Lorenzo Blandi, Vittorio Bolcato, Alessandro Meloni, Daniele Bosone, Anna Odone
{"title":"Healthcare-Associated-Infections: preliminary results from a real-time reporting system of an Italian neurologic research hospital.","authors":"Lorenzo Blandi, Vittorio Bolcato, Alessandro Meloni, Daniele Bosone, Anna Odone","doi":"10.7416/ai.2024.2603","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Healthcare-Associated-Infections are a critical concern in healthcare settings, posing serious threats to patient safety and causing significant morbidity, mortality, and financial strain. This study aims to calculate healthcare-associated-infections trends in the hospital setting through an automatic reporting system.</p><p><strong>Study design: </strong>The study is a descriptive analysis of automatically generated trends of an innovative digital tool based on existing hospital information flows.</p><p><strong>Methods: </strong>An algorithm was developed within a Clinical Information System to create a suite of quality indicators for monitoring healthcare-associated-infections trends. The algorithm used criteria related to admission, laboratory tests and antimicrobial administrations. A descriptive analysis was conducted for patients aged 18 or older, admitted to a neurological or to a neuro-rehabilitation department of a neurologic hospital from 2019 to 2022.</p><p><strong>Results: </strong>The results showed fluctuations in healthcare-associated-infections prevalence from 2.9% to 5.6% and hospital infec-tions prevalence from 4.5% to 10.9%, with notable increases in 2020 and 2021. The majority (70.3%) of healthcare associated infections identified by the tool were confirmed to be potentially hospital-acquired, according to the European Centre of Disease Prevention and Control's definition.</p><p><strong>Discussion and conclusions: </strong>The study posits the algorithm as a vital tool for automatically monitoring hospital infections, providing valuable preliminary results for improving care quality and guiding the infections' prevention and control strategies, with plans to benchmark the algorithm against a gold standard in the future.</p>","PeriodicalId":7999,"journal":{"name":"Annali di igiene : medicina preventiva e di comunita","volume":" ","pages":"256-260"},"PeriodicalIF":1.5000,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Annali di igiene : medicina preventiva e di comunita","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.7416/ai.2024.2603","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/1/18 0:00:00","PubModel":"Epub","JCR":"Q3","JCRName":"HEALTH CARE SCIENCES & SERVICES","Score":null,"Total":0}
引用次数: 0
Abstract
Background: Healthcare-Associated-Infections are a critical concern in healthcare settings, posing serious threats to patient safety and causing significant morbidity, mortality, and financial strain. This study aims to calculate healthcare-associated-infections trends in the hospital setting through an automatic reporting system.
Study design: The study is a descriptive analysis of automatically generated trends of an innovative digital tool based on existing hospital information flows.
Methods: An algorithm was developed within a Clinical Information System to create a suite of quality indicators for monitoring healthcare-associated-infections trends. The algorithm used criteria related to admission, laboratory tests and antimicrobial administrations. A descriptive analysis was conducted for patients aged 18 or older, admitted to a neurological or to a neuro-rehabilitation department of a neurologic hospital from 2019 to 2022.
Results: The results showed fluctuations in healthcare-associated-infections prevalence from 2.9% to 5.6% and hospital infec-tions prevalence from 4.5% to 10.9%, with notable increases in 2020 and 2021. The majority (70.3%) of healthcare associated infections identified by the tool were confirmed to be potentially hospital-acquired, according to the European Centre of Disease Prevention and Control's definition.
Discussion and conclusions: The study posits the algorithm as a vital tool for automatically monitoring hospital infections, providing valuable preliminary results for improving care quality and guiding the infections' prevention and control strategies, with plans to benchmark the algorithm against a gold standard in the future.