Neutrosophic method to measure compliance with terminal disinfection in the surgical center of the Delfina Torres de Concha Hospital

Main Article Content

Zuly Rivel Nazate Chuga
Clara Elisa Pozo Hernández
Jesly Vanessa Chamorro Nazate
López Puetate Evelin Daniela

Abstract

The objective of the research was to develop a neutrosophic method to measure compliance with terminal disinfection in the surgical center of the Delfina Torres de Concha hospital. Based on the results obtained with the implementation of the neutrosophic method, it was necessary to develop educational strategies to reduce the risk factors that influence noncompliance with terminal disinfection in the surgical center under study. A mixed-approach methodology was used, which includes qualitative and quantitative methods, and is also cross-sectional with a descriptive scope. In the end, a survey and a checklist were developed that allowed the identification of the main problems in terminal disinfection in the operating room; the results obtained were tabulated using a calculation base and the most relevant data showed that 88% of health personnel rarely receive feedback on the performance of terminal disinfection; 54% consider that communication with cleaning staff makes disinfection compliance difficult; 73% believe that the urgency of performing a subsequent surgery significantly affects the quality of disinfection and finally 63% identify the lack of resources as the main reason for non-compliance with their cleaning and disinfection activities.

Downloads

Download data is not yet available.

Article Details

How to Cite
Neutrosophic method to measure compliance with terminal disinfection in the surgical center of the Delfina Torres de Concha Hospital. (2024). Neutrosophic Computing and Machine Learning. ISSN 2574-1101, 33, 211-221. https://fs.unm.edu/NCML2/index.php/112/article/view/565
Section
Articles

How to Cite

Neutrosophic method to measure compliance with terminal disinfection in the surgical center of the Delfina Torres de Concha Hospital. (2024). Neutrosophic Computing and Machine Learning. ISSN 2574-1101, 33, 211-221. https://fs.unm.edu/NCML2/index.php/112/article/view/565

Most read articles by the same author(s)