Smart technologies for infection control and antimicrobial stewardship in critical care settings: A narrative review

Overview of digital platforms in ICU infection control

Predictive analytics powered by AI and ML might be considered the system's "brain," real-time data collection through the IoT its "spinal cord," and remote expertise and intervention delivery through telemedicine and digital platforms its "hands, legs, eyes, ears, and voice." The research that is now available suggests that these applications of smart technologies may be loosely divided into three functional domains, which correlate to the activity and flow of information in clinical settings. [5, 8]. AI and ML for prediction and surveillance, to effectively evaluate large amounts of diverse data. This discipline uses cutting-edge methods that significantly outperform human analysis in areas such as genetic data, diagnostic results, clinical records, and EHR data [5]. AI can identify the pathogen causing the infection, predict its resistance profile, and recommend the most effective antimicrobial treatment by precisely recognising patterns and correlations that predict the likelihood of an upcoming infection such as sepsis or a healthcare-associated infection (HAI) [4]. These approaches aid in slowing the development of AMR by emphasising precise prediction over conjecture. As a result, doctors will be able to treat patients faster, select antibiotics from a smaller range, and ultimately enhance patient outcomes [8].

For real-time data collection, ML and AI models in the second category rely on the IoT's primary data layer. Since the precision and dependability of prediction models are dependent on the timeliness, quality, and accuracy of the input data, IoT has radically changed patient monitoring procedures in this industry. This category includes wearable sensors that continuously monitor vital indicators, including body temperature, heart rate, and breathing rate, smart medical equipment, and environmental sensors that monitor catheter use and hand hygiene compliance [6]. This continuous flow of high-resolution data, as opposed to the usual intermittent manual data collection, enables the identification of particular physiological changes that may be an early indicator of infection, sometimes before clinical symptoms manifest. The effectiveness of early warning systems and the dynamic inputs needed for updated adaptive treatment models depend on this kind of real-time situational awareness [9].

Role of AI and ML in ICU infection control

Critical care has greatly benefited from AI and ML, especially in the areas of disease prognostics and diagnostic applications. Two of the most pressing infection control concerns that this analytical capability is being used to address are the early detection of sepsis and the identification of particular drug-resistant bacteria. Numerous studies suggest that, with the correct training and assessment, AI models might be a beneficial tool for strengthening clinical judgment, surpassing manual monitoring methods and standard scoring systems [4].

The ability of these models to simultaneously incorporate several high-dimensional and heterogeneous data sources is a key component of their effectiveness. Table 1 shows which AI systems are suitable for which types of functions. For instance, time-series data from EHRs may be analysed using deep learning models such as Long Short-Term Memory (LSTM) networks, which are very effective at predicting the onset of illnesses like sepsis or ventilator-associated pneumonia (VAP) [4]. Deep learning models, such as networks with LSTM, can be used to analyse time-series data from EHRs. In a specific ICU setting, supervised machine-learning models such as gradient-boosted trees have demonstrated high predictive performance for early sepsis detection, achieving an area under the curve of up to 0.94 in single-centre retrospective validation studies, although such performance is not uniform across models or clinical contexts  [5]. By predicting the formation of multidrug-resistant organisms (MDROs) and guiding the proper antibiotic selection, AI has also demonstrated a substantial contribution to enhancing AMS and preserving the effectiveness of last-line treatments [12]. Even before definitive culture findings are received, AI-based empirical treatment has a greater connection with the expected pathogen and its susceptibility profile [13].

Table 1 Artificial intelligence/ machine learning models for infection detection and prediction in intensive care unit settings

The AI/ML models have demonstrated accurate prediction of specific infection-related outcomes—particularly ICU-acquired sepsis, VAP, central line–associated bloodstream infection, surgical site infections, and urinary tract infections—under defined clinical settings, achieving area under the receiver operating characteristic curve values ranging from ~0.80 to 0.94 and enabling early detection up to 4–12 hours before clinical onset in validated ICU and hospital cohorts. This achievement is therefore tempered by other notable and enduring barriers that hinder widespread clinical use. Nonetheless, generalizability and validation are the two most crucial issues. Since many of the high-performing models described in the literature were developed and validated using retrospective data, it is unclear if they will perform as well in other clinical settings with different patient populations and data infrastructures. Another closely related issue is the "black box" nature of many complex models, particularly deep learning networks. Since doctors could be hesitant to accept a diagnosis if they are unsure of its source, explainable active learning (XAI) models were created to promote model transparency and boost user confidence. In conclusion, the effective integration of these technologies entails both technical and nontechnical issues, necessitating significant financial assistance for clinician training, data infrastructure, and a seamless transition into a modern, smart, technology-driven clinical practice.

