Applying Machine Learning to Workers’ Compensation Data to Identify Industry-Specific Ergonomic and Safety Prevention Priorities

Details

• Alternative Title:
  J Occup Environ Med
• Personal Author:
  Meyers, Alysha R. ; Al-Tarawneh, Ibraheem S. ; Wurzelbacher, Steven J. ; Bushnell, P. Timothy ; Lampl, Michael P. ; Bell, Jennifer L. ; Bertke, Stephen J. ; Robins, David C. ; Tseng, Chih-Yu ; Wei, Chia ; Raudabaugh, Jill A. ; Schnorr, Teresa M.
• Description:
  Objective
  
  This study leveraged a state workers’ compensation claims database and machine learning techniques to target prevention efforts by injury causation and industry.
  
  Methods
  
  Injury causation auto-coding methods were developed to code more than 1.2 million Ohio Bureau of Workers’ Compensation claims for this study. Industry groups were ranked for soft-tissue musculoskeletal claims that may have been preventable with biomechanical ergonomic (ERGO) or slip/trip/fall (STF) interventions.
  
  Results
  
  On the basis of the average of claim count and rate ranks for more than 200 industry groups, Skilled Nursing Facilities (ERGO) and General Freight Trucking (STF) were the highest risk for lost-time claims (>7 days).
  
  Conclusion
  
  This study created a third, major causation-specific U.S. occupational injury surveillance system. These findings are being used to focus prevention resources on specific occupational injury types in specific industry groups, especially in Ohio. Other state bureaus or insurers may use similar methods.
  
  
• Subjects:
  Article
• Source:
  J Occup Environ Med. 60(1):55-73
• Pubmed ID:
  28953071
• Pubmed Central ID:
  PMC5868484
• Document Type:
  Journal Article
• Funding:
  CC999999/Intramural CDC HHS/United States
• Volume:
  60
• Issue:
  1
• Collection(s):
  CDC Public Access
• Main Document Checksum:
  urn:sha256:ed9f1df2de89bcb78f630955b560b60fdb7ed5bec2369c89cb2372207162369b
• Download URL:
  https://stacks.cdc.gov/view/cdc/52833/cdc_52833_DS1.pdf
• File Type:
  [PDF - 574.89 KB ]