Mayla Dos S Silva, Maria Alzira de A Nunes, Suélia de Siqueira R F Rosa, Antônio Piratelli-Filho
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引用次数: 0
Abstract
Background: Infusion pumps (IPs) are medical devices used for the continuous and precise delivery of medications or nutrients. Their use has expanded and is now widespread in emergency rooms, ICUs, pediatrics, and other hospital departments. Failures in IPs can lead to adverse events, compromising patient health. In addition to the risks to patients, IPs are the medical devices most frequently associated with reports of adverse events in Brazil, highlighting the need to monitor their operational conditions to minimize failures during use.
Results: Thus, the objective of this research is to analyze the reliability of infusion pumps (IPs) in a Brazilian hospital using an internal database from Clinical Engineering software. Probability distributions for repair time and time between failures were modeled, and parameters such as reliability and availability were calculated, with a focus on investigating hospital departments with recurring failures.
Conclusion: In evaluating the operating equipment, a lack of detail in failure notes and service order openings was observed, which can hinder maintenance planning. The longest repair times were recorded in the ICU (Neurology), which houses the majority of IPs. Graphical analysis and testing demonstrated that the Weibull distribution effectively models both time between failures and repair time. The IP A model showed better results in terms of availability and reliability, thereby improving the security of the IPs.
期刊介绍:
BioMedical Engineering OnLine is an open access, peer-reviewed journal that is dedicated to publishing research in all areas of biomedical engineering.
BioMedical Engineering OnLine is aimed at readers and authors throughout the world, with an interest in using tools of the physical and data sciences and techniques in engineering to understand and solve problems in the biological and medical sciences. Topical areas include, but are not limited to:
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Bioinstrumentation-
Biomechanics-
Biomedical Devices & Instrumentation-
Biomedical Signal Processing-
Healthcare Information Systems-
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Neural Engineering-
Rehabilitation Engineering-
Biomaterials-
Biomedical Imaging & Image Processing-
BioMEMS and On-Chip Devices-
Bio-Micro/Nano Technologies-
Biomolecular Engineering-
Biosensors-
Cardiovascular Systems Engineering-
Cellular Engineering-
Clinical Engineering-
Computational Biology-
Drug Delivery Technologies-
Modeling Methodologies-
Nanomaterials and Nanotechnology in Biomedicine-
Respiratory Systems Engineering-
Robotics in Medicine-
Systems and Synthetic Biology-
Systems Biology-
Telemedicine/Smartphone Applications in Medicine-
Therapeutic Systems, Devices and Technologies-
Tissue Engineering