Predictive Analysis of Healthcare-Associated Blood Stream Infections in the Neonatal Intensive Care Unit Using Artificial Intelligence: A Single Center Study

Are AI-based surveillance systems for healthcare-associated infections ready for clinical practice? A systematic review and meta-analysis

Healthcare-associated infections (HAIs) are a global public health concern, imposing significant clinical and financial burdens. Despite advancements, surveillance methods remain largely manual and resource-intensive, often leading to underreporting. In this context, automation, particularly through Artificial Intelligence (AI), shows promise in optimizing clinical workflows. However, adoption challenges persist. This study aims to evaluate the current performance and impact of AI in HAI surveillance, considering technical, clinical, and implementation aspects. We conducted a systematic review of Scopus and Embase databases following PRISMA guidelines. AI-based models' performances, accuracy, AUC, sensitivity, and specificity, were pooled using a random-effect model, stratifying by detected HAI type. Our study protocol was registered in PROSPERO (CRD42024524497). Of 2834 identified citations, 249 studies were reviewed. The performances of AI models were generally high but with significant heterogeneity between HAI types. Overall pooled sensitivity, specificity, AUC, and accuracy were respectively 0.835, 0.899, 0.864, and 0.880. About 35.7 % of studies compared AI system performance with alternative automated or standard-of-care surveillance methods, with most achieving better or comparable results to clinical scores or manual surveillance. <7.6 % explicitly measured AI impact in terms of improved patient outcomes, workload reduction, and cost savings, with the majority finding benefits. Only 30 studies deployed the model in a user-friendly tool, and 9 tested it in real clinical practice. In this systematic review, AI shows promising performance in HAI surveillance, although its routine application in clinical practice remains uncommon. Despite over a decade, retrieved studies offer scant evidence on reducing burden, costs, and resource use. This prevents their potential superiority over traditional or simpler automated surveillance systems from being fully evaluated. Further research is necessary to assess impact, enhance interpretability, and ensure reproducibility.

Advancing Patient Safety: The Future of Artificial Intelligence in Mitigating Healthcare-Associated Infections: A Systematic Review

BACKGROUND

Healthcare-associated infections are infections that patients acquire during hospitalization or while receiving healthcare in other facilities. They represent the most frequent negative outcome in healthcare, can be entirely prevented, and pose a burden in terms of financial and human costs. With the development of new AI and ML algorithms, hospitals could develop new and automated surveillance and prevention models for HAIs, leading to improved patient safety. The aim of this review is to systematically retrieve, collect, and summarize all available information on the application and impact of AI in HAI surveillance and/or prevention.

METHODS

We conducted a systematic review of the literature using PubMed and Scopus to find articles related to the implementation of artificial intelligence in the surveillance and/or prevention of HAIs.

RESULTS

We identified a total of 218 articles, of which only 35 were included in the review. Most studies were conducted in the US (n = 10, 28.6%) and China (n = 5; 14.3%) and were published between 2021 and 2023 (26 articles, 74.3%) with an increasing trend over time. Most focused on the development of ML algorithms for the identification/prevention of surgical site infections (n = 18; 51%), followed by HAIs in general (n = 9; 26%), hospital-acquired urinary tract infections (n = 5; 9%), and healthcare-associated pneumonia (n = 3; 9%). Only one study focused on the proper use of personal protective equipment (PPE) and included healthcare workers as the study population. Overall, the trend indicates that several AI/ML models can effectively assist clinicians in everyday decisions, by identifying HAIs early or preventing them through personalized risk factors with good performance. However, only a few studies have reported an actual implementation of these models, which proved highly successful. In one case, manual workload was reduced by nearly 85%, while another study observed a decrease in the local hospital's HAI incidence from 1.31% to 0.58%.

CONCLUSIONS

AI has significant potential to improve the prevention, diagnosis, and management of healthcare-associated infections, offering benefits such as increased accuracy, reduced workloads, and cost savings. Although some AI applications have already been tested and validated, adoption in healthcare is hindered by barriers such as high implementation costs, technological limitations, and resistance from healthcare workers. Overcoming these challenges could allow AI to be more widely and cost-effectively integrated, ultimately improving patient care and infection management.

The premise, promise, and perils of artificial intelligence in critical care cardiology

Artificial intelligence (AI) is an emerging technology with numerous healthcare applications. AI could prove particularly useful in the cardiac intensive care unit (CICU) where its capacity to analyze large datasets in real-time would assist clinicians in making more informed decisions. This systematic review aimed to explore current research on AI as it pertains to the CICU. A PRISMA search strategy was carried out to identify the pertinent literature on topics including vascular access, heart failure care, circulatory support, cardiogenic shock, ultrasound, and mechanical ventilation. Thirty-eight studies were included. Although AI is still in its early stages of development, this review illustrates its potential to yield numerous benefits in the CICU.

