Introduction: Skin cancer prevention is a public health priority due to its high incidence and potential to cause significant morbidity and mortality. This study evaluated the effectiveness of an early skin cancer detection program in a community pharmacy, utilizing advanced technologies and interdisciplinary collaboration. Objectives: 1. Early detection of melanoma and non-melanoma skin cancer in patients visiting the community pharmacy. 2. Facilitate patient referral to a dermatologist through the pharmacy. Materials and Methods:A cross-sectional observational study was conducted in a community pharmacy between June 2018 and September 2023. Adult patients concerned about skin lesions were included. After obtaining written informed consent, an interview was conducted, and photographs of the lesions were taken and electronically sent to a dermatology service for a preliminary diagnosis within 24-48 hours. Results:A total of 41 patients were evaluated (51% men; 54% over 50 years old). Benign lesions were identified in 54% of cases, avoiding unnecessary consultations and optimizing health resources. 46% presented suspicious cancer lesions requiring detailed evaluation, with 63% of this group being over 60 years old. More than 85% of the preliminary diagnoses were accurate with the images sent. Discussion:The results underscore the effectiveness of early detection programs in community pharmacies for identifying benign and suspicious lesions, optimizing resources, and facilitating early interventions. The collaboration between pharmacists and dermatologists, along with the use of advanced technologies, were key to the program's success. However, the study's limitations include the sample size and its focus on a single pharmacy. Conclusions: Community pharmacies can playa crucial role in the secondary prevention of skin cancer, improving healthcare system efficiency and benefiting patients through early detection and timely intervention. Future studies with larger samples are recommended to validate these findings and quantify resource optimization.
