Hospital Practices and Research

Abstract Despite remarkable advances in reperfusion strategies, antithrombotic therapy, and secondary prevention, myocardial infarction (MI) remains a leading cause of heart failure and cardiovascular mortality worldwide. Among emerging regenerative approaches, exosome engineering has attracted considerable scientific interest. Exosomes are nano-sized extracellular vesicles secreted by nearly all cell types and are now recognized as highly organized mediators of intercellular communication. By transporting microRNAs, proteins, lipids, and signaling molecules, exosomes influence gene expression and cellular behavior in target tissues. We argue that integrating metabolic modulators with engineered exosome delivery platforms represents not merely an incremental innovation but a paradigm shift in post-MI regenerative therapy.

Abstract Neonatal Intensive Care Units (NICUs) face a persistent challenge in controlling healthcare-associated infections (HAIs), with reported rates ranging from 6% to 25% globally. While traditional infection control strategies have focused on symptomatic patients and high-risk groups, a growing body of evidence suggests that “silent spreaders,” asymptomatic carriers of multidrug-resistant organisms (MDROs) among healthcare workers, parents, and neonates themselves may represent a critical blind spot in current surveillance paradigms. If this is the case, then fundamental assumptions underpinning NICU infection control may warrant reexamination. We argue that existing approaches, which rely heavily on active surveillance cultures (ASCs) and hand hygiene compliance, are structurally ill-suited to detect asymptomatic transmission. Emerging genomic, environmental, and data-driven methodologies offer not merely incremental improvements but a necessary reconceptualization of surveillance itself.

Abstract Background: Artificial Intelligence (AI), as a transformative technology, has found widespread applications in the health and hospital sectors.
Objectives: The present study aimed to analyze scientific articles related to AI in hospitals using text mining methods to identify dominant topics and emerging trends.
Methods: In the present study, text mining and topic modeling approaches were used to analyze research trends and identify dominant topics. The research steps included data collection from Scopus, text preprocessing, extraction of frequent words, topic modeling using Latent Dirichlet Allocation (LDA), and visualization. All steps were performed using the Python programming language and open-source libraries, such as NLTK, Gensim, Matplotlib, scikit-learn, and pyLDAvis.
Results: A total of 2238 records related to AI in hospitals were collected from Scopus since 2000. The terms "patient," "model," "machine learning," and "artificial intelligence" were identified as the most frequently used terms. The dominant topic clusters included "patient monitoring," "data-driven systems," "service innovation and emerging technologies," "clinical outcome prediction," "COVID-19 risk prediction," "mortality and hospitalization prediction," "health tourism," "management and implementation," and "hospital death prediction." Most articles were in the clusters "clinical outcome prediction modeling" (663 documents) and "mortality and hospitalization prediction" (335 documents). The publication trend has accelerated significantly since 2018, especially in the clusters "clinical outcome prediction" and "management and implementation."
Conclusion: Conclusion: Artificial intelligence in hospitals has grown rapidly over the last two decades. The shift from limited applications in modeling and prediction to interdisciplinary areas and innovative services indicates the gradual growth of this technology and its role in improving the quality of care, optimizing organizational processes, and developing new services.

Abstract Background: Hospital-acquired infections (HAIs), particularly device-associated infections (DAIs) in ICUs, pose a significant global health burden, especially in low- and middle-income countries.
Objectives: This study aims to estimate the HAI burden by analyzing DAI rates and resistance patterns in ICU settings, contributing to evidence-based infection control strategies.
Methods: We conducted a six-month prospective observational study in the adult ICU of GTBH (July–December 2024), focusing on patients with DAIs. Incidence and device utilization rates were calculated using CDC-standardized metrics based on device and patient days.
Results: Out of 286 patients, 40 developed a total of 62 DAIs, comprising 25 cases of ventilator-associated pneumonia (VAP) (40.3%), 19 cases of catheter-related bloodstream infections (CLABSI) (30.65%), and 18 cases of catheter-associated urinary tract infections (CAUTI) (29.03%). VAP had the highest cumulative infection rate (22.6%), followed by CLABSI (21%) and CAUTI (10.8%). Acinetobacter baumannii was the dominant isolate in VAP cases, Klebsiella pneumoniae and Enterobacter spp. were predominant in CLABSI cases, and non-albicans Candida was the leading pathogen in CAUTI cases. Significant antimicrobial resistance was observed, especially among Acinetobacter and Pseudomonas species. Notably, the case fatality rate among DAI patients reached 57.5%.
Conclusion: In conclusion, our surveillance study highlights a substantial burden of DAIs in the ICU, with VAP being the most prevalent. The dominance of multidrug-resistant pathogens and the striking 57.5% fatality rate emphasize the urgent need for robust infection control, tailored stewardship programs, and continuous local epidemiological monitoring.

Abstract Background: Hospital utilization and financial performance are shaped by payer incentives, managed care participation, and capital investment decisions.
Objectives: This study aims to evaluate the extent to which variations in managed care exposure, capital expenditures, and outpatient revenue are associated with facility-level changes in service volume and net patient revenue over time.
Methods: This study uses a multi-year panel dataset of hospitals observed over 22 quarters from 2020 to 2025. Outcomes include changes in total patient days and net patient revenue, capturing shifts in service volume and realized earnings. Key explanatory variables include capital expenditures, managed care revenue exposure, teaching-related payment adjustments, and outpatient revenue. Hospital fixed-effects models are used to isolate within-hospital variation over time.
Results: Greater managed care revenue exposure and higher capital investment are associated with faster utilization growth but do not consistently produce proportional revenue gains, suggesting margin pressures under certain reimbursement structures. Teaching-related payment adjustments are associated with slower changes in both utilization and revenue.
Conclusion: These findings highlight how payer mix and investment patterns influence provider behavior and operational outcomes. Aligning reimbursement incentives and capital allocation strategies with sustainable utilization and financial stability is important for population health management and delivery system planning.

Abstract Background: Evaluating preparedness in the health sector, particularly within hospitals, is crucial for mitigating the impact of uncontrollable disasters like chemical incidents.
Objectives: This study aims to evaluate the readiness of a military hospital in Tehran to respond effectively to such emergencies.
Methods: In this study, data were collected using a standardized and validated questionnaire, specifically the Hospital Preparedness Tool for Chemical Crises, developed to assess the readiness of the selected military hospital in Tehran.
Results: According to the study's results, the overall level of preparedness at the hospital was assessed as "very good," with a total score of 119. Among the six areas examined in the selected hospital, the highest score was for the planning and guidelines area, with points (73.94%), and in the "very good" category. In contrast, the lowest score was for the risk assessment and early warning area with 7 points (70%), and in the "good" category.
Conclusion: Based on the study findings, the performance of the selected hospital in various dimensions of preparedness and response is assessed at a very favorable level. Also, the hospital has significant capabilities in improving preparedness for various events, including chemical incidents.