Diagnostic Performance of Machine Learning-based Models in Neonatal Sepsis: A Systematic Review

BACKGROUND

Timely diagnosis of neonatal sepsis is challenging. We aimed to systematically evaluate the diagnostic performance of sophisticated machine learning (ML) techniques for the prediction of neonatal sepsis.

METHODS

We searched MEDLINE, Embase, Web of Science and Cochrane CENTRAL databases using "neonate," "sepsis" and "machine learning" as search terms. We included studies that developed or validated an ML algorithm to predict neonatal sepsis. Those incorporating automated vital-sign data were excluded. Among 5008 records, 74 full-text articles were screened. Two reviewers extracted information as per the CHARMS (CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies) checklist. We followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) guideline extension for diagnostic test accuracy reviews and used the PROBAST tool for risk of bias assessment. Primary outcome was a predictive performance of ML models in terms of sensitivity, specificity and positive and negative predictive values. We generated a hierarchical summary receiver operating characteristics curve for pooled analysis.

RESULTS

Of 19 studies (15,984 participants) with 76 ML models, the random forest algorithm was the most employed. The candidate predictors per model ranged from 5 to 93; most included birth weight and gestation. None performed external validation. The risk of bias was high (18 studies). For the prediction of any sepsis (14 studies), pooled sensitivity was 0.87 (95% credible interval: 0.75-0.94) and specificity was 0.89 (95% credible interval: 0.77-0.95). Pooled area under the receiver operating characteristics curve was 0.94 (95% credible interval: 0.92-0.96). All studies, except one, used data from high- or upper-middle-income countries. With unavailable probability thresholds, the performance could not be assessed with sufficient precision.

CONCLUSIONS

ML techniques have good diagnostic accuracy for neonatal sepsis. The need for the development of context-specific models from high-burden countries is highlighted.

Investigation of emergency department abandonment rates using machine learning algorithms in a single centre study

A critical problem that Emergency Departments (EDs) must address is overcrowding, as it causes extended waiting times and increased patient dissatisfaction, both of which are immediately linked to a greater number of patients who leave the ED early, without any evaluation by a healthcare provider (Leave Without Being Seen, LWBS). This has an impact on the hospital in terms of missing income from lost opportunities to offer treatment and, in general, of negative outcomes from the ED process. Consequently, healthcare managers must be able to forecast and control patients who leave the ED without being evaluated in advance. This study is a retrospective analysis of patients registered at the ED of the "San Giovanni di Dio e Ruggi d'Aragona" University Hospital of Salerno (Italy) during the years 2014-2021. The goal was firstly to analyze factors that lead to patients abandoning the ED without being examined, taking into account the features related to patient characteristics such as age, gender, arrival mode, triage color, day of week of arrival, time of arrival, waiting time for take-over and year. These factors were used as process measures to perform a correlation analysis with the LWBS status. Then, Machine Learning (ML) techniques are exploited to develop and compare several LWBS prediction algorithms, with the purpose of providing a useful support model for the administration and management of EDs in the healthcare institutions. During the examined period, 688,870 patients were registered and 39188 (5.68%) left without being seen. Of the total LWBS patients, 59.6% were male and 40.4% were female. Moreover, from the statistical analysis emerged that the parameter that most influence the abandonment rate is the waiting time for take-over. The final ML classification model achieved an Area Under the Curve (AUC) of 0.97, indicating high performance in estimating LWBS for the years considered in this study. Various patient and ED process characteristics are related to patients who LWBS. The possibility of predicting LWBS rates in advance could be a valid tool quickly identifying and addressing "bottlenecks" in the hospital organization, thereby improving efficiency.

Evaluation of Comfort Behavior Levels of Newborn by Artificial Intelligence Techniques

BACKGROUND

One of the scales most frequently used in the evaluation of newborn comfort levels is the Neonatal Comfort Behavior Scale (NCBS). It is important therefore that an increased use of the NCBS is encouraged through a more practical method of assessment.

OBJECTIVE

This study was carried out for the purpose of designing a means of assessing neonatal comfort levels by employing the techniques of artificial intelligence (AI).

METHODS

The AI-based study was conducted with 362 newborns under treatment in the neonatal intensive care unit of a hospital. A data collection form, the NCBS, and a camera system were used as data collection tools. The data were analyzed with the SPSS Statistics 21.0 program. Descriptive statistics and Cohen κ test were employed in the analysis.

