Journal of Taibah University Medical Sciences · 2026
Development of a mobile application for wound severity detection and modern dressing selection.
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Muhaemin Gunawan, Setiawan Henri, Amelia Vivi L, Supriyadi, Purwito Dedy
Аннотация
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.
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.