ABSTRACT
BACKGROUND: This narrative review aims to explore the current state and future perspective of artificial intelligence (AI) in respiratory care. The objective is to provide insights into the potential impact of AI in this field. METHODS: A comprehensive analysis of relevant literature and research studies was conducted to examine the applications of AI in respiratory care and identify areas of advancement. The analysis included studies on remote monitoring, early detection, smart ventilation systems, and collaborative decision-making. RESULTS: The obtained results highlight the transformative potential of AI in respiratory care. AI algorithms have shown promising capabilities in enabling tailored treatment plans based on patient-specific data. Remote monitoring using AI-powered devices allows for real-time feedback to health-care providers, enhancing patient care. AI algorithms have also demonstrated the ability to detect respiratory conditions at an early stage, leading to timely interventions and improved outcomes. Moreover, AI can optimize mechanical ventilation through continuous monitoring, enhancing patient comfort and reducing complications. Collaborative AI systems have the potential to augment the expertise of health-care professionals, leading to more accurate diagnoses and effective treatment strategies. CONCLUSION: By improving diagnosis, AI has the potential to revolutionize respiratory care, treatment planning, and patient monitoring. While challenges and ethical considerations remain, the transformative impact of AI in this domain cannot be overstated. By leveraging the advancements and insights from this narrative review, health-care professionals and researchers can continue to harness the power of AI to improve patient outcomes and enhance respiratory care practices. IMPROVEMENTS: Based on the findings, future research should focus on refining AI algorithms to enhance their accuracy, reliability, and interpretability. In addition, attention should be given to addressing ethical considerations, ensuring data privacy, and establishing regulatory frameworks to govern the responsible implementation of AI in respiratory care.
ABSTRACT
Background: Anesthesia providers face numerous occupational hazards, including exposure to anesthesia gases, which can lead to fatigue. These professionals face challenges such as night shifts, OR stress, limited mobility and sunlight access, high workload, inadequate rest breaks. Health-related sociodemographic variables, such as smoking, sleep patterns, and obesity. Our research aims to explore various risk factors associated with fatigue among operating theatre workers including sleep quality. Methods: A cross-sectional study was conducted on 227 of operating room healthcare professionals from five tertiary hospitals in Saudi Arabia, for a period of 6 months, between January 1, 2023 to June 1, 2023. The study used a five-point Likert scale sheet and the FSS "fatigue severity scale" to analyze and measure fatigue and sleep quality. The questionnaire included all socio-demographic variables, work conditions, and fatigue severity scale items. Results: The major findings revealed a significant correlation between fatigue severity scores and exposure to anesthesia gases. Socio-demographic variables such as smoking have showed major relevance to fatigue in the sample size, as (76.6%) of the participants that answered as regular smokers have showed result of positive correlation to fatigue and with a significant of (0.034). Out of the total sample, 76.1% were exposed to anesthesia gases once daily, showing a positive association with fatigue severity scores. Work-related factors like job experience and position also had a lower association with fatigue severity. p (0.031) Univariate logistic regression p (0.035). Conclusion: The study found that the work-related conditions like workload on Anesthesia technicians and technologists over 44 h per week and gas exposure is directly linked to fatigue severity and sleep quality so is the socio-demographic considerations. With poor sleep quality in younger staff which is documented in the study result a large-scale prospective analysis to understand the factors affecting OR staff's sleep quality and fatigue severity and what can be done to regulate working hours and break time and incorporate naps in to enhance patient safety and well-being for anesthesia providers in Saudi Arabia.
