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Why near-miss reports can reveal safety risks
A near miss is a close call that did not result in the injury or illness that could have occurred. Its report may capture the task, location, equipment, conditions and controls involved. When reports are collected consistently, similarities across events can point to a hazardous task or condition that warrants investigation.
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OSHA describes incidents, including close calls and near misses, as indicators of where hazards exist. It recommends investigating to identify underlying causes—which may be multiple—rather than stopping at individual blame. Grouping similar incidents and considering both potential severity and likelihood can help prioritize corrective actions. OSHA’s hazard identification and assessment guidance explains this approach.
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What AI can do with incident data—and what it cannot establish
Make reports easier to organize
Incident reports often include free-text descriptions. Natural-language processing can help sort those descriptions into categories, surface recurring terms and make larger collections easier to review. OSHA’s Injury Tracking Application documentation describes an example: OIICS codes generated with a BLS AI-powered auto-coder add standardized categories to narrative fields, helping cases be grouped and analyzed. Categorization can support analysis, but it is not by itself proof that a model can forecast future accidents. OSHA’s ITA documentation describes the system and its data.
Look for patterns across different kinds of information
Depending on the setting and available data, analysis may combine reports with context such as sensor readings, images or video. NIOSH has described sensors as potentially useful for exposure estimates and prediction of adverse events, and natural-language processing as a possible way to review safety reports. These are potential applications, not evidence that every system can reliably predict a future event at every workplace. NIOSH’s discussion of AI and the future of work notes both the promise and the research gaps.
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Treat a signal as a prompt for investigation
A model may flag a repeated task, location or condition for review. People who understand the site can check whether the pattern reflects a genuine hazard, incomplete reporting, a change in work or some other context. The model’s output should guide attention; the investigation and hazard-control decisions remain human responsibilities.
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How to turn reports into corrective action
- Make reporting practical. Explain what counts as a near miss, how to report one and what details help reconstruct the event. Make the process accessible and encourage reporting without treating a report as an admission of fault.
- Review reports promptly. Involve the relevant managers and worker representatives. Investigate what happened, what could have happened and which underlying factors—including tools, time, training, maintenance or supervision—may have contributed.
- Standardize without losing context. Apply consistent categories so similar reports can be compared, while retaining the original narrative and relevant task and site details.
- Find recurring conditions and prioritize carefully. Group similar events and assess plausible severity and likelihood. Raw report counts alone are not a risk score: differences in reporting practice or data completeness can affect what appears most frequent.
- Assign and verify controls. Give corrective actions an owner, communicate relevant lessons and follow up to check whether the change addressed the hazard.
- Validate any AI-assisted analysis. Compare model classifications and alerts with human review and local outcomes. Document limitations, monitor for bias or drift, and keep a person accountable for decisions.
Choosing an analysis approach
The right level of technology depends less on having the most complex model than on whether the organization can investigate findings and close corrective actions. These approaches are not a head-to-head product comparison; they describe different ways to organize the work.
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Reporting and investigation process
A consistent reporting and investigation process can expose hazards without predictive software. OSHA recommends planning investigations and preparing forms or templates. A log or form is a recordkeeping aid: it does not predict risk or replace a sound investigation.
In-house trend analysis
An organization can group reports using consistent categories and compare them with site and task context. This can make recurring patterns visible while preserving local knowledge. The approach still depends on staff having the time and skill to review trends and verify what they mean.
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EHS or predictive-analytics software
Software may help consolidate reports and other safety information, standardize categories or surface trends. Before adopting it, check whether it connects reports to inspections and corrective actions, whether its alerts can be explained and verified, and whether the organization has a process to respond. Ask what evidence supports its performance: a case study describes one deployment, not validated accuracy across comparable workplaces.
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Analysis can only reflect the information it receives. Missing details, inconsistent descriptions or changes in reporting practice can distort patterns. An increase in reports does not automatically mean conditions became more dangerous, just as a decrease does not establish that risk fell. The cited guidance does not quantify underreporting or provide a universal adjustment for it; assess the quality and consistency of local reporting rather than applying an assumed correction.
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Published datasets also have limits. OSHA’s Injury Tracking Application covers defined groups of establishments, not every workplace. OSHA cautions that the data may not generalize to the worker population, notes that some submitted errors remain unresolved and says establishment counts are not agency-validated. It also says rates in these data should not be used alone to label establishments the most or least dangerous. For operational decisions, check the dataset’s coverage and year, and give preference to comparable local information when available. OSHA’s ITA page sets out these qualifications.
Workplace AI also raises issues beyond analytic accuracy. NIOSH has highlighted black-box challenges and concerns involving worker autonomy, privacy and bias. A responsible deployment should tell workers what information is collected and why, limit access appropriately, test whether errors or effects differ across groups and sites, and provide a meaningful way for workers to add context or challenge a misleading inference. A person should remain responsible for deciding what action to take. NIOSH’s 2024 discussion of managing AI-related workplace risks addresses these concerns.
What current survey and case-study figures show
Employer interest, not universal adoption
The National Safety Council’s 2024 Safety Technology Survey covered 500 employers and 1,000 employees in safety-sensitive industries. In its spotlight on predictive analytics, 20% of surveyed employers reported currently using it, while 45% said they were testing or considering it. Those figures describe that survey population and year, not workplace adoption everywhere. The NSC also identifies development cost, data security, human oversight and bias as considerations. Read the NSC’s 2024 predictive-analytics spotlight.
Reported deployments are examples, not general performance guarantees
An NSC case-study report published in 2023 describes a JE Dunn/Newmetrix deployment that used project, weather, staffing, image and video data from 2016 to 2021. The report says 75% of recordable incidents occurred on the seven projects ranked highest for risk each week, and reports 350 additional safety conversations. These are outcomes attributed to that vendor-associated case study; they do not establish general model accuracy or show that the same results should be expected elsewhere.
The same report describes Los Alamos National Laboratory’s Cority implementation, reporting that 75 surplus software applications were decommissioned and 1–2 FTEs’ worth of work was redirected to other initiatives. Those figures are also specific to the case-study account, not typical savings estimates or evidence of predictive performance. Read the NSC’s 2023 predictive-analytics and machine-learning case studies.
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