RESULTS

The 2 researchers named in the study first labeled the audiovisual recordings of the 362 newborns in the study. These labeled audiovisual recordings were used in training (80%) as well as testing (20%) the AI model. The AI model displayed a rate of success of 99.82%.

CONCLUSION

It was ultimately seen that the AI model that had been developed was a successful tool that could be used to determine the comfort behavior levels of newborns in the neonatal intensive care unit.

Assisting the infection preventionist: Use of artificial intelligence for health care-associated infection surveillance

BACKGROUND

Health care-associated infection (HAI) surveillance is vital for safety in health care settings. It helps identify infection risk factors, enhancing patient safety and quality improvement. However, HAI surveillance is complex, demanding specialized knowledge and resources. This study investigates the use of artificial intelligence (AI), particularly generative large language models, to improve HAI surveillance.

METHODS

We assessed 2 AI agents, OpenAI's chatGPT plus (GPT-4) and a Mixtral 8×7b-based local model, for their ability to identify Central Line-Associated Bloodstream Infection (CLABSI) and Catheter-Associated Urinary Tract Infection (CAUTI) from 6 National Health Care Safety Network training scenarios. The complexity of these scenarios was analyzed, and responses were matched against expert opinions.

RESULTS

Both AI models accurately identified CLABSI and CAUTI in all scenarios when given clear prompts. Challenges appeared with ambiguous prompts including Arabic numeral dates, abbreviations, and special characters, causing occasional inaccuracies in repeated tests.

DISCUSSION

The study demonstrates AI's potential in accurately identifying HAIs like CLABSI and CAUTI. Clear, specific prompts are crucial for reliable AI responses, highlighting the need for human oversight in AI-assisted HAI surveillance.

CONCLUSIONS

AI shows promise in enhancing HAI surveillance, potentially streamlining tasks, and freeing health care staff for patient-focused activities. Effective AI use requires user education and ongoing AI model refinement.

Combining simulation models and machine learning in healthcare management: strategies and applications

Simulation models and artificial intelligence (AI) are largely used to address healthcare and biomedical engineering problems. Both approaches showed promising results in the analysis and optimization of healthcare processes. Therefore, the combination of simulation models and AI could provide a strategy to further boost the quality of health services. In this work, a systematic review of studies applying a hybrid simulation models and AI approach to address healthcare management challenges was carried out. Scopus, Web of Science, and PubMed databases were screened by independent reviewers. The main strategies to combine simulation and AI as well as the major healthcare application scenarios were identified and discussed. Moreover, tools and algorithms to implement the proposed approaches were described. Results showed that machine learning appears to be the most employed AI strategy in combination with simulation models, which mainly rely on agent-based and discrete-event systems. The scarcity and heterogeneity of the included studies suggested that a standardized framework to implement hybrid machine learning-simulation approaches in healthcare management is yet to be defined. Future efforts should aim to use these approaches to design novel intelligentin-silicomodels of healthcare processes and to provide effective translation to the clinics.

Advancing nursing practice with artificial intelligence: Enhancing preparedness for the future

AIM

This article aimed to explore the role of AI in advancing nursing practice, focusing on its impact on readiness for the future.

DESIGN AND METHODS

A position paper, the methodology comprises three key steps. First, a comprehensive literature search using specific keywords in reputable databases was conducted to gather current information on AI in nursing. Second, data extraction and synthesis from selected articles were performed. Finally, a thematic analysis identifies recurring themes to provide insights into AI's impact on future nursing practice.

RESULTS

The findings highlight the transformative role of AI in advancing nursing practice and preparing nurses for the future, including enhancing nursing practice with AI, preparing nurses for the future (AI education and training) and associated, ethical considerations and challenges. AI-enabled robotics and telehealth solutions expand the reach of nursing care, improving accessibility of healthcare services and remote monitoring capabilities of patients' health conditions.

Human microbiome and microbiota identification for preventing and controlling healthcare-associated infections: A systematic review

Objective

This systematic review describes the role of the human microbiome and microbiota in healthcare-associated infections (HAIs). Studies on the microbiota of patients, healthcare environment (HE), medical equipment, or healthcare workers (HCW) and how it could be transmitted among the different subjects will be described in order to define alarming risk factors for HAIs spreading and to identify strategies for HAIs control or prevention.

Methods

This review was performed in adherence to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. After retrieval in databases, identification, and screening of available records, 36 published studies were considered eligible and included in the review.

