Development of a mobile application for wound severity detection and modern dressing selection.

INTRODUCTION

Inconsistent wound assessment and inappropriate dressing selection remain major barriers in chronic diabetic wound management. Digital decision-support systems offer a practical approach for standardizing clinical processes and supporting evidence-based care.

OBJECTIVE

The aims of this study were to develop and evaluate a mobile application that integrates wound severity detection with modern dressing selection to support standardized chronic diabetic wound management.

METHODS

A research and development design was used with the Analysis, Design, Development, Implementation, and Evaluation (ADDIE) model. The application integrated the Perdana Kusuma II wound severity scoring system with a moisture-based dressing recommendation algorithm. Content validity was evaluated by 10 multidisciplinary experts using a four-point Likert scale. Usability was assessed by 12 wound care nurses with the System Usability Scale (SUS). A descriptive pre-test and post-test evaluation examined the performance in wound color identification, inflammation assessment, exudate evaluation, and dressing selection among new graduate and independent wound care nurses.

RESULTS

All expert validation items achieved the maximum score (4.00), with item-content validity and scale-content validity indexes of 1.00. The application exhibited good usability based on the SUS interpretation. Among the new graduate nurses, the mean pre-test scores increased from 0.92 to 1.42 to 1.92-2.00 across all domains. Independent wound care nurses had consistently high baseline scores (1.58-2.00) and near-maximum post-test values. Automated wound severity outputs were comparable to the manual assessments, and dressing recommendations were aligned with modern wound care standards.

CONCLUSION

The mobile application developed in this study is a feasible digital clinical decision-support tool with the potential to support standardized wound assessment and evidence-informed dressing selection, particularly for early-career nurses, while maintaining expert-level clinical accuracy.