Abstract Background Chronic wounds are associated with pain, odor, loss of mobility, an increased risk for infection and sepsis and a reduced quality of life. Treatment costs are rising due to the longevity of the population and increasing number of diabetes cases. For service planning in healthcare, it is thus necessary to describe patient characteristics and estimate the prevalence and incidence of chronic wounds. Currently, no data exists on this subject broken down by age group, gender, and province in Austria. Methods A retrospective analysis of real-world data was conducted using two merged datasets from an outpatient wound centre and the Austrian Health Insurance Fund. The study population consisted of people who resided in Austria and were covered by statutory health insurance from 2018 to 2022. We performed a descriptive, socio-demographic analysis of the data of patients, followed by a set of cluster analyses using the unsupervised K-means algorithm. From these clusters, we derived subgroup characteristics and patient trajectories and calculated prevalence and incidence rates for chronic wounds according to diagnoses, different age groups, gender, and different regions. Results Gender was almost evenly distributed in the wound centre’s data set (n = 4963; 49.6% female). The age distribution revealed the age group from 71-80 years being the largest group. In general, more women than men are affected by chronic wounds from the age of 81 onwards. 31% of chronic wound patients had diabetes as an underlying condition. More men were affected by peripheral arterial occlusive disease and diabetic foot syndrome and, conversely, more women by chronic venous insufficiency. These are preliminary results of the analyses. Conclusions Chronic wound patients were characterised by older age and multi-morbidity. Interestingly, gender differences appeared in the occurrence of comorbidities. Key messages • This study makes a crucial contribution in making routine data usable for the epidemiological study of chronic wounds, a cluster of diseases of complex aetiology. • It thus contributes to filling a knowledge gap. The prevalence estimate serves as a basis of knowledge for health care planning and a basis for the health economic assessment of the problem.