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 2 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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Enhancing risk management in hospitals: leveraging artificial intelligence for improved outcomes

Guerra

2024

Ital J Med

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In hospital settings, effective risk management is critical to ensuring patient safety, regulatory compliance, and operational effectiveness. Conventional approaches to risk assessment and mitigation frequently rely on manual procedures and retroactive analysis, which might not be sufficient to recognize and respond to new risks as they arise. This study examines how artificial intelligence (AI) technologies can improve risk management procedures in healthcare facilities, fortifying patient safety precautions and guidelines while improving the standard of care overall. Hospitals can proactively identify and mitigate risks, optimize resource allocation, and improve clinical outcomes by utilizing AI-driven predictive analytics, natural language processing, and machine learning algorithms. The different applications of AI in risk management are discussed in this paper, along with opportunities, problems, and suggestions for their effective use in hospital settings.

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

Cited by 2 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

Enhancing risk management in hospitals: leveraging artificial intelligence for improved outcomes

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