Prevention and Management of Retained Surgical Items
Summary
Retained surgical items (RSIs) represent unintended foreign materials left in patients after operative procedures, constituting a serious threat to patient safety and healthcare quality. These events arise from complex interactions of human factors, system processes and technological limitations. Traditional prevention strategies have relied on meticulous manual counts of sponges and instruments, standardised checklists and team communication protocols. Nevertheless, manual counting alone has proved insufficient, prompting adoption of multimodal approaches that integrate advanced detection technologies—such as radiofrequency scanning, barcoding and radiopaque markers—with process improvements in operating‐room workflow. Risk mitigation also hinges on fostering a culture of safety, encouraging near‐miss reporting and regular team training in high‐reliability practices. Management of detected RSIs encompasses prompt identification through imaging and intraoperative detection tools, followed by minimally invasive or open retrieval to avert complications such as infection, obstruction or legal consequences. Globally, the emphasis is shifting toward proactive systems that blend human vigilance with automated detection, ultimately aiming to eliminate RSIs as never‐events and to enhance surgical quality across diverse healthcare settings.
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Prevention and Management of Retained Surgical Items publication trend
The graph below shows the total number of articles in prevention and management of retained surgical items across all publications each year (not limited to Nature Index journals).
Technical terms
Retained surgical item (RSI): A medical device or material inadvertently left inside a patient after surgery.
Gossypiboma: A cotton‐based surgical sponge retained in the body that incites a foreign‐body reaction.
Machine learning: A subset of artificial intelligence enabling algorithms to learn patterns from data to make predictions.
Deep learning: A branch of machine learning using multi‐layer neural networks to analyse complex data structures.
Radiofrequency identification (RFID): A technology employing electromagnetic fields to automatically identify and track tagged objects.
Radiopaque marker: A substance added to surgical materials to render them visible on radiographic imaging.
References
- Minimization of occurrence of retained surgical items using machine learning and deep learning techniques: a review. BioData Mining (2024).
- Modification of Polyvinyl Chloride Composites for Radiographic Detection of Polyvinyl Chloride Retained Surgical Items. Polymers (2023).
- Risk factors and preventive strategies for unintentionally retained surgical sharps: a systematic review. Patient Safety in Surgery (2021).
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