Artificial intelligence crowdsourcing competition for injury surveillance
In 2018, NIOSH, the Bureau of Labor Statistics (BLS), and OSHA contracted the National Academies of Science (NAS) to conduct a consensus study on improving the cost-effectiveness and coordination of occupational safety and health (OSH) surveillance systems. NAS’s report recommended that the federal government use recent advancements in machine learning and artificial intelligence (AI) to automate the processing of data in OSH surveillance systems.
The main source of OSH information on fatal and non-fatal workplace incidents comes from the unstructured free-text “injury narratives” recorded in surveillance systems. For example, an employer may report an injury as “worker fell from the ladder after reaching out for a box.” For decades, humans have read these injury narratives to assign standardized codes using the U.S. Bureau of Labor Statistics’ (BLS) Occupational Injury and Illness Classification System (OIICS). Coding these injury narratives to analyze data is expensive, time consuming, and fraught with coding errors.