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Anesthesia Delivery Systems Recall: GE HealthCare Issues Correction for Certain Carestations due to Risk of Ineffective Ventilation When Used in Volume Control Ventilation (VCV) Mode
Haleon Issues Voluntary Nationwide Recall of Gas-X Extra Strength Softgels 125mg, 120 ct. and 72 ct.
Anesthesia Kit Correction: Becton Dickinson Issues Correction for BD Spinal Trays Containing Bupivacaine Ampules
UPDATE: Hintermann Series H3 Total Ankle Replacement Has a Higher-Than-Expected Risk of Device Failure: FDA Safety Communication
2024 Safety Communications
Early Alert: Catheter Introducer Kit Issue from Abiomed
Dexcom Uncovers Theft of Scrapped Product, Notifies Potentially Impacted Users
Pediatric Care Bed Correction: KayserBetten Issues Correction for KayserBett IDA Beds
Early Alert: IV Tubing Set Issue from ICU Medical
Early Alert: Heart Pump Issue from Abiomed
Early Alert: Heart Pump Issue from Abiomed
Insulet Initiates Voluntary Medical Device Correction for Certain Omnipod® Pods in the U.S. and Affected International Markets
Manifold Recall: Medline Removes Namic Star Off Handle Manifolds
Dialysis Catheter Kit Recall: Arrow International Removes Dialysis Catheter Kits Containing Merit Medical Splittable Sheath Introducers
Early Alert: Heart Pump Controller Issue from Abiomed
Ventilator Recall: React Health Removes VOCSN V+Pro Ventilators
Automated Compounding System Recall: Omnicell Removes Syringe Labels Used with the i.v.STATION Automated Compounding System
Thoracic Stent Graft Recall: Bolton Medical Removes Relay Pro System
Retrospective validation study of a large language model approach to screening for intraabdominal surgical site infections for quality and safety reporting
Surgical site infections (SSIs), particularly intra-abdominal (IAB) infections, are challenging to identify and remain a resource-intensive focus of infection prevention programmes. Current automated screening measures rely on discrete data from the electronic health record (EHR), such as microbiology results, diagnosis codes and/or return to the operating room. This approach has poor specificity, and therefore surveillance methods depend heavily on additional manual chart review by trained infection preventionists. Large language models (LLMs) offer an opportunity to improve surveillance by synthesising complex clinical documentation alongside structured data elements.
We evaluated the performance of a locally hosted LLM (gpt-35-turbo-16k) to improve IAB SSI screening using perioperative clinical notes and microbiology results. The model analysed documentation across the perioperative period (3 days before through 30 days after surgery) to generate case-level SSI summaries and likelihood assessments. We compared the performance of this tool against the current EHR-based screening workflow.
Among 1977 abdominal surgical cases, including 56 with confirmed IAB SSIs, the LLM screened 104 cases as high risk, identifying all infections (negative predictive value (NPV) 100%) and achieving a positive predictive value (PPV) of 53.8%. In contrast, the EHR-based workflow identified 288 cases for further review, with a PPV of only 19.4% and the same NPV of 100%. Analysis of 57 224 notes required ~107 million tokens, translating to approximately USD 0.05 per case.
An LLM-based approach to SSI surveillance has the potential to substantially improve efficiency while remaining highly accurate and cost-effective. By reducing reliance on manual chart review, this strategy could allow infection preventionists to shift their attention from surveillance toward quality improvement and patient safety initiatives.
