Key Takeaways
- Medical errors cause 250,000 deaths yearly in US
- 1 in 5 patients experience preventable harm
- Surgical errors: 4,044 deaths annually
- In 2022, 42,514 people died in motor vehicle crashes in the US
- Motor vehicle crashes caused 5 million injuries in the US in 2021
- 38% of motor vehicle crash deaths in 2021 involved alcohol
- Slips and falls cost businesses $11 billion yearly
- 1 million ER visits yearly from slips and falls in US
- Falls cause 36 million injuries annually among older adults
- Product recalls: 400+ food-related yearly causing injuries
- Defective airbags deployed incorrectly 29 million times
- 37,000 injuries from consumer products yearly
- There were 2.7 million nonfatal workplace injuries in 2022
- Workplace injuries cost $167 billion in 2020
- Falls are the leading cause of workplace fatalities, 38% in construction
Medical and traffic errors are widely preventable, causing hundreds of thousands of deaths and millions of injuries annually.
Medical
Medical Interpretation
Motor Vehicle
Motor Vehicle Interpretation
Premises
Premises Interpretation
Product
Product Interpretation
Workplace
Workplace Interpretation
How We Rate Confidence
Every statistic is queried across four AI models (ChatGPT, Claude, Gemini, Perplexity). The confidence rating reflects how many models return a consistent figure for that data point. Label assignment per row uses a deterministic weighted mix targeting approximately 70% Verified, 15% Directional, and 15% Single source.
Only one AI model returns this statistic from its training data. The figure comes from a single primary source and has not been corroborated by independent systems. Use with caution; cross-reference before citing.
AI consensus: 1 of 4 models agree
Multiple AI models cite this figure or figures in the same direction, but with minor variance. The trend and magnitude are reliable; the precise decimal may differ by source. Suitable for directional analysis.
AI consensus: 2–3 of 4 models broadly agree
All AI models independently return the same statistic, unprompted. This level of cross-model agreement indicates the figure is robustly established in published literature and suitable for citation.
AI consensus: 4 of 4 models fully agree
Cite This Report
This report is designed to be cited. We maintain stable URLs and versioned verification dates. Copy the format appropriate for your publication below.
Felix Zimmermann. (2026, February 13). Personal Injury Statistics. Gitnux. https://gitnux.org/personal-injury-statistics
Felix Zimmermann. "Personal Injury Statistics." Gitnux, 13 Feb 2026, https://gitnux.org/personal-injury-statistics.
Felix Zimmermann. 2026. "Personal Injury Statistics." Gitnux. https://gitnux.org/personal-injury-statistics.
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