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Applying Machine Learning to Workers' Compensation Data to Identify Industry-Specific Ergonomic and Safety Prevention Priorities: Ohio, 2001 to 2011.

Meyers, Alysha R. PhD; Al-Tarawneh, Ibraheem S. PhD; Wurzelbacher, Steven J. PhD; Bushnell, P. Timothy PhD, MPA; Lampl, Michael P. MS; Bell, Jennifer L. PhD, MS; Bertke, Stephen J. PhD; Robins, David C. AAS; Tseng, Chih-Yu MS; Wei, Chia PhD; Raudabaugh, Jill A. MPH; Schnorr, Teresa M. PhD
Journal of Occupational & Environmental Medicine: Post Author Corrections: September 25, 2017
doi: 10.1097/JOM.0000000000001162
Original Article: PDF Only

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.

Copyright (C) 2017 by the American College of Occupational and Environmental Medicine