Nursing-centered development of an AI-based decision support system in pressure ulcer and incontinence-associated...

Differentiating between stage 1 or 2 pressure ulcer/pressure injury (PU/PI) and incontinence-associated dermatitis (IAD) poses a significant challenge for healthcare professionals, due to their visual similarity. Incorrect assessments may trigger inappropriate interventions, potentially resulting in delayed treatment. KIADEKU is a multi-center research project aimed at supporting the assessment and documentation of PU/PI and IAD, as well as the implementation of evidence-based care through an AI-based application in nursing care. This paper investigates how to integrate evidence from nursing science and clinical practice into the development of the proposed AI system.

We conducted a literature review of nursing criteria for wound assessment. Nursing experts iteratively evaluated the findings, leading to the definition of a Minimum Data Set (MDS) that the research team used to annotate wound images for AI training. We collected a data set of wound images from the medical records of two university hospitals. To ensure high data quality, we implemented a validation process involving up to four independent expert assessments of each wound image. We calculated Krippendorff's alpha to assess the internal consistency of the annotation process for reliability analysis. This study adhered to the TRIPOD-AI guidelines.

The differentiation between PU/PI and IAD primarily relies on clinical observation and visual inspection, with key factors including aetiology, anatomical location, and wound morphology. The validated MDS encompasses 18 wound-related and four aetiological categories, including visual and contextual patient data. The AI system consequently integrates wound images with categorical patient information. The reliability analysis of 1,521 annotated wound images indicates substantial agreement for wound type classification (α = 0.64, 95% CI 0.62-0.68) and fair to moderate agreement for PU/PI (α = 0.57, 95% CI 0.55-0.63) and IAD categorization (α = 0.27, 95% CI 0.20-0.36).

The integration of evidence from nursing science and practice into the AI development process using a mixed-methods approach, established a robust, evidence-based foundation. This approach yielded an innovative implementation of routine care data for AI training, advancing the field of AI-driven wound care solutions.

Registered with the German Clinical Trials Register (DRKS) on 2023-09-05.

DRKS00029961.