Antibiotic Resistance in Nursing Homes

Ricci, Giorgio; Barrionuevo, Lucia Maria; Cosso, Paola; Pagliari, Patrizia; Ianes, Aladar

doi:10.5772/29448

Cited by 1 publication

(1 citation statement)

References 406 publications

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“…Viruses such as influenza, SARS-CoV-2, and norovirus can persist on surfaces for hours to days 5,6 . The cross-transmission of pathogens in nursing homes results in an uncontrollable occurrence of clinical cases within a few days to months 7 . In a systemic review, environmental cleaning and decontamination were correlated with a remarkable reduction of C. difficile in healthcare settings 8 .…”

Section: Introductionmentioning

confidence: 99%

Cleanliness assessment in long-term care facilities using deep learning and multiwavelength fluorescence imaging

Husarik

Gorji

Qin

et al. 2023

Sensing for Agriculture and Food Quality and Safety XV

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Protecting elders in long-term care facilities (LTCFs) from foodborne illnesses, such as norovirus, listeria, salmonella, and E. coli, is critical. Sanitation inspection is an ongoing concern for LTCF kitchens and dining facilities and staff who handle and serve food. LTCFs must prevent food contamination but must also deal with the potential spread of infection among workers and customers. By 2050 the number of Americans needing LTCFs is expected to double. The Centers for Disease Control and Prevention (CDC) reports that 1 to 3 million serious infections occur annually in nursing homes and assisted living. LTCF sanitization can benefit from standardized tools such as checklists and frequent staff education, including specific product use training. Visual inspection is the most common evaluation method for cleanliness after cleaning but is non-objective and less accurate. Swab-based adenosine triphosphate (ATP) bioluminescence assays are objective for evaluating the quality of cleaning in LTCFs. While more accurate than visual assessment, it requires additional swab and analysis time. We present a fast and easy-to-use handheld fluorescence imaging system for infection prevention in LTCFs. It detects invisible contamination, provides immediate UVC deactivation of potential threats (i.e., bacteria, viruses), and documentation for traceable evidence of cleanliness. We have developed an algorithm to detect organic residue contamination found in images of high-touch surfaces. We provide fluorescence imaging optimization of camera parameters to improve the machine-learning results of different surfaces in LTCFs that were measured, analyzed, and recorded. This information can improve cleaning procedures and educate and train staff.

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