Information Quality Requirements for AI-Generated Patient Education Materials in Diabetic Foot Ulcer Care: A Directed Qualitative Content Analysis of Patient and Nurse Perspectives.

Abstract

Purpose: To explore the information quality requirements of artificial intelligence generated patient education materials for diabetic foot ulcers from the perspectives of patients and nurses.

Patients and methods: This single-center qualitative study was conducted at the Fifth Affiliated Hospital of Guangxi Medical University, China, from November 2025 to June 2026. Maximum-variation purposive sampling recruited 15 hospitalized patients with diabetic foot ulcers and 10 nurses. Saturation was assessed separately for each group and defined as no new codes, categories, or relevant insights. It was reached after 13 patient and 8 nurse interviews, with two additional interviews per group for confirmation. Face-to-face semi-structured interviews used standardized artificial intelligence generated diabetic foot ulcer education material as a discussion stimulus. Data were analyzed using directed content analysis guided by Wang and Strong's information quality framework.

Results: Five themes and 17 subthemes were identified. Four themes mapped to Wang and Strong's framework: intrinsic information quality (accuracy, believability, objectivity, and reputation); contextual information quality (relevancy, completeness, timeliness, appropriate amount of data, and value-added); representational information quality (ease of understanding, interpretability, consistent representation, and concise representation); and accessibility information quality (accessibility and access security). An additional specific theme comprised clinical safety and actionability. Both groups valued reliable, relevant, understandable, and accessible information, but their priorities differed: nurses emphasized clinical accuracy, professional review, and the risks of unsafe recommendations, whereas patients prioritized relevance to current wound-care needs, ease of understanding and access, and clear guidance for self-care.

Conclusion: Patients and nurses showed different but complementary priorities. Clinical safety and actionability extend conventional information quality considerations. Artificial intelligence may support educational content generation. However, a professional review is necessary for clinically sensitive information, and patient input is essential to ensure usability. Future studies should refine and validate these requirements across settings and user groups.