Surgery · Medical Literature
Exploring patient and NHS staff acceptability of artificial intelligence-supported surgical wound monitoring within the WISDOM feasibility study: a multi-centre qualitative interview study.
Abstract
Artificial intelligence (AI) is beginning to be used within digital surgical wound monitoring to facilitate implementation at scale. AI acceptability is essential to its success. This study aimed to explore patient and staff acceptability of an AI-based digital surgical wound monitoring platform. Qualitative interviews SETTING: Two hospitals performing cardiac surgery in England. 20 patients undergoing cardiac surgery and 10 clinical staff participating in a randomised feasibility trial of surgical wound monitoring with AI. Semi-structured interviews were conducted focusing on participants' experiences or perceptions of AI-based digital surgical wound monitoring. Data were analysed, guided by the theoretical framework of acceptability. Patient and staff acceptability of AI within surgical wound monitoring. Patients and staff were supportive of the use of AI within surgical wound monitoring and felt safety was improved, access was increased and efficiency was improved. AI monitoring was perceived to allow the early detection and treatment of surgical wound complications and facilitate the implementation of monitoring at scale for all patients. Some participants were concerned about data security risks, staff over-reliance on AI and the loss of human interaction. AI-supported digital surgical wound monitoring appeared acceptable to participants within this feasibility study. Further research is needed to evaluate implementation in broader settings. ISRCTN16900119 and NCT06475703.
Clinical relevance
Surgical site infections and wound complications following cardiac surgery represent a significant source of morbidity, prolonged hospitalisation, and NHS resource utilisation. This qualitative feasibility study addresses a critical implementation gap by evaluating the human acceptability — among both patients and clinical staff — of AI-integrated digital wound monitoring platforms prior to large-scale deployment. As AI-driven remote monitoring tools move toward routine surgical practice, understanding stakeholder acceptability is a necessary precondition for safe and effective adoption.
Key findings
1. Patient and staff acceptability of AI-supported digital surgical wound monitoring was broadly positive across both participant groups (n=30: 20 cardiac surgery patients, 10 clinical staff) at two NHS cardiac surgery centres in England. 2. Perceived benefits reported by both cohorts included improved patient safety, increased access to wound monitoring, and enhanced operational efficiency — though no quantitative effect sizes were reported in the abstract. 3. AI monitoring was perceived by participants to enable earlier detection and treatment of surgical wound complications, a clinically meaningful qualitative outcome in a population at elevated risk for sternal wound infections and mediastinitis. 4. Implementation at scale for all patients was identified as a key perceived advantage, suggesting AI may address equity and capacity limitations inherent to traditional in-person or nurse-led wound review. 5. Concerns were raised by a subset of participants around three themes: data security risks, the potential for staff over-reliance on AI decision-support, and erosion of therapeutic human interaction in postoperative care. 6. The study is registered under ISRCTN16900119 and NCT06475703 as a randomised feasibility trial; qualitative findings are preliminary and hypothesis-generating, with no quantitative efficacy or diagnostic accuracy data yet reported.
Practice implications
Clinicians and surgical teams considering adoption of AI-based wound monitoring platforms should proactively address staff training to mitigate over-reliance on algorithmic outputs, ensuring AI functions as decision-support rather than a replacement for clinical judgement, particularly in high-acuity cardiac surgery populations. Governance frameworks must incorporate robust data security protocols to address documented patient and staff concerns, which if unresolved may undermine platform uptake and trust. Broader multicentre implementation studies with quantitative endpoints — including diagnostic accuracy, time-to-intervention for wound complications, and patient-reported outcomes — are needed before this technology can be recommended for routine post-cardiac surgery wound surveillance.