Validation of a Clinical Decision‐Support Algorithm for Chronic Wound Classification and Treatment: An Expert Consensus

Accurate chronic wound classification is essential for appropriate management, yet diagnostic variability persists in routine practice. Transparent, rule‐based decision‐support tools may improve standardisation but require validation against expert judgement under clearly defined conditions. To evaluate inter‐expert agreement, agreement between a rule‐based algorithm and an expert‐consensus reference standard, diagnostic accuracy as a complementary measure, exploratory comparison with a non‐expert nurse, and expert agreement with algorithm‐generated therapeutic recommendations. Thirty anonymised standardised clinical cases were classified by the algorithm and one non‐expert nurse. Thirty wound‐care experts, including 26 nurses, three physicians, and one researcher, were organised into six independent panels of five and classified case subsets, yielding 150 ratings. A consensus reference diagnosis was defined a priori as agreement by at least 3/5 experts. The primary outcome was algorithm–consensus agreement using Cohen's κ . Expert reliability was assessed using Krippendorff's α and Fleiss' κ . Recommendation agreement was dichotomised and analysed exploratorily. Expert agreement was low to moderate (Krippendorff's α = 0.26–0.60), highest for pressure ulcers/injuries and venous leg ulcers, and lowest for mixed or unknown leg ulcers and diabetic foot ulcers. Consensus was reached in 29 of 30 cases. The algorithm achieved 86.2% accuracy (25/29) and substantial agreement ( κ = 0.70, 95% CI 0.46–0.94). Nurse accuracy was 72.4% (21/29, p = 0.219 ). Experts endorsed 85.2% of therapeutic recommendations. The algorithm showed promising agreement under controlled conditions, supporting further prospective validation in larger, balanced real‐world datasets.