In this article, we describe the role of "clinical scenario" information to assure the safety of interoperable systems, as well as the system's ability to deliver the requisite clinical functionality to improve clinical care. Described are methods and rationale for capturing the clinical needs, workflow, hazards, and device interactions in the clinical environment. Key user (clinician and clinical engineer) needs and system requirements can be derived from this information, therefore, improving the communication from clinicians to medical device and information technology system developers. This methodology is intended to assist the health care community, including researchers, standards developers, regulators, and manufacturers, by providing clinical definition to support requirements in the systems engineering process, particularly those focusing on development of Integrated Clinical Environments described in standard ASTM F2761. Our focus is on identifying and documenting relevant interactions and medical device capabilities within the system using a documentation tool called medical device interface data sheets and mitigating hazardous situations related to workflow, product usability, data integration, and the lack of effective medical device-health information technology system integration to achieve safe interoperability. Portions of the analysis of a clinical scenario for a "patient-controlled analgesia safety interlock" are provided to illustrate the method. Collecting better clinical adverse event information and proposed solutions can help identify opportunities to improve current device capabilities and interoperability and support a learning health system to improve health care delivery. Developing and analyzing clinical scenarios are the first steps in creating solutions to address vexing patient safety problems and enable clinical innovation. A Web-based research tool for implementing a means of acquiring and managing this information, the Clinical Scenario Repository™ (MD PnP Program), is described.
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http://dx.doi.org/10.1213/ANE.0000000000001351 | DOI Listing |
Int J Surg
January 2025
Department of Cardiovascular Surgery, Xijing Hospital, Xi'an, Shaanxi, China.
Background: The impact of aortic arch (AA) morphology on the management of the procedural details and the clinical outcomes of the transfemoral artery (TF)-transcatheter aortic valve replacement (TAVR) has not been evaluated. The goal of this study was to evaluate the AA morphology of patients who had TF-TAVR using an artificial intelligence algorithm and then to evaluate its predictive value for clinical outcomes.
Materials And Methods: A total of 1480 consecutive patients undergoing TF-TAVR using a new-generation transcatheter heart valve at 12 institutes were included in this retrospective study.
Microb Genom
January 2025
Department of Laboratory Medicine, Clinical Microbiology, Faculty of Medicine and Health, rebro University, rebro, Sweden.
National epidemiological investigations of microbial infections greatly benefit from the increased information gained by whole-genome sequencing (WGS) in combination with standardized approaches for data sharing and analysis. To evaluate the quality and accuracy of WGS data generated by different laboratories but analysed by joint pipelines to reach a national surveillance approach. A national methicillin-resistant (MRSA) collection of 20 strains was distributed to nine participating laboratories that performed in-house procedures for WGS.
View Article and Find Full Text PDFInt J Surg
January 2025
Department of Trauma and Emergency Surgery, Chang Gung Memorial Hospital, Linkou; Chang Gung University, Taoyuan, Taiwan.
Background: Detecting kidney trauma on CT scans can be challenging and is sometimes overlooked. While deep learning (DL) has shown promise in medical imaging, its application to kidney injuries remains underexplored. This study aims to develop and validate a DL algorithm for detecting kidney trauma, using institutional trauma data and the Radiological Society of North America (RSNA) dataset for external validation.
View Article and Find Full Text PDFInt J Surg
January 2025
Department of Otolaryngology Head and Neck Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui Province, China.
Background And Objectives: Recent advances in multimodal large language models (MLLMs) have shown promise in medical image interpretation, yet their utility in surgical contexts remains unexplored. This study evaluates six MLLMs' performance in interpreting diverse imaging modalities for laryngeal cancer surgery.
Methods: We analyzed 169 images (X-rays, CT scans, laryngoscopy, and pathology findings) from 50 patients using six state-of-the-art MLLMs.
Int J Surg
January 2025
Department of neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China.
Background: Risk factors and mechanisms of cognitive impairment (CI) after aneurysmal subarachnoid hemorrhage (aSAH) are unclear. This study used a neuropsychological battery, MRI, ERP and CSF and plasma biomarkers to predict long-term cognitive impairment after aSAH.
Materials And Methods: 214 patients hospitalized with aSAH (n = 125) or unruptured intracranial aneurysms (UIA) (n = 89) were included in this prospective cohort study.
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