A Fuzzy Inference System for the Assessment of Indoor Air Quality in an Operating Room to Prevent Surgical Site Infection
Ylenia Colella,
Antonio Saverio Valente,
Lucia Rossano,
Teresa Angela Trunfio,
Antonella Fiorillo and
Giovanni Improta
Additional contact information
Ylenia Colella: Department of Electrical Engineering and Information Technologies, University of Naples “Federico II”, 80125 Naples, Italy
Antonio Saverio Valente: Department of Electrical Engineering and Information Technologies, University of Naples “Federico II”, 80125 Naples, Italy
Lucia Rossano: Department of Electrical Engineering and Information Technologies, University of Naples “Federico II”, 80125 Naples, Italy
Teresa Angela Trunfio: Department of Advanced Biomedical Sciences, University Hospital of Naples “Federico II”, 80131 Naples, Italy
Antonella Fiorillo: Department of Advanced Biomedical Sciences, University Hospital of Naples “Federico II”, 80131 Naples, Italy
Giovanni Improta: Department of Public Health, University of Naples “Federico II”, 80131 Naples, Italy
IJERPH, 2022, vol. 19, issue 6, 1-20
Abstract:
Indoor air quality in hospital operating rooms is of great concern for the prevention of surgical site infections (SSI). A wide range of relevant medical and engineering literature has shown that the reduction in air contamination can be achieved by introducing a more efficient set of controls of HVAC systems and exploiting alarms and monitoring systems that allow having a clear report of the internal air status level. In this paper, an operating room air quality monitoring system based on a fuzzy decision support system has been proposed in order to help hospital staff responsible to guarantee a safe environment. The goal of the work is to reduce the airborne contamination in order to optimize the surgical environment, thus preventing the occurrence of SSI and reducing the related mortality rate. The advantage of FIS is that the evaluation of the air quality is based on easy-to-find input data established on the best combination of parameters and level of alert. Compared to other literature works, the proposed approach based on the FIS has been designed to take into account also the movement of clinicians in the operating room in order to monitor unauthorized paths. The test of the proposed strategy has been executed by exploiting data collected by ad-hoc sensors placed inside a real operating block during the experimental activities of the “Bacterial Infections Post Surgery” Project (BIPS). Results show that the system is capable to return risk values with extreme precision.
Keywords: fuzzy logic; indoor air quality; operating room; surgical site infection (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (2)
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