Results

A multifaceted approach is required and the analyses of the many factors related to human microbiota, which can influence HAIs onset, could be of paramount importance in their prevention and control. In this review, we will focus mainly on the localization, transmission, and prevention of ESKAPE (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter species) bacteria and Clostridium difficile which are the most common pathogens causing HAIs.

Conclusions

Healthcare workers' microbiota, patient's microbiota, environmental and medical equipment microbiota, ecosystem characteristics, ways of transmission, cleaning strategies, and the microbial resistome should be taken into account for future studies on more effective preventive and therapeutic strategies against HAIs.

A Semi-Supervised Machine Learning Approach in Predicting High-Risk Pregnancies in the Philippines

Early risk tagging is crucial in maternal health, especially because it threatens both the mother and the long-term development of the baby. By tagging high-risk pregnancies, mothers would be given extra care before, during, and after pregnancies, thus reducing the risk of complications. In the Philippines, where the fertility rate is high, especially among the youth, awareness of risks can significantly contribute to the overall outcome of the pregnancy and, to an extent, the Maternal mortality rate. Although supervised machine learning models have ubiquity as predictors, there is a gap when data are weak or scarce. Using limited collected data from the municipality of Daraga in Albay, the study first compared multiple supervised machine learning algorithms to analyze and accurately predict high-risk pregnancies. Through hyperparameter tuning, supervised learning algorithms such as Decision Tree, Random Forest, Support Vector Machine, K-Nearest Neighbors, Naïve Bayes, and Multilayer Perceptron were evaluated by using 10-fold cross validation to obtain the best parameters with the best scores. The results show that Decision Tree bested other algorithms and attained a test score of 93.70%. To address the gap, a semi-supervised approach using a Self-Training model was applied to the modified Decision Tree, which was then used as the base estimator with a 30% unlabeled dataset and achieved a 97.01% accuracy rate which outweighs similar studies.

Antepartum Antibiotic Therapy under 34 Weeks of Gestation and Its Impact on Early-Onset Neonatal Infection and Maternal Vaginal Microbiota

The use of prenatal antibiotics should be carefully considered, owing to their potential adverse effects on neonatal outcomes. This study aimed to identify the contributing factors to early-onset neonatal infection and to determine the influence of antepartum antibiotics on women and neonates. This study included 127 pregnant women without obvious intra-amniotic infection on admission, who delivered under 34 weeks of gestation. Information on maternal and neonatal characteristics was obtained from their medical charts. Vaginal swabs were taken from all women on admission. In total, 29 (22.8%) neonates developed early-onset infection. Multivariate analysis revealed that antepartum antibiotics were the most strongly associated factor for early-onset neonatal infection (odds ratio, 11.2; 95% confidence interval, 4.08–31.02). The frequency of early-onset neonatal infection was significantly higher in women who received antibiotic therapy than in those who did not; no significant difference in prolonging their gestation or neonatal morbidities was observed. The prevalence of women who hosted vaginal microorganisms on admission was similar to that in women whose infants subsequently developed early-onset neonatal infection compared with that of women whose infants did not. Among infants of the 40 women who received antepartum antibiotic therapy, 21 developed early-onset infection. Of the women who delivered these 21 infants, 62% (13/21) showed reduced lactobacilli and 43% (9/21) had resistant bacterial strains in their vaginal microbiota at the time of delivery. The use of antepartum antibiotics is the most strongly associated factor in early-onset neonatal infection; it does not prolong gestation and would change the vaginal environment.

Risk Factors Analysis of Surgical Infection Using Artificial Intelligence: A Single Center Study

Background: Surgical site infections (SSIs) have a major role in the evolution of medical care. Despite centuries of medical progress, the management of surgical infection remains a pressing concern. Nowadays, the SSIs continue to be an important factor able to increase the hospitalization duration, cost, and risk of death, in fact, the SSIs are a leading cause of morbidity and mortality in modern health care. Methods: A study based on statistical test and logistic regression for unveiling the association between SSIs and different risk factors was carried out. Successively, a predictive analysis of SSIs on the basis of risk factors was performed. Results: The obtained data demonstrated that the level of surgery contamination impacts significantly on the infection rate. In addition, data also reveals that the length of postoperative hospital stay increases the rate of surgical infections. Finally, the postoperative length of stay, surgery department and the antibiotic prophylaxis with 2 or more antibiotics are a significant predictor for the development of infection. Conclusions: The data report that the type of surgery department and antibiotic prophylaxis there are a statistically significant predictor of SSIs. Moreover, KNN model better handle the imbalanced dataset (48 infected and 3983 healthy), observing highest accuracy value.