Dataset Information

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

ABSTRACT: 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.

SUBMITTER: Meyers AR

PROVIDER: S-EPMC5868484 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature

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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 AR Al-Tarawneh Ibraheem S IS Wurzelbacher Steven J SJ Bushnell P Timothy PT Lampl Michael P MP Bell Jennifer L JL Bertke Stephen J SJ Robins David C DC Tseng Chih-Yu CY Wei Chia C Raudabaugh Jill A JA Schnorr Teresa M TM

Journal of occupational and environmental medicine 20180101 1

<h4>Objective</h4>This study leveraged a state workers' compensation claims database and machine learning techniques to target prevention efforts by injury causation and industry.<h4>Methods</h4>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) inter ...[more]

PMID: 28953071

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Dataset Information

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

Publications

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

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