workplace injury case registry history represents a new frontier for epidemiological research.
• This approach facilitates a deeper understanding of workplace injuries through enhanced data utilization.