AI and Workers’ Comp
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The Journal of Occupational and Environmental Medicine just awarded the article “Applying Machine Learning to Workers’ Compensation Data to Identify Industry-Specific Ergonomic and Safety Prevention Priorities, Ohio, 2001 to 2011” with an Adolph G. Kammer Merit in Authorship Award for the best article published in their journal last year.
The article details how researchers used artificial intelligence (AI) or machine learning methods to successfully auto-code over 1 million workers compensation claims. Essentially, researchers ”taught” a computer to use an extensive and complicated data set to answer the research question “What caused this injury?”
Researchers from the National Institute for Occupational Safety and Health (NIOSH) together with colleagues from the Ohio Bureau of Workers’ Compensation (OHBWC) used auto-coding to determine the cause of 1.2 million workers’ compensation claims. The claims were placed into one of three, broad categories: (1) ergonomic related; (2) slips, trips, and falls; and (3) all other categories combined. The number of records auto-coded in this study surpasses other similar studies. What took the revised computer program less than 3 hours to finish would have taken 4.5 years to manually code at an average manual coding rate of 2.2 claims per minute.
With this success, researchers were able to create another major causation-specific U.S. occupational injury surveillance system. Previously, there were only a few such systems, including the Bureau of Labor Statistics (BLS) Survey of Occupational Injuries and Illnesses (SOII) and the Safety and Health Assessment and Research for Prevention Program of the Washington State Department of Labor and Industries (W-L&I). Having another source of surveillance data for Ohio is a valuable resource which allows researchers to analyze data from multiple sources to find common patterns of risk among specific industries.
The results are already being used to prioritize prevention and research efforts by industry using ergonomic or slips, trips and falls intervention strategies. The research prioritized 267 industry groups by cause of injury for both lost-time claims and total claims. Additionally, using these data, NIOSH has prioritized NIOSH-funded research to a greater degree of precision, which can maximize impact. The data were also recently visualized in a dashboard that was published online to improve access and encourage further exploration. Additional data through 2015 will be added next month.
The workers’ compensation system provides medical treatment for workplace injuries or illnesses and compensates injured workers for a portion of their lost wages. The data collected through this system can also provide valuable information on how to prevent the injury from occurring in the first place by identifying the cause. The use of auto-coding to explore these and other databases makes them infinitely more valuable as an occupational injury and illness surveillance tool. Other state bureaus or insurers may be able to use similar methods. If you are interested in working with NIOSH please contact the NIOSH Center for Workers’ Compensation Studies at cwcs@cdc.gov. The center is constantly improving auto-coding capabilities and actively working with partners towards auto-coding external cause into more detailed categories.
Alysha R. Meyers, PhD, CPE, is an epidemiologist in the NIOSH Division of Surveillance, Hazard Evaluations, and Field Studies
6 comments on “AI and Workers’ Comp”
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Congrats Dr. Meyers. This is fascinating. Great work.
SO proud of you Dr. Myers!!! I am a member of a Medical Review Board and we cover volumes of protocols presented by PIs, including Workers Comp. Great article and with your consent, would like to share your article with board members. Please let me know. Lilli Rollins
Thank you for the kind words. Please share the article and let me know if you have any questions.
This is an informative post. Got a lot of info and details from here. Thank you for sharing this and looking forward to reading more of your post.
The next step should be the inclusion of chemical exposure illnesses and fatalities. Since many of these illnesses have a latency period it may be hard to connect them with an exposure at a particular workplace and time. Consequently, a great underestimation of the occupational illness and fatality rate has been ongoing and is ripe for effective cause analysis research.
The number of fatal illnesses per year is 10X or more than the number of injury fatalities. The Bureau of Labor Statistics states that they do not count illnesses or their fatalities in their annual statistics on incidence and fatalities.
Thank you for your comment. Since this blog posted we published a paper with more detailed event/exposure categories “Workers’ compensation claim counts and rates by injury event/exposure among state-insured private employers in Ohio, 2007–2017”. Chemical exposures are included in the ‘Exposure to Harmful Substances or Environments’ category. Although some of these claims were fatalities, the paper doesn’t report count fatal workers’ compensation claims separately.
While we are not BLS, I wanted to mention that BLS does count illnesses and fatalities in their annual statistics. Here is a link to their SOII data: https://www.bls.gov/iif/soii-data.htm
This report is just on nonfatal illnesses, for example https://www.bls.gov/charts/injuries-and-illnesses/number-and-rate-of-nonfatal-work-illnesses-by-industry.htm