Clinical decision support tools in wound management: A scoping review of existing and emerging tools.

Clinical Snapshot

Key Findings

• P Value: Not reported

• Effect Size: Not applicable — scoping review with narrative synthesis only; no pooled effect size calculated

• Primary Outcome: Identification and mapping of CDSTs used in wound management: 17 studies included; most CDSTs incorporated structured wound assessment and treatment planning; only one tool was fully validated; two studies evaluated AI-supported CDSTs

• Nnt Or Sensitivity: Not reported; AI-enabled tools and a convolutional neural network-based model were described as improving assessment consistency, documentation accuracy, and workflow efficiency, but no sensitivity, specificity, or diagnostic accuracy metrics are reported in the abstract

• Confidence Interval: Not reported — no quantitative pooling performed

Clinical Application

Implementation feasibility varies considerably by tool type. Non-digital CDSTs (e.g., structured assessment frameworks) are immediately deployable with minimal infrastructure. AI-enabled CDSTs require digital infrastructure, staff training, workflow redesign, and institutional governance frameworks. Key barriers identified include clinician trust, concerns about clinical autonomy, and usability — all of which must be addressed in implementation planning. Senior nurses were less likely to rely on CDSTs, suggesting targeted education strategies are needed for experienced clinicians. This review is directly relevant to Australian practice. The authors are affiliated with the University of Sydney and Sydney Local Health District (Royal Prince Alfred Hospital), situating the work firmly within the Australian healthcare system. Wound management CDSTs are relevant to RACGP-aligned primary care, NSW Health community nursing, and hospital-based tissue viability services. The TGA regulates software as a medical device (SaMD) under the Therapeutic Goods Act 1989, and AI-enabled CDSTs would require TGA classification and approval before clinical deployment in Australia. The Australian Commission on Safety and Quality in Health Care (ACSQHC) Wound Management Standards and the National Safety and Quality Health Service (NSQHS) Standards provide the regulatory and quality framework within which CDSTs must be integrated. PBS reimbursement for wound care products is a separate consideration, but CDST adoption could influence prescribing patterns and product selection. The RACGP's Silver Book and chronic wound management guidelines provide a clinical governance context for primary care CDST integration. Nurses and clinicians managing chronic and acute wounds across acute hospital, primary care, and community health settings; particularly relevant for wound care specialists, tissue viability nurses, and general practice nurses

Abstract

AIM: This scoping review aimed to identify and map clinical decision support tools (both digital and non-digital) used by clinicians for wound management in acute, primary, and community health settings globally, with a focus on the use of artificial intelligence, and the barriers and enablers to implementation. MATERIALS AND METHODS: Studies published between January 2015, and August 2024 were identified through systematic searches of Scopus, Embase, MEDLINE, and CINAHL, conducted in accordance with JBI methodology and reported using the PRISMA-ScR guidelines. Eligible studies included quantitative, qualitative, and mixed-methods research examining non-digital and AI-supported clinical decision support tools for wound management across healthcare settings. RESULTS: Seventeen studies were included, two evaluating AI-supported clinical decision support tools. Most clinical decision support tools had structured wound assessment and treatment planning, though evidence of clinical effectiveness was limited, with only one tool fully validated. Adoption by nurses was influenced by experience, trust, training, and workflow integration, with senior nurses less likely to rely on clinical decision support tools. AI-enabled tools, and a convolutional neural network-based model, improved assessment consistency, documentation, and workflow efficiency. Key barriers included concerns about trust, clinical autonomy, and usability. CONCLUSIONS: Both traditional and AI-supported clinical decision support tools are used for chronic wound management across acute, primary, and community care, but evidence of effectiveness and validation remains limited. The absence of experimental studies highlights the need for rigorous evaluation, clinician education, and strategies to support integration of AI-enabled clinical decision support tool into routine practice.

References

1. 1.Symon, D.-J., Frotjold, A., & Barakat-Johnson, M. (2026). Clinical decision support tools in wound management: A scoping review of existing and emerging tools. Journal of Tissue Viability. https://doi.org/10.1016/j.jtv.2026.101019