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Background Ageing populations are at risk of skin tears due to changes in their skin. Real-world data sets offer the ability to see current prevalence rates and practice changes to understand the size of the problem and glean practice insights. Aim Leveraging Swift Medical's big data set, a point-prevalence analysis was conducted over five years on skin tears in skilled nursing facilities (SNF) in North America, to better understand time to heal and explore the frequency of commonly used treatments and cleansing solutions for skin tears. Methods This descriptive prevalence study used a subset of an anonymised big dataset from participating SNFs across North America. Data from 188,675 skin tears in patients aged 20 years of age or older from 2017-2021 were included. Relative prevalence compared to other wounds was analysed, and healing times based on skin tear classification and frequency of primary, secondary and cleansing solutions were reported. Results More than 1.5 million wounds were included in this dataset, and skin tears accounted for 10.3-12.8% of skin tears in SNFs over the five-year period. The prevalence of skin tears increased with age. Median healing time ranged from 15-27 days, based on skin tear classification. Conclusion Big data sets can provide insight into current wound prevalence and practice patterns. The high prevalence of skin tears highlights the need for standardised tools to assess risk and prevent skin tears, and to educate clinicians on classifying and treating skin tears effectively. [ABSTRACT FROM AUTHOR]