Innovative Techniques for Infection Control and Surveillance in Hospital Settings and Long-Term Care Facilities: A Scoping Review

Arzilli, Guglielmo; De Vita, Erica; Pasquale, Milena; Carloni, Luca Marcello; Pellegrini, Marzia; Di Giacomo, Martina; Esposito, Enrica; Porretta, Andrea Davide; Rizzo, Caterina

doi:10.3390/antibiotics13010077

Cited by 7 publications

(1 citation statement)

References 56 publications

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“…A number of recent reviews and studies already showcase first solutions and promising results utilising AI or ML [ 19 , 21 – 23 ]. However, some authors are concluding that currently most studies concerning this issue are still lacking real world-performance metrics and proper validation [ 4 , 22 ] and that AI and ML in infection control are currently still in their infancy.…”

Section: Discussion and Future Implicationsmentioning

confidence: 99%

A qualitative, multi-centre approach to the current state of digitalisation and automation of surveillance in infection prevention and control in German hospitals

Eisenmann,

Spreckelsen,

Rauschenberger

et al. 2024

Antimicrob Resist Infect Control

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Background Healthcare associated infections (HAI) pose a major threat to healthcare systems resulting in an increased burden of disease. Surveillance plays a key role in rapidly identifying these infections and preventing further transmissions. Alas, in German hospitals, the majority of surveillance efforts have been heavily relying on labour intensive processes like manual chart review. In order to be able to identify further starting points for future digital tools and interventions to aid the surveillance of HAI we aimed to gain an understanding of the current state of digitalisation in the context of the general surveillance organisation in German clinics across all care-levels. The end user perspective of infection prevention and control (IPC) professionals was chosen to identify digital interventions that have the biggest impact on the daily surveillance work routines of IPC professionals. Perceived impediments in the advancement of surveillance digitalisation should be explored. Methods Following the development of an interview guideline, eight IPC professionals from seven German hospitals of different care levels were questioned in semi- structured interviews between December 2022 and January 2023. These included questions about general surveillance organisation, access to digital data sources, software to aid the surveillance process as well as current issues in the surveillance process and implementation of software systems. Subsequently, after full transcription, the interview sections were categorized in code categories (first deductive then inductive coding) and analysed qualitatively. Results Results were characterised by high heterogeneity in terms of general surveillance organisation and access to digital data sources. Software configuration of hospital and laboratory information systems (HIS/LIS) as well as patient data management systems (PDMS) varied not only between hospitals of different care levels but also between hospitals of the same care level. Outside research projects, neither fully automatic software nor solutions utilising artificial intelligence have currently been implemented in clinical routine in any of the hospitals. Conclusions Access to digital data sources and software is increasingly available to aid surveillance of HAI. Nevertheless, surveillance processes in hospitals analysed in this study still heavily rely on manual processes. In the analysed hospitals, there is an implementation and funding gap of (semi-) automatic surveillance solutions in clinical practice, especially in healthcare facilities of lower care levels.

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Section: Discussion and Future Implicationsmentioning

confidence: 99%

A qualitative, multi-centre approach to the current state of digitalisation and automation of surveillance in infection prevention and control in German hospitals

Eisenmann,

Spreckelsen,

Rauschenberger

et al. 2024

Antimicrob Resist Infect Control

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Development and evaluation of a model for predicting the risk of healthcare-associated infections in patients admitted to intensive care units

Wang,

Wang

et al. 2024

Front. Public Health

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This retrospective study used 10 machine learning algorithms to predict the risk of healthcare-associated infections (HAIs) in patients admitted to intensive care units (ICUs). A total of 2,517 patients treated in the ICU of a tertiary hospital in China from January 2019 to December 2023 were included, of whom 455 (18.1%) developed an HAI. Data on 32 potential risk factors for infection were considered, of which 18 factors that were statistically significant on single-factor analysis were used to develop a machine learning prediction model using the synthetic minority oversampling technique (SMOTE). The main HAIs were respiratory tract infections (28.7%) and ventilator-associated pneumonia (25.0%), and were predominantly caused by gram-negative bacteria (78.8%). The CatBoost model showed good predictive performance (area under the curve: 0.944, and sensitivity 0.872). The 10 most important predictors of HAIs in this model were the Penetration Aspiration Scale score, Braden score, high total bilirubin level, female, high white blood cell count, Caprini Risk Score, Nutritional Risk Screening 2002 score, low eosinophil count, medium white blood cell count, and the Glasgow Coma Scale score. The CatBoost model accurately predicted the occurrence of HAIs and could be used in clinical practice.

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Preventing and Controlling Healthcare-Associated Infections: The First Principle of Every Antimicrobial Stewardship Program in Hospital Settings

Sartelli,

Marini,

McNelis

et al. 2024

Antibiotics

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Antimicrobial resistance (AMR) is one of the main public health global burdens of the 21st century, responsible for over a million deaths every year. Hospital programs aimed at improving antibiotic use, referred to as antimicrobial stewardship programs (ASPs), can both optimize the treatment of infections and minimize adverse antibiotics events including the development and spread of AMR. The challenge of AMR is closely linked to the development and spread of healthcare-associated infection (HAIs). In fact, the management of patients with HAIs frequently requires the administration of broader-spectrum antibiotic regimens due to the higher risk of acquiring multidrug-resistant organisms, which, in turn, promotes resistance. For this reason, even before using antibiotics correctly, it is necessary to prevent and control the spread of HAIs in our hospitals. In this narrative review, we present seven measures that healthcare workers, even if not directly involved in the tasks of infection prevention and control, must know, support, and embrace. We hope that this review may raise awareness among all healthcare professionals about the issues with the increasing rate of AMR and the ongoing efforts towards minimizing its rise.

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Innovative Techniques for Infection Control and Surveillance in Hospital Settings and Long-Term Care Facilities: A Scoping Review

Cited by 7 publications

References 56 publications

A qualitative, multi-centre approach to the current state of digitalisation and automation of surveillance in infection prevention and control in German hospitals

A qualitative, multi-centre approach to the current state of digitalisation and automation of surveillance in infection prevention and control in German hospitals

Development and evaluation of a model for predicting the risk of healthcare-associated infections in patients admitted to intensive care units

Preventing and Controlling Healthcare-Associated Infections: The First Principle of Every Antimicrobial Stewardship Program in Hospital Settings

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