Role of IoT in ICU infection

The IoT is the most efficient means of managing the constant flow of high-quality, real-time data required for the potent prediction abilities of AI models. An important change from short-term, human data collection to ongoing, automated monitoring is represented by the usage of IoT in critical care. By collecting and transmitting a vast array of physiological and environmental data, this technology layer serves as the peripheral nervous system, offering an unparalleled level of insight into a patient's condition. This continuous flow of data makes it possible to create more dynamic and individualised patient care models and is essential for the early detection of clinical deterioration [6].

Table 2 illustrates the diverse range of IoT applications in critical care that are continuously expanding. Vital indicators, such as temperature, oxygen saturation, heart rate, and breathing rate, are continuously monitored at the patient level using wearable and reasonably priced sensors. This enables the detection of even the slightest alterations that could be the initial indication of a major disease [6]. Therefore, acute care facilities may benefit in the future from adapting approaches derived from traditional remote patient monitoring, which has historically been implemented in out-of-hospital settings . The IoT encompasses both the patient and the medical environment. Smart hospital beds, automatic hand hygiene equipment, and intelligent infusion pumps can all keep an eye on adherence to infection control protocols, and a secure network architecture guarantees that information is transmitted securely [9]. The vast, accurate, and real-time data that this network of networked devices delivers is essential for future AI-driven infection control [6].

Table 2 Internet of things-based innovations for infection monitoring and antibiotic optimisation in the intensive care unit

The IoT integration in critical care enables active, data-rich infection management. The main outcome is the shift from sporadic to ongoing surveillance, which enables the detection of patient decline well in advance of its clinical manifestation. The initial instances of predictive AI are fueled by this constant flow of data. The two main new issues brought on by the growing number of connected devices are data security and privacy. Implementing secure protocols and robust authentication is essential to safeguarding vital patient data throughout collection, transmission, and storage, since every sensor is a potential point of attack. The second major challenge, however, is interoperability and data integration. For data from devices manufactured by many manufacturers to be meaningful, it must be standardised and connectable. In addition to technology standards, new data architectures (such as edge computing)  are needed to handle the massive amount of data without causing delays or problems. Finally, human elements such as process integration and user permission are just as important for effectiveness as AI.

Telemedicine and digital stewardship platforms

The network of telemedicine and digital stewardship platforms is what turns the promises of AI and IoT into reality in clinical practice, even though they provide real-time data and analytical insights, respectively. These technologies, which serve as the foundational layer of communication and action, ensure that specialised information is available when and where it is needed, and that regular practitioners receive recommendations based on data. This is especially crucial in AMS, when prompt and informed decisions are required to maintain antibiotic potency and optimise treatment. The study indicates that these digital platforms are developing from useful instruments to crucial infrastructure for stewardship initiatives that are efficient, scalable, and fair [7, 14, 15, 16].

The primary ways that digital platforms are altering AMS are listed in Table 3. First, hospitals and intensive care units without staff or specialists can benefit from telemedicine and e-consultation services. These services are an excellent way to improve access to information about AMS and infectious diseases (ID), according to published literature. In areas with limited resources, particularly in rural areas, this approach proves to be beneficial [7, 14, 15, 16]. Local practitioners gain from the democratisation of information, which also encourages a commitment to best practices. Additionally, more automated monitoring methods are required for contemporary stewardship projects. These systems can automatically track antimicrobial consumption indicators like days of treatment (DoT) or defined daily doses (DDD), identify patients who are taking too little or the incorrect kind of broad-spectrum medication, and notify the AMS team of possible intervention opportunities by continuously monitoring EHR and pharmacy data [11]. Automating the laborious case-finding process allows stewardship teams to focus their efforts more efficiently [10]. Finally, by sharing ideas and providing feedback, these platforms which frequently have integrated CDS in the EHR support the core stewardship duties of prospective audit. It has frequently been shown that this makes drugs more appropriate [3, 10].